future policy modeling - cordis · 0.4 21.05.2015 ‐ revision of chapter three and four burkhardt,...

Project Reference No.

287119

Deliverable No.

5.6

Relevant workpackage:

WP 5: Advanced Visualizations

Nature:

Prototype

Dissemination Level:

Public

Document version:

V 1.0

Future Policy Modeling

Deliverable 5.6 – Final Visualization Software

FUPOL Deliverable 5.6 v1.0 ‐ 29.05.2015 Page 1 / 68

Editor(s):

Kawa Nazemi

Contributors:

Dirk Burkhardt, Kawa Nazemi

Reviewers:

Wilhelm Retz

Document description: This document provides the final iteration of the implementationphase in FUPOL WP5: Advanced Visualization.

History

Version Date Reason Prepared / Revised by

0.1 14.04.2015 ‐ First draft of the TOC of the document D5.6 – Request for comments

Burkhardt, Nazemi

0.2 24.04.2015 ‐ General structure of the document and specification of changing sections

Burkhardt, Nazemi

0.3 13.05.2015 ‐ Revision of chapter two Burkhardt, Nazemi

0.4 21.05.2015 ‐ Revision of chapter three and four

Burkhardt, Nazemi

0.5 27.05.2015 ‐ Minor refinements on the structure and revision of the introduction and conclusion

Burkhardt, Nazemi

0.7 29.05.2015 ‐ Review Retz, Nazemi

1.0 29.05.2015 ‐ Last refinements and finalization Burkhardt, Nazemi


Table of Contents

1 Introduction ......................................................................................................................................4

2 The Visualization Scenarios ..............................................................................................................5

2.1 Scenario I: Visual Social Data Analysis ..................................................................................... 5

2.1.1 REST‐API Integration ........................................................................................................ 5

2.1.2 Advanced CorePlatform Integration ............................................................................... 7

2.1.3 API change from topics to categories .............................................................................. 7

2.1.4 Visualization from Overview to Details‐on‐Demand ....................................................... 7

2.1.5 Visual Interface Adaptation ........................................................................................... 11

2.2 Scenario II: Visualization of Statistical Data .......................................................................... 14

2.2.1 Enhanced inclusion of EuroStat ..................................................................................... 14

2.2.2 Stability advancements and Improved Failover Handling ............................................. 15

2.2.3 Open Data Sources and SDMX ...................................................................................... 16

2.2.4 Visual Interfaces and Interactions ................................................................................. 16

2.2.5 Overview to Details‐on Demand ................................................................................... 17

2.2.6 Enhanced Problem and Solution Identification through inclusion of Explain‐a‐LOD

Service ....................................................................................................................................... 18

2.3 Scenario III: Simulation and Impact Visualization ................................................................. 23

2.3.1 Simulation Model Visualization ..................................................................................... 23

2.3.2 Stability Advancements on the FUPOL Simulators and Simulator API .......................... 24

2.3.3 Visual Interfaces and Interactions ................................................................................. 25

2.3.4 User‐Interface Integration of Simulators and Advanced Visualizations........................ 26

2.4 Scenario IV: FUPOL Knowledge Database and Visualization ................................................. 27

2.4.1 API changes and Prototype Stability Advancement ...................................................... 27

2.4.2 Knowledge‐Databases ................................................................................................... 28

2.4.3 Data Querying ................................................................................................................ 28

2.4.4 Visual Adaptation .......................................................................................................... 30

3 The Evaluation System .................................................................................................................. 33

3.1 The Methodology of the Evaluation System ......................................................................... 33

3.1.1 Entering participation ID (Optional) .............................................................................. 34

3.1.2 Introduction (Optional) ................................................................................................. 34

3.1.3 Demographic questionnaire .......................................................................................... 35

3.1.4 User’s computer experience questionnaire (INCOBI) ................................................... 35

3.1.5 Visualization introduction screen (Optional) ................................................................ 36


3.1.6 Visualization evaluation – work with the prototype ..................................................... 37

3.1.7 Visualization use experience questionnaire (INTUI) ..................................................... 37

3.1.8 FUPOL questionnaire ..................................................................................................... 38

3.1.9 Final screen .................................................................................................................... 38

3.2 Technical Integration and Configuration of the Evaluation System ...................................... 39

3.2.1 Overall System refinements .......................................................................................... 39

3.2.2 Definition of Experiments .............................................................................................. 39

3.2.3 Definition of Questionnaires ......................................................................................... 40

3.2.4 Persistence of Evaluation Results .................................................................................. 41

4 Release Notes ................................................................................................................................ 43

4.1 General Interconnection to FUPOL Technologies ................................................................. 43

4.2 Visual Social Data Analysis (final) Prototype ......................................................................... 44

4.2.1 Change from SparQL to REST API .................................................................................. 44

4.2.2 Change from topics to categories ................................................................................. 44

4.2.3 SemaVis Visualization integration in the FUPOL CorePlatform ..................................... 45

4.2.4 Implemented REST API Queries..................................................................................... 45

4.3 Visualization of Statistical Data (final) Prototype .................................................................. 46

4.3.1 The EuroStat Statistical Data API ................................................................................... 47

4.3.2 The SemaVis service ...................................................................................................... 51

4.4 Visualization of Simulation Results (final) Prototype ............................................................ 55

4.4.1 The Statistic Data Simulator API .................................................................................... 56

4.4.2 Retrieval of the Hierarchy about Simulation Results .................................................... 57

4.4.3 Retrieval of concrete Simulation Results ...................................................................... 57

4.5 Technical Requirements ........................................................................................................ 58

4.5.1 Platform ......................................................................................................................... 58

4.5.2 Installation and Upgrade ............................................................................................... 58

5 Summary ....................................................................................................................................... 60

6 References ..................................................................................................................................... 61

7 List of Figures ................................................................................................................................. 65


1 Introduction This document describes the third iteration of the development in WP5 Advanced Visualizations. The

goal of this document is to provide an insight about the progress of development and possibly targeted

further goals. The development process is based on the requirements of the FUPOL project that

submitted relevancy criteria for each task. The priority focuses still primarily at the “real” interfacing

to the FUPOL core platform with its extended server‐technology and the revised ontology design. The

second priority is the third visual integration of social data. Based on the enormous numbers of

entities, the performance, adaptation, and the overview to detail view is of great interest in the third

iteration. The third aspect is the visualization of “valid” statistical data with simple visual interfaces,

such as a list of basic visualizations, by reducing the complexity by data‐transformation methods. This

aspect will be completed by the fourth aspect of visualizing simulation data, which provides additional

to the statistical data predicted (forecast) data based on planned policy changes. Even more the fifth

aspect will lay on the knowledge base visualization by the provision of semantics visualization for an

intuitive access to the huge existing Linked‐Open Data that are also relevant for policy makers.

This document has an iterative characteristic and will be enhanced with each development. Further

the upcoming technical challenges and problems will be addressed here.

The document will first reflect the general on‐going work on the scenarios, which were prioritized by

the consortium. In D.5.3 the FUPOL process and all the possible visualization technology features

(scenarios) were mentioned. In D.5.4 the user adaptation model was explained in a detailed shape,

which was requested in the last review (to avoid a copy&paste, this description is not included in this

document). In D.5.5 the two main scenarios are revised and described: the social data visualization and

the statistical data visualization. In this deliverable, we describe the work and changes on the final

software prototypes.

The general focus was stabilizing the software so that the pilot cities can use the software in their

productive environment. In the social data visualization the new implemented REST API was included,

next to an enhanced internal data handling to achieve a higher stability. On the second scenario, the

statistic data visualization prototype for EuroStat indicator data, the new API of EuroStat was included

and also here we focused on a higher system stability. Also this demonstrator version includes the

correlation analysis feature that automatically generate rules based on Linked‐Open Data to determine

reasons for differences in the political indicator data. The simulation and impact visualization

prototype as third scenario was extended by the simulation model visualization, which enables analyst

a better understanding about the model that explains the simulated result in better way. On the FUPOL

knowledge database and visualization prototype as fourth scenario some API changes were required.


2 The Visualization Scenarios D.5.2 and D.5.3 introduced the visualization scenarios, based on the outlined policy making process,

which Fraunhofer IGD, Cellent and the pilot cities defined. To this policy making process, a couple of

technology features were aligned. These technology features representing the features the pilot cities

need to push policy making on higher level with a major focus on citizens opinions.

In discussion with the pilot cities the most necessary features were selected, which define the focus of

the current development work of the project. Based on this, WP5 aims to develop prototypes for these

scenarios first, because of the necessity of cities, but even more for the dissemination and exploitation

strategy of the consortium.

It should be considered that this chapter has an iterative nature and was continued, enhanced, and

changed based on the requirements and development phases and documented in the past deliverables

D.5.3 until this current deliverable D.5.6. The changes are mentioned at the beginning of each of the

following scenarios are mentioned at the beginning.

2.1 ScenarioI:VisualSocialDataAnalysisVisualizations are an essential contribution in FUPOL in order to allow new approaches to get

understanding for the issues regarding policy making. To support stakeholders in understanding

citizens’ opinions, the social media analysis should help to identify them. Based on discussions in social

media, e.g. on Facebook or on blogs, stakeholders should get the opportunity to topics that get

important. Based on the observation of upcoming topics, stakeholders should be able to drill‐down on

concrete problems and what ideas do be mentioned there to solve it.

In general we can define two main strategies to initiate such a social media analysis. On the one hand

the analysts can observe the relevance development of topics by time. Such an observation allows to

early recognition of upcoming topics that should be analyzed in more detail by experts. In case of a

new upcoming (hot) topic, the stakeholders can click on them for further information. So they can

detect relevant information channels as well as opinion leaders.

On the other hand it can be start by a query search. Hereby, the stakeholder wants to analysis the

discussions about a specific query. As an example, this strategy can be interesting to make analysis

based on the objectives and goals of the decision makers. If decisions makers are focused on the social

aspect, they can dedicatedly search about possible challenges in that focus. But also if such a topic is

just important for specific region of a city, it is possible to check the feedbacks based on an explicit

search about it.

In comparison to the previous deliverable D.5.5, the sections 2.1.4 and 2.1.5 were applied with only

minor revisions, but these sections are important as they describe the general visual concept and

intention on how to deal with the data complexity of social media data.

2.1.1 REST‐APIIntegrationThe data for social media analysis is crawled from a couple of social media portals, such as Facebook,

blogs or twitter by the CorePlatform of WP3. Based on these contents categories (and internally topics)

are generated through the Hot Topic Sensing technology by Xerox (WP6). The result of this data


generation is stored in a database and provided over a new developed REST API via the FUPOL

CorePlatform of WP3 (see Figure 1).

The data is being accessed from with the SemaVis visualization system of WP5 over the even

mentioned REST API at the FUPOL CorePlatform. Based on this API, all data communication between

the CorePlatform and the visualizations of SemaVis is performed. In contrast to the previous SparQL

API, the REST API allows a less flexible use, so that for instance no dynamic statistical data can be

generated. On the other hand, the major benefit is the higher stability of the API and the higher

performance, since the API can use the performance of the database system in the background in much

better manner. The API is also appropriate to face future challenges, since an extension can be realized

through further REST statements.

Figure 1. General Date Processing and use‐cases within FUPOL project (taken from Deliverable 3.6

[Rumm13b]).

The inclusion of the new API in SemaVis required some adaptations on the query strategy. In the

previous implementations we followed the idea of atomic queries – very small queries that could be

fast processed by the SparQL endpoint, which are now not be required anymore. Another adaptation

issue is the limited flexibility of the API, which does not allows to generate e.g. statistical data on

demand by performing a query. All statistic data must now being provided by the REST API, therefore

intensive discussion about required data had to be done before the implementation of the new API.


2.1.2 AdvancedCorePlatformIntegrationIn the past prototypes the SemaVis visualization were separately linked into the CorePlatform. This

was result of the different used servers, because SemaVis needs own Servers for the provision of the

user adaptation capabilities. In the prototype the focused laid in a cooperate user‐interface.

Figure 2. Screenshot of the integrated SemaVis visualization in the FUPOL CorePlatform, based on a Zagreb

campaign.

The technical details about the integration in the CorePlatform are explained in section 4.2.3.

2.1.3 APIchangefromtopicstocategoriesThe internal representation of the Social Media topics was changed from topics to categories at the

end of project year three. The idea is to get an enhancement towards better human readability of

category names.

The change on the API had only minor impact on the visualization, since most of the change are

considered on the WP6 API and on CorePlaform. Together with the integration of new REST API in the

past period the change towards categories is completely solved. From the Look & Feel we aimed to

stay coherent to previous visualization versions, so that the selection of categories is being

implemented in the same style as topics were shown.

2.1.4 VisualizationfromOverviewtoDetails‐on‐DemandThe main challenge of visualizing the social data is the masses of instances in the described semantic

representation. We have elaborated two ideas of partner technologies to face this problem on the

data level, but beside a solution reducing the amount of instances per class/concept, the challenge of

visualizing a mass amount of data still remains. An adequate way of facing this challenge on the

visualization‐level is the appliance of Shneiderman’s Information Seeking Mantra. Shneiderman

proposed a three‐level seeking mantra containing the following steps: overview first, zoom and filter


then details‐on‐demand (see also Figure 3). In the context of visualizing the social information the

overview aspect plays a key role. In particular, we identify in context of social data visualization three

main views on this information‐level:

Overview on categorical level

Overview on temporal level

Figure 3. Overview‐to‐Detail approach for the visualization interaction in Social Media Data

The levels of overview visualizations are not distinct and can be combined to view on different

information aspects.

The thematic arrangement enables a visual overview definition of “categories‐of‐interest”, whereas all

are some part of information are visualized interactively. We apply in this context two main

visualization types to visualize the relevance computed by WP6 and the result of a quantitative analysis

on the user request. The different informational requirements are then visualized on the presentation

level by using the visual variables. The size of a graphical entity will provide quantitative information

whereas the relevance is visualized by their color.

2.1.4.1 Overviewvisualizationontime‐baseddataWe provide as the first categorical visualization a so called ThemeRiver, which visualizes the topics and

the weights over time. This visualization addresses both of the above mentioned information level: (1)

the categorical level, and (2) the temporal level. At this stage, the user can analyze upcoming relevant

topics as well as important topics. By selecting a topic, the user filters the data in significantly on a

special part. With visualizing the temporal overview and providing a faceting in time another

dimension of the data is investigated. We propose that the temporal view is the most beneficial way

to:

View the trend of upcoming social opinions

Interacting with and filtering semantic data for topic‐of‐relevance based on time


Here, we propose the use of a stacked graph with the using the following informational requirements on the information dimensions:

Size: quantity of topics, terms or extracted features

Color: relevance based on the computed relevance by WP6

X‐Axis: temporal spread

Figure 4. Concept for visualizing topics over time, including aspects as quantity of topics, relevancies and

temporal spread.

2.1.4.2 SocialMediaVisualizationsforZoomandFilterWe provide as the second categorical visualization an hierarchical treemap that uses the thematic

hierarchy of the ontology as one visual indicator, the relevance of the topics as another visual indicator

and the size as a third indicator for providing an overview of a topic on categorical level.

Figure 5. Simplified abstract illustration of the hierarchical treemap


Figure 5 illustrates a very simple example of the described view. The parameters are abstracted to

highest level. The hierarchy is simplified visualized as an overlapping (superimposing) and integrating

spatial spaces. The size is illustrating the quantity and the color the relevance:

In contrast to that very simple visual view, a graph‐based layout will be integrated that targets on the

same information values. Therefore the size of circle will be used as the indicator for the quantity of

information in one category, the hierarchy will be displayed as smaller integrated circles, and the color

will be used for the computed relevance. We are dismissing any semantic relationships in this view, to

not confuse the user with too many information.

Figure 6. Filtering by time through the timeline sliders in the SemaTime visualization

Next to the categorical filtering, also mechanism for a temporal filtering are provided (Figure 6). For

this purpose the timeline visualization SemaTime is expected. In regards of the performance limitation

of the SparQL‐endpoint at the beginning of the Zooming & Filtering stage, we reduce postings on a

post number lower than 50 pieces. Through an enlargement of the timeframe at the bottom of the

SemaTime, the time range can be increased and in the follow further postings will be retrieved by the

SparQL‐endpoint.

2.1.4.3 Details‐on‐Demandvisualizationongraph‐basedstructuresThe next step after the overview is a more detailed view with relational information. Therefore the

existing graph‐based visualizations will be extended to visualize the dependencies between actors and

topics, between actors themselves and between topics themselves. This step can be done after a

refinement on the overview visualization or based on a specific search that contains a comprehensible

number of entities.

We propose to use a force‐directed visual graph algorithm with quantitative analysis for this issue. In

this case the size of a circle indicates the number of entities, the color the relevance, the size of entities

the number and/or relevance of a topic or actor himself and the relations the semantic relationship

design in the FUPOL social data ontology.


Figure 7:Graph‐based detail visualization (own development)

The detailed visualizations can further provide more information by requesting more details on

demand. For example in the figure, we see one actor with a greater size than the others. With this

information we can assume that this actor is an opinion maker, because either he has many postings

or the postings are read by many people (regarding to the underlying data and goal). By clicking on this

actor the visual representation will first give more information about him and further provide detailed

information (as far as available) about the person.

In all the steps we have defined different visualization types that are appropriate to meet the

informational requirements from the social data part of view, described in D5.2 [Naze13]. One of the

main contributions in this task is that the visual change of the steps from overview to details and vice

versa is recognized and appropriate visualizations are provided in combined user interfaces.

The categorical, temporal, and in future the geographical view can be combined in various ways to

provide a sufficient view on the social data. One promising way to provide a fruitful way for visualizing

the different informational requirements of social data and statistical data respectively is the

juxtaposed orchestration of visualizations.

2.1.5 VisualInterfaceAdaptationThe adaptation algorithm is explained in detailed shape in Deliverable D.5.4 (pp. 5‐13) as well as in the proceedings of the HCI International 2014 [NRKK14]. This section describes the outcomes of the visual interface based on the adaptation approach

developed in FUPOL. Some parts of this chapter were published at the International Symposium on

Visual Computing 2013 [NRB*13]. The here described procedure incorporates the FUPOL API in order

to provide user‐centered design.

Our application integrates a set of seven visualization‐algorithms that are responsible for the

placement and arrangement of the objects on the screen. We separate the visual presentation and

spatial arrangement (Layout) of the objects, based on the research outcomes of vision perception

[Ren02], [War13], to provide a more efficient adaptability. The visualization‐algorithms can be

combined in a visualization cockpit with an enhanced brushing and linking metaphor. We further

enhance this approach by automating the selection of appropriate visualization‐algorithms based on

the search result. Therefore each visualization‐algorithm is annotated with its visualizing capabilities.


Graph‐based algorithms visualize the relationships between different and within categories, a time‐

based visualization illustrates the temporal spread of the results, a Treemap‐similar visualization

provides the ability to browse within the categories and lists with textual information provide the

content of the found results.

Figure 8: adapted social data visualization

The capability of each visualization algorithm is one indicator to recommend and automate the

selection of the most appropriate visualization algorithm. Another indicator is the users' interaction

with visualizations. We enable the users to place visualizations into the user interface or to remove

them and enhanced the interaction analysis and prediction algorithm proposed in D5.2 [Naze13] to

investigate the users' choice of combined visualizations. The user interactions on visualizations placed

on the screen and the choice of alternative visualizations or their movement from the screen are used

to derive a canonic user model. Our canonic user model, models the behavior of all users by analyzing

the interactions with system based on the KO*/19 algorithm [NSF10]. Therefore users' interactions are

transformed in a numerical, internal representation and the Steady State Vector is determined as a

relative measurement for the occurrence of interactions. [NSF10] The model involves the interaction

quantity with each data element, visualization element and the choice of visualizations to enable a

learning system that considers the behavior of the majority of users. Further it provides general usage

information of the visualizations to enable the recommendation and automatic selection of

visualization‐algorithms.

The canonic user model does not require personal information about the user because the model itself

provides a general data‐dependent ''initialization''. To overcome an over‐generalization of

visualization choice, the canonic user model is counterbalanced with an additional user grouping,

based on individual interactivity preferences and behavior. Thus, the system provides the capability to

respond to individual users. For this, we implemented an algorithm that computes the deviation of the

user interaction behavior. Therefore the user‐interaction behavior of the current user is compared

with the canonic user model with respect to the number of users based on the cosine algorithm:


This enables to estimate, if the same or a similar distanced user is interacting with the system and can

be enhanced to group the users with diverting intentions. The approach provides two different modi

for user‐oriented adaptation. First, the canonic‐user model that investigates the behavior of all users,

and second an individualized user model. The individual user model is an instantiation of the canonic

user model with certain preferences and interaction history of a certain user. If a user is interested in

getting behavior‐based visual adaptation, he is able to log‐in as individual user. The default user model

in our approach is the canonical user model. It is activated, if an individual user is not logged‐in. The

similarity measurements are based on Pearson similarity and enable a collaborative grouping of users.

The automatic selection and recommendation of the visualization‐algorithms is one adaptation

characteristic of our application. In addition the visualization layout is decoupled from the visual

representation [NSK10], [NK13]. We define visual presentation as the sum of those visual or retinal

variables, which can be perceived by human in parallel [TG80], e.g. color, shape, texture, size etc. of

edges, nodes, objects etc. [NSK10], [NK13].

Our approach uses the visual presentation for quantitative information of the underlying social data

results or for specific user preferences on content. The number of results is used as an indicator for

adapting the visual presentation. For example, the system highlights the persons with the most

postings. If the individual user model is activated by the user, the visual presentation can be used for

recommending content. Therefore, the history of her interactions is considered with a subsumption of

the hierarchy of the FUPOL Ontology schema. For example if the user is more interested in urban‐

planning topics (based on his previous interactions) and searches for a certain person, our application

presents the `categories‐of‐interest' visually highlighted. In contrary the canonical user model applies

the number of results as indicator for the visual variables. Visualizations that are not applicable for

currently analyzed data types are temporarily excluded from the set of user‐selectable visualization

types.

If the data type changes within the exploration work flow, the system automatically adapts the set of

provided visualization types. For example, the user may request all postings of a specific person on

demand. In this case the system automatically adapts the visualization for the specific results during

the interaction with a visualization transformation. Changes in provided visualizations are performed

as unobtrusively as possible in order to not confuse the user.

This is achieved by automatically suggesting the most similar visualization type (e.g. aim to apply a

graph‐based visualization when replacing another graph‐based visualization). Although the

transformation phases between two visualizations have not been considered as irritating by the test

users in the development phase, we aim to conduct a formal evaluation to measure the obtrusiveness

of a change while a user interacts with the system.


2.2 ScenarioII:VisualizationofStatisticalDataThe social data analysis allows gathering of a subjective problem identification and solution finding. To

verify these subjective social media analysis an objective data analysis is necessary based valid data.

Therefore public data (also named as open government data) is used in the second scenario for the

visualization of statistical data.

Through open data‐source, e.g. EuroStat, various indicators for certain countries and municipalities

are available and can be visualized for analysis or comprehension purposes.

In comparison to the previous deliverable D.5.5, the sections 2.2.4 and 2.2.5 were overtaken with only


intention on how to deal with the data complexity of statistic data, e.g. coming from an Open

Government Data‐Source.

2.2.1 EnhancedinclusionofEuroStatIn the last two prototypes (described in detail in D.5.3 [Naze13b] and D.5.4 [Naze14]) of the statistic

data visualization we used the default data API from EuroStat1. The major purpose was to have a solid

statistic data fundament to enable developing the statistic data support in SemaVis. The major

challenge was to deal with the masses of data, because the EuroStat API provides to each indicator the

statistics for all European geographic regions. This is in particular challenging for client visualization

systems, if indicators for cities or districts will be requested, because the result‐XML file will have a size

of up to 180MByte.

Figure 9. Data processing pipeline from EuroStat through the service to the SemaVis visualization technology

As a solution for this issue, we investigated in a solution to be able to provide the Open Government

Data visualization to the pilot cities since the analysis based on this objective data is a necessary phase

1 Default EuroStat API: http://ec.europa.eu/eurostat/data/database (last accessed: 10/05/2015)


in the policy modeling process. After investigation for alternatives, a new webservice2 was announces

from EuroStat at the end of 2013. The major challenge of using this webservice is to know the dataset

specific parameters, which are explained in metafile. The benefit of this webservice is the provision of

an API that allows filtering the data on the relevant data by a number of dimensions. Dimensions are

for instance the specification of geographical regions, the parameterization of the expected unit, the

data frequency or to mention a specific age group and so forth.

To integrate this webservice it was necessary to develop a small service which overtakes some of the

request and data transformation for the visualization. In general it is possible to do this also on the

client, but the interactivity would be limited because of response delays. Another need for this small

service is the limitation of the EuroStat webservice. At this moment only indicators from “Database by

Themes” could be requested by this, but other, e.g. “Tables by Themes” cannot be requested. For

indicators from this section the traditional form has to be performed by downloading the large result

files that could not be handled on the client (as it was done in the previous prototypes). By the use of

our new service, it is possible to provide an emulation of that what EuroStat does for indicators from

“Database by Themes”, so that in fact the user is able elaborate with our visualizations and the service

the entire list of indicators of EuroStat for the objective data analysis.

At the beginning of 2015 EuroStat has upgraded their websites and in particular their web‐services. In

consequence a number of changes on our new web‐service as well as the visualizations were required

to visualize the EuroStat data.

2.2.2 StabilityadvancementsandImprovedFailoverHandlingA challenging point that is hard to cover is the stability of the EuroStat data API. In particular at the end

of quarters the API is often down or throws errors because of the revision of the provided data. Even

more the web‐service quite their work by throwing exceptions, because internal errors. In particular

for the pilot cities this is an annoying issue, but it is also hard to solve.

In consequence of these troubles we implemented a caching routine on the web‐service that holds

already loaded data from EuroStat in memory. If another request on the same indicator is performed,

it will be checked, if (new) data can be requested from the EuroStat API or if the cached data should

be taken. It is to mentioned that because of the size it is not possible to cache all available indicator

data on the server, but it was important to focus on the relevant ones, which are primary the regional

indicators (NUTS‐3 and NUTS‐2 level) that are most often chosen for the visualizations.

This failover handling reduces the impact of error incident on the EuroStat API, in particular for the

pilot cities, but indeed it cannot avoid any negative implications since not all indicators can be cache.

Further refinements were investigated in stabilizing the communication between our web‐service and

the statistic visualizations. Therefore, the internal error handling was enhanced so that error states

could be reduce on a minimum.

2 EuroStat Rest Webservice: http://ec.europa.eu/eurostat/de/web/sdmx‐web‐services/rest‐sdmx‐2.1 (last accessed: 21/05/2015)


2.2.3 OpenDataSourcesandSDMXThe structure of open government data‐sources is similar. The indicators are categorized in a hierarchy.

To allow a drill‐down operation, it is necessary to support the exploration through the hierarchy. For

specific indicators, search functionality can help to find the indicators faster.

A major challenge is the exchange of the statistical data over distributed architectures. A couple of

rudimentary formats still exist, e.g. CSV, but they are limited in the description of meta‐information

and structural information. An advanced approach is the relatively new definition of SDMX (Statistical

Data and Metadata eXchange). SDMX is an XML‐based notation that allows next to the statistic data

block, an additional block to for meta‐information.

Many of the today’s existing open government data‐sources support the SDMX specification. The

implementation can vary. Some portals support just a very basic implementation with very simple

interfaces to request data, and some support complex request with filter options by indicator, location

and time ranges.

EuroStat, as our used Open Government Data source, provides the statistical data and the meta‐

information about them based on the SDMX specification. Because of the complexity of the SDMX data

from EuroStat, we introduced the web‐service that handles these SDMX issues and provide the results

in summarized way for the visualizations (see also the explained approach in section 2.2.1). In fact the

SemaVis statistic visualization (FUPOL SemaVis) consist of a server‐component that handles the SDMX

communication and pre‐processes the data for the final SemaVis visualization (client).

Figure 10. General architecture of SemaVis to request statistical data based on the SDMX standard for the

visualization.

2.2.4 VisualInterfacesandInteractionsThe GUI‐mockup of the visual interface for the first statistical data visualization will orient primarily on

the functionality (see Figure 11). Therefore the User‐Interface provides a search bar with the ability to

select the statistical data‐source (1). Based on the search term an indicator can be chosen directly, but

therefore the user needs an orientation or knowledge about the existing indicators. As a more intuitive

approach we provide the overview to detail metaphor, which allows the user to navigate to preferred

indicators. Therefore, open government data‐sources provide commonly also a hierarchy to find

relevant indicators. However, for all interactions the user can select his preferred and appropriate


visualization from the right list (2). It consists of semantics as well as statistics visualizations. Based on

these visualizations the user can orchestrate his best appropriate “knowledge cockpit” to perform his

tasks.

Beginning at navigation through the hierarchy, the user can choose the domain what he needs to

analyze. Thereby she uses the semantics visualization (3), which illustrates the existing indicators and

their domains. After an indicator was selected, the user gets the possibility to analyze the real statistical

data with appropriate visualizations (4).

Figure 11. The User Interface Design of the first statistical data visualization prototype, it orients strongly on

the required function for statistical analysis.

2.2.5 OverviewtoDetails‐onDemandCommonly the entire number of indicators is not known. Furthermore, if users have to work on new

statistical data‐sources, they do not have any information about the structure etc. For both cases it is

necessary to provide an intuitive exploration and analysis strategy to guide the user from the overview

to detail (Figure 12). Therefore, we use existing information about the hierarchy of indicators. Often

statistical data‐sources provide additional information to group indicators, e.g. transportation

network, urban use or information about the population.

For such information, SemaVis provides functionalities with various semantics visualization to explore

and navigate through the hierarchy and network of indicators. These visualizations are highly

interactive and allow in appropriate way to get an overview of the existing information.


Figure 12. Overview‐to‐Detail approach for the visualization interaction in Statistical Data to provide an

intuitive drill‐down strategy in SemaVis to find relevant and necessary indicators based on exploration

through the indicator network and hierarchy and stat analyzing on indicators of interest.

By the interaction through the hierarchy, the user is filtering the data too. This zooming and filtering

allows the user to select that piece of data that he is really interested in.

If the user is interested in a specific indicator, which he found after exploration, he can select it for

further analysis. At this time the required statistical data are requested from the data‐source to

visualize it and the statistical details will be displayed. After that the user can use and combine a set of

statistical visualization to identify problems and challenges based on the statistical data.

2.2.6 EnhancedProblemandSolutionIdentificationthroughinclusionofExplain‐a‐LODService

This section explains how reasons in form of correlations for statistical data could be found based on

LOD. Parts of this section appears in the proceedings of the IEEE International Conference for Internet

Technology and Secured Transactions 2014 [BNRK14*].

This section describes a new approach for visually interlinking Open Government Data with Linked‐

Open Data to generate and visualize explanations for certain indicator data. This is beneficial for

problem finding in policy making, especially if the reason finding is complicate because of the problem

complexity. Through the analysis and comprehension of statistical data against entries and properties

from Linked‐Open Data, correlation were extracted that may include possible reasons that can

enlighten indicator deviations from the normal range.

2.2.6.1 DesignforaVisualSemanticsExplanationsystemDecision makers and analysts are always interested in getting an overview about exiting and relevant

problems. However, in scope of problem understanding and solution finding the traditional use of only

Open Government Data is often limited for this purpose. Open Government Data can just indicate a

possible problem through deviations from the normal level, e.g. if the unemployment rate increased

significantly within a short time. In fact, the indicator does not explain why there is such a deviation.

In the following section we explain our integration approach that allows generating explanations for

certain deviations. This is realized by a merge of Open Government Data with Linked‐Open Data and a

graphical mapping for an easy and intuitive usage.

2.2.6.1.1 SemanticsVisualizationofLinked‐OpenData(LOD)The semantics visualizations are designed to show networks of linked information and also structures.

As data sources the common LOD data‐sources, e.g. dbpedia, can be used as well as own LOD sources


that some cities or regions do carry. Such LOD sources provide often basic information about the city,

as well as references to important topics. Such data can help to understand specific behaviors of a

region, e.g. traditional problems.

The visualization plays an important role in visualizing LOD for users since the gathering of an overview

about an unknown topic and problem depends on how easy complex dependencies can be shown to

users. LOD sources can also provide additional (meta‐) information about indicator data, e.g.

dependencies and correlation between different indicators or an indicator which built upon another.

From the visual point of view the data must be shown in an explorative manner. This can be realized

by cockpit integration, which allows users to orchestrate visualizations in a personalized form. As

visualizations a set of different graphical layout algorithm is provided, which ensures that different

aspects can be shown by the same data.

2.2.6.1.2 StatisticsVisualizationsofOpenGovernmentData(OGD)The benefit of statistic visualizations of OGD is obvious. Since statistical data are majorly used in public

authorities for decision making, this kind of data visualizations are omnipresent. Based on such data

stakeholders are able to observe if a problem may occur, e.g. if an indicator has a significant deviation

from the normal level.

For a more intuitive exploration of statistical data, it is beneficial to visualize the data in an interactive

and explorative manner. Therefore, the user‐interface needs to provide the indicator data for

exploration. This has to count for the indicator list itself and its categorical hierarchy, as well as the

provision of the concrete statistical indicator data. To achieve such an interactive and explorative user‐

interface, we also designed a statistic cockpit approach, which allows users to orchestrate

visualizations regarding the indicator hierarchy and list, as well as concrete statistical visualizations e.g.

with LineCharts and ParallelCoordinates by their personal behavior and expectation.

2.2.6.1.3 FeGeLODforExplainationstobridgeOpenGovernmentDatatoLinked‐OpenDataLinked Open and Open Government Data have their special character and usage scenarios. The idea of

linking OGD resources on LOD entities is the normal way, but the advantage for decision makers would

be low. A different approach for any kind of statistics is described by Paulheim et al. ([Paul12],

[PaFü12], [PaFü11], [JFNP12]). Based on a data table, the system generates correlation (named as

rules), which result set contains possible reasons for a certain circumstance. In the presented version

it was applied on general statistical data. For this purpose Pauhlheim et al. develop the FeGeLOD3

system, which is a program that allows generating correlation rules for a given data table with

statistical contents. As a result explanations will be generated. In the default form it runs as standalone

desktop application.

For our purposes we needed to adapt it. The major change is that we need a backend system that

generated the information for our own frontend visualization technology. Another change was

necessary to allow an enhanced configuration. In the desktop application the system takes as input

small csv data‐table were the names were automatically tried to resolve to dbpedia resources. In

perspective to the planned direct use of Open Government Data. The resolving of the geographical

names is critical, because of their different spellings in the different languages. We extended the

3 More information and software download on: http://ke.tu‐darmstadt.de/resources/fegelod (accessed: 30/05/2014)


approach by a broad editing mode, where the user gets the ability to correct the aligned entities

through a search for an alternative resources or the option to delete the data entry.

Another change was required to decrease the generation time through a caching functionality.

Especially in peak times the rule generation takes very long. We integrated a database that stores

generated results so that not always a new generation of rules is required, which leads also to a real‐

time response on requests.

A special challenge is the parameterization for the explanations’ generation. The length of the result‐

set can be controlled based on a number of parameters. For some request sometimes more than one

passage is necessary to find the optimal parameters. Unfortunately the identification of optimal

parameters requires knowledge about how the system works. Commonly stakeholders do not have

knowledge about this issue. For an easier way of use we designed an automatic optimization routine.

Based on an initial passage the parameters were adjusted. If also the second run was not optimal, a

third and last passage will run that changes the parameters in dependency of the results of passage

one and two.

To achieve an interactive and explorative final visualization we also planned to change the result form.

To allow a graphical exploration based on the generated rules, we extracted all kinds of links and

parameters in separate form so that the user can select such a link and can further explore it and can

retrieve further information. This includes also the trivial mapping of the geographical names from the

Open Government Data base to concrete resources from dbpedia. After running explanation

generations, we have an encompassing linking from OGD to LOD, which provides a significant

advantage in the followed graphical analysis and exploration phase.

2.2.6.2 ImplementationoftheVisualandInteractiveSemanticsExplanationsystemThe implementation consists of two technical components. The first is the FeGeLOD engine, which we

transformed into a web‐service so that it acts as backend and overtakes all data processing. The second

technical component is the visualization frontend that allows visualizing the semantically as well

statistical data, and ‐of course‐ even though the explanations coming from the FeGeLOD web‐service.

Both parts are web‐resources and can be accessed directly with a browser.

2.2.6.2.1 FeGeLODasWeb‐ServiceTo allow a flexible use of the FeGeLOD system through the web, a couple of changes were required.

The major change was the general deploying of the technology as a web‐service, which includes

separating the internal processing into several stages that can also be accessed separately. We named

the web‐service Explain‐a‐LOD service. Based on the fact that the separation provides a number of

interfaces that can lead to wrong processing and in consequence to wrong results, if it is used e.g. in a

wrong order, we needed to define a clear process‐driven approach. Such a process‐driven

implementation makes the API also better usable for users since it follows a logical better

understandable order of activities [26]. We summarize the final implementation into seven stages (see

also Figure 13):

(1) Over the first interface the data‐table is shown to the user and he can select which data form an

Open Government Data source should be involved for the explanation generation.


(2) After this, a first mapping to concrete dbpedia resource is performed, e.g. the country name

Germany is resolved to http://dbpedia.org/resource/Germany. The results are stored in a database.

(3) In the third step the user can check the results and if all data could be successfully mapped to a

dbpedia resource. If an element could not be resolved to a dbpedia resource, the user has the ability

to search for a fitting resource or he can delete it, so that this entity is ignored in the further proceeding

of the explanation generation.

(4) With the beginning of the fourth step the explanation generation starts. Therefore in sub‐step (5),

features to each resource are extracted from dbpedia and based on them in sub‐step (6) the

explanations are generated. Rules and explanations have in this context the same meaning.

(7) The results –the explanations‐ are shown in the last step. These explanations are also stored in the

database and can be downloaded as export file.

Figure 13. Interaction and data flow diagram to process the data on the server, beginning with the statistic‐

data input and generation of the explanations.

2.2.6.2.2 TheDecision‐MakingCockpitFor the visualization of the data we used our own web‐based visualization system. It allows

visualization of heterogeneous data types (see Figure 14). On the top the user can choose a data‐

source and can enter a query. On the right side a couple of visualizations are available that the user

can select and orchestrate on his decision‐making cockpit.

In regards of the policy making lifecycle the experts usually do observe the indicator data about their

region. These indicator data are available through Open Government Databases. In this demo we


included EuroStat. If the expert needs to clarify some issue, he can use the dbpedia. Therefore, he can

perform a search by typing a query in the top. Afterwards he can navigate through the visualized result

set.

To perform an explanation analysis by the Explain‐a‐LOD service, the user has to choose an indicator.

At the data‐table the user can mark a column and by choosing the menu‐item “Explain by LOD” in the

context, the user can start the calculation process. In a first view, the user sees a result table where

the entities from EuroStat were assigned to dbpedia resources. If an entity could not be resolved, the

user can try to find an alternative resource manually, or he can delete this entry from the table, so that

it will not be considered in the further processing. After that, the internal data and analysis is processed

and explanations will be generated. The generated simple explanations can be like this:

A country with a high value of pop has high Employment

A country with a high value of width has high Employment

A country with a high value of wikiPageOutLinkCount has high Employment

A country with a high value of gdpNominalPerCapita has high Employment

A country with a high value of gdpPppPerCapita has high Employment

A country with a high value of hdiRank has low Employment

The origin FeGeLOD supports also so called complex rules, but for them a higher number of entities is

required that for our used EuroStat data does often not work well. These complex rules providing also

complex information, e.g. only countries within a certain data range or in regards of different aspects

have a high employment.

All of the mentioned information can be composed into one user‐interface. The control of what

visualization shows what data has always the user.

Figure 14. User‐Interface Results of the used Explain‐a‐LOD service together with SemaVis

2.2.6.3 StabilizationoftheExplain‐a‐LODSystemandAPIIn the past period the focus regarding Explain‐a‐LOD laid in stabilizing the system so that it can be used

also by non‐experts. Therefore, the database structure was extended to store information only a single


time and avoiding data redundancy. At the frontend the user gets better guided through the rule

processing process and the error handling was enhanced by better algorithms that aim to handle it

most of the time automatically. In fact the user can focus on choosing the right data instead of dealing

with internal trouble shootings.

To generate the better rules, we also re‐adjusted the initial parameters. The challenge is to define

values that generate helpful and significant rules but even more a useful number of rules that can in

general lead to possible options the analyst should maybe keep in mind in finding a problem solution.

However, it is to consider that the generated rules are extracted only on the basis of mathematical

correlations and in fact it needs to validate by a domain expert.

2.3 ScenarioIII:SimulationandImpactVisualizationThis section explains how the simulators from WP4 and SemaVis visualization of WP5 are integrated

for enhanced impact visualization. Parts of this section appears in the proceedings of the International

Symposium on Visual Computing 2014 [BNRK14b*] and European Modeling & Simulation Symposium

[GABN14].

For an enhanced impact analysis the consortium and a number of pilot cities requested the wish to get

the ability to retrieve the simulation results based with SemaVis. This comes along with the review

remarks from November 2013 to use better the potentials of the project by integrating the project

technologies.



intention on how to deal with the data complexity of simulation data.

2.3.1 SimulationModelVisualizationA typical challenge in using simulators is to understand why some foresight are differently calculated

than expected. Often the reason can be found in the simulation model, which is regularly defined in

form through a set of rules. The quality of foresights depend among others on the setting of the rules,

which in fact means that also a single rule can have a high impact on the simulation results. To

understand the simulated forecast the model visualization can gain an understanding. Even more it

can be helpful if a mistake in the rule definition was made since it can better be analyzed why a certain

incident in the data has happened.

For the visualization purpose of the simulation model, the model was reduced on the relevant aspects.

In case of the Vodno Mountain simulator the focus laid on activities, resources (a concrete region e.g.

a special hiking path), user groups and activity sequences. All instances of these facets are linked, if

they are depending to each other. The visualizations (see also Figure 15) allow an intuitive gathering

of relation of user groups to activities, activities to required resources and also based on the activity

sequence at what time of a day it will be occupied.


Figure 15. The visualization of the simulation model, which can be added to the visualization cockpit next to

simulation results.

2.3.2 StabilityAdvancementsontheFUPOLSimulatorsandSimulatorAPIWP4 is responsible for the development of simulators in the project. The leading idea behind these

simulators is to support the pilot cities in making better policies based on simulated foresights strongly

based on objective data and probabilistic models. For this purpose WP4 develops in discussion with

the corresponding city simulators that calculate foresights in regards of set parameters and the defined

model of WP2. For the normal use and users the simulators already provide a user‐interface that allows

analyzing the simulation results. Only for enhanced impact analysis, in particular together with the

Open Government Data visualization, the visualizations of WP5 can support users in identifying better

options that can be tested with the simulators or an upcoming problem could be early identified and

countervailed.

The API for the data exchange between simulators and advanced visualizations in SemaVis is defined

in dynamic form, which allows on the one hand WP4 to include the advanced visualization directly in

their simulators, and on the hand WP5 could dynamically and directly access the simulation data

results from the simulators. Especially the last option is beneficial to allow a bonding of Open

Government Data visualizations and simulation data visualization for an enhanced impact analysis.


Figure 16. A sketch of the specified API between the simulator technologies of WP4 and the advanced

visualization technologies of WP5 and how the data is shared. In the normal operation simulators and

advanced visualizations have a direct data connection, but maybe later a proxy can also be used at WP3

(Image by WP4/SocSim).

In the past period also a number of feature to provide a more stable visualization was investigated.

This is realized by an enhanced internal error handling.

Figure 17. The comparative view on EuroStat data visualization and Simulation result visualization.

2.3.3 VisualInterfacesandInteractionsThe overall interface as well as the interaction is very similar to the scenario II of the visualization of

statistical data (see therefore section 2.2.4).

The simulator API provides two kinds of data that can be visualized. The first kind of data type is the

meta‐information in form of a hierarchy. Hereby all kinds of simulated results can be categorized. This

structuring is similar to the Open Government Data, where indicators are classified into categories for


easier elaboration of users. For the API we considered the same feature to allow users an intuitive data

exploration.

The second kind of data is the real statistics data about simulated topics. With the existing statistical

visualizations the user can make his analysis.

Figure 18. Screenshot of the Simulation Result Visualization in SemaVis

2.3.4 User‐InterfaceIntegrationofSimulatorsandAdvancedVisualizationsThe integration of SemaVis could be used in two different forms. The first use‐case scenario is as

standalone application (as for instance shown in Figure 18) with inclusion of e.g. Open Government

Data. The second, what is majorly intended in regards the simulation issue in FUPOL, as advanced

visualization option (depicted in Figure 19). In this second form the SemaVis visualization could be

used next to the already included simulation visualization. This enables user to see next to primary

intended analysis aspect the observation and analysis of other aspects with the more dynamic

composition ability of the SemaVis system.


Figure 19. SemaVis Visualization integration into Simulator

2.4 ScenarioIV:FUPOLKnowledgeDatabaseandVisualizationThe introduced application scenario of SemaVis provided a sufficient insight into the feasibility of our

conceptual model. The application scenario applied different aspects of our model and adapted

according to our reference model based on both canonical and individual user. The heterogeneity of

users, who search for information on Web are enormous. Almost every one searches the Web for

information. The differences between the people, who are interacting relies not only on their prior

knowledge, interests, education, visual abilities, or aptitudes, the users differ in their cultural and

demographic background too. However, the main aspect is that the application scenario of search on

Knowledge databases has commonly the most heterogeneous users. The here described software was

accessed by users all over the world. We registered access from China over Iran to the United States

of America. Although the most of the Web accesses came from Europe and overseas, the

heterogeneity of users is given and this fact affects the way of visualizing information enormously.



intention on how to deal with the data complexity of data from open knowledge bases, e.g. coming

from DBPedia or Freebase.

2.4.1 APIchangesandPrototypeStabilityAdvancementIn the past period the focused laid on considering the API change of freebase. Google has changed a

number of internal as well as general API aspects, which required and number of changes on the

request settings. Even more a few changes were made at the DBpedia connector.

During that phase also some changes were included to achieve a better system stability.


2.4.2 Knowledge‐DatabasesIn our opinion, the heterogeneity of users in this application scenario is the main reason that visual

representations of search results could not find their way to a regular usage in Web search. The

common way of searching information on the Web is still the list‐based textual representation of

search results. Although, information visualization and visual analytics experienced enormous

enhancements and developments, the techniques are still just used by special groups of users for

special tasks. Although, we do not expect that SemaVis will be established as visual search environment

that is regularly used and is part of the daily searching tasks, we think that the idea of adaptive

visualization would enable this idea and SemaVis could be the first step to a visual search for everyone.

SemaVis uses in this application scenario two slightly different data‐bases with their search capabilities

and own servers. On the one hand the DBPedia data‐base with the structured Linked‐Data and on the

other hand the Freebase data‐base a Linked‐Open‐Data base of Google. The search process is bottom‐

up by means that the user starts the search process with a query. The main difference is that the

process of data‐cleaning and term‐disambiguation is not necessary for these data‐bases. Further the

searched term is queried on both data‐bases simultaneously that leads to results from two different

data‐bases and provides a more complex visualization process. The search results from the semantic

data‐bases are commonly instances without further semantic relations or contextual information. The

returned instances have commonly a weighting‐value, how good the queried term matches to the

results. These resulting instances are our foundation to create a visual semantics and provide

contextual information. Therefore, we apply our iterative querying approach to generate a categorical

hierarchy and a contextual semantics. Figure 20 illustrates a test environment with the results and

their weighting for the term Kabul on left, the categorical hierarchy in the center, and the contextual

semantics on right. Thereby the upper visual layouts visualizes the results of Freebase and the lower

the results of the DBPedia data‐base.

Figure 20: Inclusion of semantics from Web data‐bases

2.4.3 DataQueryingThe iterative querying approach enables to gather and visualize the semantic structure of the result

set and provides an interactive picture of the searched term. This process is the foundation of

visualizing the semantic structure. In this application scenario, we enhanced our approach based on


the users’ search intentions. We determine based on the searched term and the weight‐values of the

data‐bases, if a search is focused or more exploratory. Therefore the search terms are compared to

the weighting values of the data‐base. With the assumption that if a user searches for a specific fact,

she defines more precise search terms, we implemented (and published in ACM I‐Know 2014) an

algorithm that make use of the data‐base weightings in relation to the search terms. If one result

returns based on a specific search that may contain more than one search term is weighted

significantly higher than other results, SemaVis visualizes the entire set of results but selects the result

with the highest value initially. The process of exploratory search is thereby not limited. Although, the

user searched for a very specific combination of terms, the entire set of results is visualized. The main

difference is just that SemaVis already selected already the path to the result that is significantly high

compared to the other resulted instances. This functionality can be best explained with an example:

Let us assume that the user searched for the term Obama. As non‐exploratory search engines would

prefer and illustrate result on the president of the United States in their first pages, SemaVis visualizes

all categories and semantic relations found for this term and provide an exploratory navigation. This is

due to the unspecific search. There is also a city in Japan named Obama. If SemaVis would just

visualizes terms that are related to the president of United States, the user would not find the city of

Obama easily. But vice versa, if the user searches for the term Barack Obama, it is obvious that the

user wants to get information about the president. In this case, SemaVis visualizes all results of the

query Barack Obama but selects the instance Barack Obama with based on the highest values. Figure

21 illustrates this functionality of SemaVis in this application scenario.

Figure 21: Adaptation based on search term: SemaVis adapts in this application scenario based on the

searched term. In (a) the user entered the term Obama for search, the results are giving in categories and

hierarchies on both data‐bases. In (b) the user entered the more specific search terms Barack Obama. In this

case SemaVis visualizes all results, but selects the most appropriate result based on weighing measure of

the data‐bases.

Another aspect that is relied on the data‐bases and supported by our iterative querying approach is

the initial selection of concepts after a performed search. In many cases, the DBPedia data‐base

provides a concept‐hierarchy consisting of one sub‐class. In such cases the interaction costs of the user

increases due to interacting through single concepts and getting at last stage either a separation of

concepts or further just one concept with a set of instances. Regardless of the visual layout, the

common procedure would be to select each concept and navigate through them. To reduce the

interaction cost, we implemented a routine based on our quantitative measurements that detects if a

concept has just one sub‐concept and navigate automatically through the concept hierarchy until


either there are more than one sub‐concepts and the user can choose one or there are no concepts

anymore and the user can interact with the instances directly. We kept the concept‐hierarchy to

provide the hierarchical information for the user. During users’ interaction, new data may be loaded

on demand and new concepts may complement the hierarchical structure. Figure 22 illustrates this

functionality. Thereby the search results in Figure 22:a just provided a single sub‐concept hierarchy.

SemaVis selects automatically the lowest level and visualized the related instances. In Figure 22:b the

hierarchy was selected until more than one sub‐concept appeared. The user is able to select a further

level of hierarchy or interact with the related instances.

Figure 22: Automatic selection of sub‐concepts

2.4.4 VisualAdaptationThe adaptation in this application scenario follows our conceptual model. All major aspects of the

conceptual model could be implemented in this scenario, thus real semantic data are accessed from

the two mentioned data‐bases. The application starts similar to the introduced application scenario of

digital libraries with a blank user interface as illustrated in Figure 7.2. The user interface is the same as

already described, with its several areas for login, search, visual recommendation, and visual interface.

The application scenario includes a set of eight visual layouts. These visual layouts were the most used

ones since the first version was online accessible. The first adaptive version of SemaVis for Web search

with limited functionalities was released in 2012 on the Web and is free accessible without restrictions.

Thereby the canonical user model was integrated one year later and is trained since 2013 by various

and very heterogeneous users.

SemaVis starts with a canonical user that adapts the entire visual interface based on the queried data

and the user model. The main difference is that the search is performed simultaneously on two

different data‐bases. Therefore the visual variables that indicate relevance values and guide the users’

attention differ in their color hue. This is to enable a differentiation between the results of the two

data‐bases. Beside this, each visual layout on interface is annotated with the corresponding data‐base.

The adaptation based on the canonical user model is based on the interaction behavior of the user in


relation to the data. So the results of the different data‐bases may initially be visualized with different

visual layouts. This is due to the different structure and content of the result data. Figure 23illustrates

the changed views on the slightly differing data. Thereby a search for the term Fraunhofer was

performed and navigated to the Fraunhofer Society. The similar visual layouts are due to the similar

data, whereas the DBPedia data‐base provides additional geographical information and Freebase

temporal information. The visual interface is thereby adapted based on the user model and the

underlying data. Further the visual variables that make use of color hue (Freebase: deep orange to a

light green and DBPedia: deep green to a light turquoise), saturation, size, and order. The visual layouts

are recommended on the recommendation area for each data‐base separately. Further the user

interaction history above the visual layout is visualized in the dominant color of the particular data‐

base. Figure 7.16 illustrates that with both data‐bases were interacted. This can be seen from the

different colors of the user interaction history.

Figure 23: Automatic Adaptation based on the Canonical User Model

The canonical user model is the main user model of this application scenario and therefore, beside the

data structure, the foundation of adaptation. Due to the very different data that are returned from the

data‐base, the adaptation effects are bigger and changes during the interaction with the system appear

more often. A main aspect is that during the interaction, data may be loaded from the underlying data‐

bases on demand. The changed data structure in combination with the user model effects the visual

interface immediately and enhances the interface with new visual layouts. Thereby the automatic

dismissal of placed visual layouts are only then performed, if not data for that particular visual layout

exist or the user starts a new search that returns other data with other data‐structure. The canonical

user model is in this scenario like in the other scenarios too, the average usage behavior of all users,

who interacted with the system. Those users, who are logged‐in as individual users are considered too.

That means that regardless, if a user is interacting with the system logged‐in as individual or not,

SemaVis considers his interaction in the canonical user model and changes the entire behavior based

on this user model.


In contrast to that, if a user is logged‐in as individual and has not yet an individual user model or his

user model does not contain enough data to determine his preferences and behavior, SemaVis

investigates for that user the canonical user model and trains simultaneously the individual one. In this

case the introduced approach of measuring deviations and user similarities are continuously applied.

Thereby the individual preferences of the user are measured and if his individual user model contains

enough information for an individualized adaptation, the canonical user model is not investigated

anymore for adaptation (but still trained further) and the individual user model is applied for

adaptation. To illustrate how individual user may change in their behavior, we illustrated in Figure 7.17

the initial results of the term Albert Einstein of two differing users.

Figure 24: Visual adaptation for differing user


3 The Evaluation System The evaluation of visualizations is a challenging task. In fact it is necessary to have qualitative feedback

from the (pilot) users to have the general ability to improve the visualizations. Therefore, the

evaluation environment needs to be productive for this purpose. Often the visualizations are being

tested isolated on their own, so that the acquired feedback presents in a very basic manner and its

analysis for further improvements proves to be quite difficult.

Therefore, Fraunhofer developed an evaluation system, which allows a qualitative evaluation by

considering a questionnaire and a practical visualization usage based on real task scenarios. The

evaluation system is provided for free to the pilot cities and Fraunhofer is willing to support the pilot

users and WP7 in the evaluation preparation.



intention on how to deal with the data complexity of data from open knowledge bases, e.g. coming

from DBPedia or Freebase.

This chapter is overtaken with only minor revision from the previous deliverable D.5.5, but these

sections are important to explain the developed and prepared evaluation methodology and system.

3.1 TheMethodologyoftheEvaluationSystemThe evaluation system considers eight steps (Figure 25), which cover all important aspects that can be

useful to analyze the usability etc. Because of the generic structure of the software, changes in the

number of steps are by all means possible. The presented steps orient themselves on the

recommendations in scientific literature. We explain each step in a more detailed manner in the

following (sections). The user will be guided automatically through the entire process, to ensure that

all steps will be handled in the correct order and to decrease the barrier and effort of the pilot users.

Figure 25. General workflow of the evaluation system for testing the visualizations


3.1.1 EnteringparticipationID(Optional)The first step and page of the evaluation system is optionally and asks for a participant‐code. This

should ensure that only invited people can participate in the evaluation. The other aspect is that based

on the participant‐id also a concrete user group can be defined, e.g. the user group of facilitators have

to use the ID range 100‐150. It is not intended to align the participant code to a concrete person!

Therefore, it is essential that the codes are provided in an anonymous way.

Figure 26. Initial screen where the user gets asked to enter the participant‐code.

It is also possible to enable the setting of a random participant ID, which makes it easier to invite people

for an evaluation, e.g. by a mailing‐list. Thereby the user skips this phase starts directly with the

following questionnaire.

3.1.2 Introduction(Optional)The introduction is just a short information overview for the user. Here, he gets informed what is

intended with the evaluation and what phases he will handle. In this phase it is also necessary to inform

him in which steps the time will be measured.

It is also recommended to inform the user what data will be stored/measured and in what form they

will be analyzed afterwards (e.g. in an anonymous manner). Especially in some of the questionnaires,

the user is asked for very personal information, which the user might only give, if he can trust that his

data cannot be linked to him directly.

In fact, this introduction is mandatory to inform the user. Under concrete conditions, especially if the

evaluation is guided/supported by a moderator, this introduction is not necessary, because it will be

done orally by the moderator.


Figure 27. The introduction screen informs the user about the general procedure and what and witch data

will be used and how it will be processed afterwards.

3.1.3 DemographicquestionnaireThe first questionnaire asks for demographic information about the user, such as gender and age. This

allows for a later distinguishing between age groups etc., if e.g. usability gaps seem to only exist for a

certain group.

Figure 28. Demographic questionnaire where the user has to answer some questions about himself, like age

and gender.

3.1.4 User’scomputerexperiencequestionnaire(INCOBI)The use with modern ICT can be challenging especially for computer beginners. To allow an

enhancement analysis based on the user’s skills, this questionnaire asks more precisely about the

computer experiences of the user. A common format is to answer within a range between strongly

agree ↔ disagree.


Figure 29. Questionnaire about the computer experience of the user

3.1.5 Visualizationintroductionscreen(Optional)This screen should give an overview about the visualization system, so that the user gets a general

understanding how the visualizations and perhaps the evaluation system have to be used and where

which functions are placed on the screen. It also informs the user about the parts around the

visualizations, such as the question block, and where the user can find the current task as well as the

possible answers.

Figure 30. The visualization introduction screen introduces into the general visualization system and where

which functions are placed on the screen.

After the user clicks on the right button, the evaluation will start. It is essential to know that after

pressing the button the time will be measured as well. So, the user should start right away to avoid

faulty results.

This introduction screen is also optional. Under special circumstances, e.g. if moderator is

guiding/supporting the evaluation, this screen can be skipped. Moreover this introduction can be

skipped, if the user got time to deal with software before this evaluation.


3.1.6 Visualizationevaluation–workwiththeprototypeStep six is the most important step. Here, the user is practically working with the prototype. At the top

of the screen the user get the question/task that he should answer by using the visualizations. The

questions should be defined in collaboration of WP5 and WP7 to consider real questions of pilot users

and what the FUPOL technologies are aiming to address.

The typical manner of the procedure is that the user gets a question and approx. four possible answers

(the answers should be part/contained of the visualized data). The user can select an answer, or if he

will not be able to do identify an answer, he can also select nothing, to indicate that he was not able

to solve the question with the visualizations. It is necessary to remind the user that they should only

answer, if they really expect the answer, all guessed answers would decrease the evaluation quality.

In the background, the system logs, next to each interaction (e.g. mouse clicks), also the needed time

to solve the tasks. The meta‐information helps to identify usability gaps and it is possible to indicate

for which tasks the visualization needs to be optimized or improved. Furthermore, it can be useful to

compare the subjective mentioned experience in using the visualization with the objective measured

information, such as the ration in answering the questions correctly and the needed time to solve

them.

Figure 31. Overview of the practical evaluation step is shown. On the top the question is shown with some

possible answers the user should find with the visualizations. Underneath the question bar, the real

prototype is integrated and should be used to find the correct answer in the visualized data.

In the current version only one prototype is considered, but in general it can also be extended by

multiple prototypes and followed by an individual questionnaire. The fact that the overall procedure

would need more time, it should not be combined with too many prototypes, because the user loses

concentration the longer the evaluation will take and this will have a significant negative impact on the

evaluation results.

3.1.7 Visualizationuseexperiencequestionnaire(INTUI)After the user performed the practical evaluation, he has to answer the questionnaire about his use

experience. Here, the user gives his personal (subjective) feedback regarding the visualization. The

questions are in style of strongly agree ↔ disagree.


Figure 32. The use experience questionnaire is shown after the practical evaluation.

The questionnaire is based on the INTUI questionnaire 4 [UlDi10], and is so far a standardized

questionnaire to allow also a comprehension of the achieved results. At this stage also further

questions concerning FUPOL are possible, as well as a free‐text area to provide pilot users the ability

to mention improvement ideas etc.

3.1.8 FUPOLquestionnaireAt this stage also further questions concerning FUPOL are considered, as well as a free‐text area to

provide pilot users the ability to mention improvement ideas etc.

Figure 33. The FUPOL questionnaire covers questions about the general use and scope in the project

3.1.9 FinalscreenThe final screen informs the user that he successfully performed the evaluation. It can also give some

further information where and when the results of the evaluation will be available.

4 The INTUI questionnaire is available on: http://intuitiveinteraction.net (last accessed: 21/09/2014)


Figure 34. Final screen to inform the user that evaluation was successfully performed.

3.2 TechnicalIntegrationandConfigurationoftheEvaluationSystemIn our new version we enhanced the evaluation system in its overall design. It is now – expect the

practical visualization use ‐ completely written in HTML and JavaScript

3.2.1 OverallSystemrefinementsSince the first presentation at the review in November 2013 and the explanation in Deliverable D.5.4

[Naze14], a number of refinements were integrated to be more effective for the FUPOL scenarios.

First of all, the evaluation system is now completely developed in HTML and JavaScript. In the previous

version we included some modules that were implemented in Adobe Flex, but which made it in some

parts more difficult to change some parts. Now the evaluation solution is coherently developed in

HTML and JavaScript, which enables also further improvements and extensions.

Another advancement is the now included ability to configure the entire evaluation scenario. In the

current version it is possible to define the questionnaires as well as the entire procedure only based

on configurations (see therefore also the following two sections). This allows on the one hand creating

new questionnaires (simple questionnaires as well as questioners in combination with software usage)

and general experiments on the other hand, as they are required to use it for different FUPOL

technologies or for evaluation in the different pilot cities.

Another major advancement is the improved data export of the evaluation results. In the previous

version we provided the results in JSON format, which was useful if analysis tools were been used,

which allow to process this data. Especially for the analysis of the results in WP7 and therefore in the

pilot cities, this was not that usable. We changed the export to simple CSV files, which will be generated

for each questionnaire. This allows a very effective and easy to handle result analysis with Microsoft

Excel.

3.2.2 DefinitionofExperimentsEach experiment can now be configured in an XML‐based configuration. This will be done by the

leaders of WP5 and WP7. Here it can be defined, what questionnaires and also what visualization will

be considered in the evaluation.


<?xml version="1.0" encoding="utf‐8"?> <Experiment> <General> <Name>FUPOL‐Evaluation V 1.0</Name> <Id>FUPOL_Zagreb</Id> <ResultPath>./Results</ResultPath> <RandomizationMethod>None</RandomizationMethod> </General> <Conditions> <Condition title="Condition A"> <Unit>demographic_fupol_en</Unit> <Unit>incobi_en</Unit> <Unit>fupolEvalZagreb</Unit> <Unit>intui_en</Unit> <Unit>fupol_en</Unit> </Condition> </Conditions> </Experiment>

Figure 35. Example configuration of an experiment and the used questionnaire in the XML‐based

configuration

3.2.3 DefinitionofQuestionnairesThrough an XML configuration file all questionnaires can be defined. This allows creating new and

editing existing questionnaires pretty easy.

<?xml version="1.0" encoding="utf‐8"?> <questionnaire>

<subhead> The Visualization was... </subhead> <likert type="likert" scale="7" from="the worst" to="the best" name="fupol"> <item>This component entirely covers all the needs of users while visualizing social media data from Fupol campaign.</item> <item>This component entirely covers all the needs of users while visualizing statistic data important in policy creation process</item> <item>This component entirely covers all the needs of users while visualizing results of simulation in Fupol campaign</item> <item>Visualization has a good interface, easy to use by users</item> <item>Visualization results are clearly presented and could be easily analysed</item> <item>It is possible easy to change the type of visualization and choose a different type of graphical presentation of the selected data, the most suitable for me</item> <item>This component is efficient, provides a fast operation, without long waiting the response of the system.</item> <item>The results obtained using the Visualization component are extremely useful in the process of selecting areas for creating urban policies (agenda setting)</item> <item>The results obtained using the Visualization component are extremely useful in the phases of analysis and creating the urban policies</item> <item>The results obtained using the Visualization component are extremely useful in the process of implementing and monitoring the urban policies</item> <item>In general I am satisfied with the Visualization component and I would recommend it to the other cities</item> </likert> <subhead> Remarks/Explanations: </subhead> <bigtext name="fupol_explaination"></bigtext>

</questionnaire>



configuration

The practical evaluation is also defined in such a questionnaire configuration. Its major difference is

the mentioned link to the demo website. In our example (Figure 37) we used the link to the SemaVis

demo that opens the data for campaign 216800.

<?xml version="1.0" encoding="utf‐8"?> <questionnaire> <taskunit timer="30" randomization="none" name="EvalA"> <task> <question id="1">Which topic had the most postings in May 2014</question> <answers> <answer>Topic 1</answer> <answer>Topic 2</answer> <answer>Topic 3</answer> <answer>Topic 4</answer> </answers> </task> </taskunit> <iframe width="100%" height="500px">

http://fupol.semavis.net/demos/integration/?campaign=216800 </iframe> </questionnaire>


configuration

3.2.4 PersistenceofEvaluationResultsIn our last evaluation system prototype, presented in Deliverable D5.4, the result data were stored in

three files. The first file contained all questionnaire answers. The results were stored in a JSON format.

The second file contained the answers which were chosen during the practical use of the visualization

system. Next to the pure answers, also meta‐information were stored, e.g. when the user clicked on

an answer, how long he needed to choose an answer and if the answer was correct. The results were

stored in a CSV format. The third file is designed to store technical details, e.g. what has the user clicked

when. This should help to understand how users are using the visualizations and when they could not

answer a question correctly, why.

In our new versioned we refined the storage of the questionnaire answers, because it could be

identified that third parties have problems in analyzing the results in JSON form. Now, the results are

stored in separate files, based on the questionnaires, and for each questionnaire the answers are

stored in a simple CSV format. In fact that each user’s answer is stored in a separate row, the analysis

becomes pretty easy also for non‐technicians.

Figure 38. Example CSV result (in Excel) for a conventional questionnaire

experiment sex age job app_usage0 app_usage1 app_usage2 internet_purposes0 internet_purposes1 internet_purposes2 file userId send time Condition

fupol‐test 0 99 99 99 99 2 99 99 99 demographic_fupol_en 21‐08‐2014‐16‐07 1 1408630039 Condition A

fupol‐test 1 43 Facilitator 99 1 2 99 1 2 demographic_fupol_en 22‐08‐2014‐15‐40 1 1408714960 Condition A

fupol‐test 1 43 Facilitator 99 1 2 99 1 2 demographic_fupol_en 25‐08‐2014‐10‐50 1 1408956693 Condition A

fupol‐test 0 30 Developer 99 1 2 99 99 99 demographic_fupol_en 26‐08‐2014‐12‐05 1 1409047562 Condition A

fupol‐test 1 43 F 99 99 99 99 99 99 demographic_fupol_en 26‐08‐2014‐14‐07 1 1409054848 Condition A


Figure 39. Example CSV result (in Excel) about a practical evaluation questionnaire, where the user has to

find answers in the visualization software

experiment q1‐ans q2‐ans q3‐ans q1‐res q2‐res q3‐res q1‐time q2‐time q3‐time q8‐time file userId send time Condition

fupol‐test a3 a3 a3 false true false 50653 3183 1763 2387 fupolEvalZagreb1 21‐08‐2014‐16‐07‐06_67007 1 1408630113 Condition A

fupol‐test a1 a3 a2 true true true 59683 159353 25029 626472 fupolEvalZagreb1 25‐08‐2014‐10‐50‐34_60903 1 1408958131 Condition A

fupol‐test a1 a99 a99 true false false 12812 5502 962 1356 fupolEvalZagreb1 26‐08‐2014‐12‐05‐41_43127 1 1409047598 Condition A

fupol‐test a1 a3 a2 true true true 37637 165943 21366 39856 fupolEvalZagreb1 26‐08‐2014‐14‐07‐19_5889 1 1409055810 Condition A


4 Release Notes This section provides a summary of information regarding the mentioned prototypes.

In the previous chapter the general idea of the most required scenarios (based on the FUPOL feature

list) were introduced and explained on an abstract level. To realize the described ideas, a couple of

changes were required to use SemaVis for these purposes as well.

In this chapter we describe the development activities and also the technical aspects which allow the

social media analysis and the statistical visualization. Therefore, in the first parts the general actions

and changes were explained, followed by the specific activities for both scenarios.

4.1 GeneralInterconnectiontoFUPOLTechnologiesAt the initial project phase it was planned that almost all kinds of data will be handled and provided by

the FUPOL CorePlatform (indicated by transparent marked boxes). During project runtime this

approach seemed not feasible since specific issues and requirements have to be taken into account for

each single kind of data and data source. In consequence only the Social Media Data are organized and

provided via the FUPOL CorePlatform that consists of the default social media data (posts, user

interactions etc.) and some basic statistic data aggregations, e.g. about the topic development over

time.

Figure 40. The defined APIs of the FUPOL Core Platform the exchange data with clients.

The social media data is provided over a REST API. The post itself are stored in structured relational

database. Next to the posts and related basic information such as authors and categories, also some

basic statistical data are provided that are also stored in the database and are provided via the REST

API.

In SemaVis an internal organization for each of the specified data formats will be available, regardless

of the connected data source. Also a couple of visualizations will be developed to support the display

of that data. To support most of the features of the data that are also relevant for the users’ tasks, an

advanced concept for data mapping needs to be developed (Figure 41). For each kind of data we define

explicit data mapping instructions, which ensure that for each kind of data the most useful contained

information are considered in the visualization. The mapping is an internal processing step that is

hidden to users. Thereby, the data will be processed and relevant information for visualization purpose


will be selected. In regards to the visualization functionalities and the mapping instructions definitions

the visualizations will be parameterized to show the data. The visualization result is a specifically

optimized view on the data.

Figure 41. The alignment and mapping of the various data for the visualizations within SemaVis.

Based on the used model and the data mapping also the interactivity is organized. Each interaction can

have effects on the model, for instance to make a request to reload data. This strong cooperation of

model and data mapping allows a high interactivity and a fast navigation through the data.

4.2 VisualSocialDataAnalysis(final)PrototypeThe previous section gave an overview about the general connection to the FUPOL Core Platform, as

well as the general mapping to the final visualizations. In this chapter, the technical issues for each

prototype scenario are explained in a clear manner.

4.2.1 ChangefromSparQLtoRESTAPIAt the beginning of the work on the Social Media Scenario the major data exchange API was a SparQL

interface. Because of troubles with the performance and stability the API was changed in last period

to a REST API, which now directly provides data that is stored in a database. The advantage is a higher

performance and a higher availability of the API, but on the other hand the flexibility is reduced too.

In SparQL it was possible to send own created queries that can e.g. calculate some statistical values.

This is not possible via a REST API, since the requests are hard defined by each given REST statement.

Earlier introduced feature, as for instance the atomic SparQL‐queries, which aimed on decreasing the

server load, are not required anymore and therewith not implemented anymore.

In the following, we introduce some example queries we integrated into SemaVis. Based on these

example queries we are able to show the information on demand.

4.2.2 ChangefromtopicstocategoriesAt the end of project year three the originally introduced topics were changed to categories. The

reason is a change on the Hot Topic Sensing technology of WP6. In fact, also the internal representation

at the FUPOL CorePlatform of WP3 and even more in the visualizations of WP5 needed to be change.

The most significant change was from topics and topicLabels to categories. These change was needed

because of enhancements of the HotTopic Sensing technology of WP6, which furthermore requires


changes on the API of WP3. In fact, the SparQL queries of the visualizations needed to be adapted too.

The refined queries were listed below. After the complete integration of the REST API, the change to

the categories was completely achieved.

4.2.3 SemaVisVisualizationintegrationintheFUPOLCorePlatformIn the current period the final integration, in particular regarding the coherent look and feel, was in

scope. In the past the SemaVis visualization were shown in separate browser window, which often

looked not final integrated. In the actual form it is completely integrated in the FUPOL CorePlatform

portal (see also section 2.1.2).

A challenging aspect was the two different web‐service have to be accessed. The one kind of servers

are the adaptation servers that analyze the user’s interaction a making user‐interface adaptations. The

other web‐service are those that retrieve the data from the databases. To be complient with the

required security standards, the embedding cannot be done directly reference to SemaVis. It is

required use a proxy on the FUPOL servers. In fact the final inclusion looks like this.:

<iframe id="id98" width="100%" height="800px" src=" https://fupol‐6.cellent.at/ext/semavis/?campaign=campaignID"> </iframe>

To provide the SemaVis under the mentioned alias, the proxy must be configured like this:

ProxyPass /ext/semavis http://projects.semavis.net/fupol/demos/integration ProxyPassReverse /ext/semavis http://projects.semavis.net/fupol/demos/integration

As a result SemaVis loaded from the SemaVis servers but over the proxy which allows using SSL too

and avoids warnings in the browsers.

4.2.4 ImplementedRESTAPIQueriesThese queries are used for the visualization of the social media data coming from Social Media REST

API of the WP3’s CorePlatform.

4.2.4.1 Topicsvisualizationovertime

4.2.4.1.1 Requestinggeneralcampaigninformation

https://fupol-6.cellent.at/fupol-services/rest/public/Campaign/216800

4.2.4.1.2 Requestingallavailabletopicstoacertaincampaign

https://fupol-6.cellent.at/fupol-services/rest/public/Category/findByCriteria

{"page":0,"size":1000,"criteria":{"campaignId":216800}}


4.2.4.1.3 Queryingthetemporaldevelopmentofaconcretetopic/category(byid)

https://fupol-6.cellent.at/fupol-services/rest/public/

CategoryAggregation/findByCriteria

{"page":0,"size":1000,"criteria":{"campaignId":216800,"categoryId":16327469,"publ

ishDateFrom":"2013-07-16T00:00:00+0000"}}

4.2.4.2 DirectEntitySearchesThe search for posts is an initial step to start the work with the visualizations. In following we also

present request that are requested during the interaction phase of the user. The queries consider now

the change in the endpoint from topics to categories.

4.2.4.2.1 Searchingforposthavingcontent‘Rukomet’

https://fupol-6.cellent.at/fupol-services/rest/public/SmPost/

findByCriteria

{"page":0,"size":50,"sort":"publishTime","sortDir":"DESC","criteria":{"co

ntentWords":["Rukomet"],"campaignId":216800,"publishTimeFrom":"2013-07-

16T00:00:00+0000","resultFields":["id","smPostType","resourceUri","publis

hTime","publishUserId","publishUserName","content","categoryIds"]}}

4.2.4.3 Explorative/DynamicRequest

4.2.4.3.1 Requestallinformationregardingacertaintopic

https://fupol-6.cellent.at/fupol-services/rest/public/Category/5055569

4.2.4.3.2 Requestallpoststoacertaintopic/category

https://fupol-6.cellent.at/fupol-services/rest/public/SmPost/

findByCriteria

{"page":0,"size":50,"sort":"publishTime","sortDir":"DESC","criteria":{"ca

tegoryId":5055569,"campaignId":216800,"publishTimeFrom":"2013-07-

16T00:00:00+0000","publishTimeTo":"2015-06-

01T00:00:00+0000","resultFields":["id","smPostType","resourceUri","publis

hTime","publishUserId","publishUserName","content","categoryIds"]}}

4.3 VisualizationofStatisticalData(final)PrototypeIn contrast to the visualization of social media data, where the focus lays on the relations, for instance

on main influencing authors of a region, the focus of statistical data lays on the visualization of

statistical objective data. These statistical data are represented in a spreadsheet form. The

requirements can vary.

However, in focus of policy modeling we have to consider the aspects of open government data too.

In concrete that does mean we have data‐sources with indicators and structures, in which these

indicators are classified. Therefore, we have to consider the real statistical data next to structural data

too.

4.3.1 TheEuroStatStatisticalDataAPIWe use the Eurostat library as data fundament for Open Government Data, as one of the biggest open

government data‐source for (real and up to date) statistical data. It provides indicators for high number

of indicators for all European countries. Also a couple of municipality data are currently considered.

EuroStat provides a hierarchy where the indicators are categorized. The hierarchy file is among others

available in XML:

http://epp.eurostat.ec.europa.eu/NavTree_prod/everybody/BulkDownloadListing?file=table

_of_contents.xml

This hierarchy allows to find an expected indicator based on its domain, for instance if the user is

interested in the GDP, he will find them in category financial data. The hierarchy file consists of

categories and indicators, so called datasets.

For each dataset (or indicator) a number of meta‐information is available, e.g. responsible

administrator, where these data were imposed etc. More important are the specification of so called

dimensions. For each dataset a certain number of dimensions do exist, which can also be seen as a

kind of filter criteria. Common dimension are data frequency on e.g. annual, quarterly or daily level.

Another dimension can be the geographical location, for instance the country Germany. But even more

there are further specific dimensions as for instance the speciation of genders or age groups, which

allow filtering the indicator date on a very specific group. The available dimensions can be accessed by

the EuroStat Webservice:

http://ec.europa.eu/eurostat/SDMX/diss‐web/rest/datastructure/ESTAT/DSD_datasetname

To request the meta‐information to the population indicator –as an example‐ the following URL has to

be used:

http://ec.europa.eu/eurostat/SDMX/diss‐web/rest/datastructure/ESTAT/DSD_demo_pjan

The result will be a description like the following in SDMX/XML notation:

<?xml version="1.0" encoding="UTF-8" ?> -  - <message:Structurexmlns:message="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/message" xmlns:structure="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/structure" xmlns:common="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/common" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/message ../../schemas/SDMXMessage.xsd"> - <message:Header> <message:ID>DEMOGRAPHY</message:ID> <message:Test>false</message:Test>


<message:Prepared>2010-11-13T08:00:33+08:00</message:Prepared> <message:Sender id="ESTAT" /> </message:Header>

- <message:Structures> - <structure:Codelists> + <structure:Codelist id="CL_DECIMALS" agencyID="SDMX" version="1.0"isExternalReference="true" structureURL="../common/common.xml"> + <structure:Codelist id="CL_FREQ" agencyID="SDMX" version="1.0"isExternalReference="true" structureURL="../common/common.xml"> + <structure:Codelist id="CL_CONF_STATUS" agencyID="SDMX" version="1.0"isExternalReference="true" structureURL="../common/common.xml"> + <structure:Codelist id="CL_OBS_STATUS" agencyID="SDMX" version="1.0"isExternalReference="true" structureURL="../common/common.xml"> + <structure:Codelist id="CL_UNIT_MULT" agencyID="SDMX" version="1.0"isExternalReference="true" structureURL="../common/common.xml"> - <structure:Codelist id="CL_UNIT" agencyID="ESTAT" version="1.0"isPartial="true"> <common:Name xml:lang="en">Unit code list</common:Name> - <structure:Code id="PERS"> <common:Name xml:lang="en">Persons</common:Name> </structure:Code> - <structure:Code id="CPW"> <common:Name xml:lang="en">Children per woman (fertility rate)</common:Name> </structure:Code> - <structure:Code id="YRS"> <common:Name xml:lang="en">Years</common:Name> </structure:Code> </structure:Codelist> - <structure:Codelist id="CL_SEX" agencyID="ESTAT" version="1.0"> <common:Name xml:lang="en">Sex codelist</common:Name> - <structure:Code id="F"> <common:Name xml:lang="en">Female</common:Name> </structure:Code> - <structure:Code id="M"> <common:Name xml:lang="en">Male</common:Name> </structure:Code> - <structure:Code id="T"> <common:Name xml:lang="en">Total</common:Name> </structure:Code> </structure:Codelist> + <structure:Codelist id="CL_COUNTRY" agencyID="ESTAT" version="1.0"isPartial="true"> </structure:Codelists> - <structure:Concepts> + <structure:ConceptScheme id="CROSS_DOMAIN_CONCEPTS" agencyID="SDMX"version="1.0" isExternalReference="true"structureURL="../common/common.xml"> - <structure:ConceptScheme id="DEMO_CONCEPTS" agencyID="ESTAT" version="1.0"><common:Name xml:lang="en">Demography domain concept scheme</common:Name> - <structure:Concept id="COUNTRY"> <common:Name xml:lang="en">Reporting Country</common:Name> </structure:Concept> - <structure:Concept id="SEX"> <common:Name xml:lang="en">Sex</common:Name> </structure:Concept> - <structure:Concept id="DEMO"> <common:Name xml:lang="en">Demography</common:Name> </structure:Concept> </structure:ConceptScheme> + <structure:ConceptScheme id="DEMO_MEASURES" agencyID="ESTAT" version="1.0"></structure:Concepts> - <structure:DataStructures> - <structure:DataStructure id="DEMOGRAPHY" agencyID="ESTAT" version="1.0"> <common:Name xml:lang="en">DEMOGRAPHY Data Structure</common:Name> + <structure:DataStructureComponents> - <structure:ConceptIdentity> <Ref agencyID="ESTAT" maintainableParentID="DEMO_CONCEPTS"maintainableParentVersion="1.0" id="DEMO" /> </structure:ConceptIdentity>


- <structure:LocalRepresentation> - <structure:Enumeration> <Ref agencyID="ESTAT" id="DEMO_MEASURES" version="1.0" class="ConceptScheme" /> </structure:Enumeration> </structure:LocalRepresentation> </structure:MeasureDimension> </structure:DataStructure> </structure:DataStructures>

</message:Structures> </message:Structure>

Figure 42. Example SDMX‐ML responses of meta‐information about a data request

Based on this meta‐information it is possible to extract the available dimension and use it for a precise

request for the really expected data (for the interested geo locations, the preferred frequency, unit

etc.). For this purpose EuroStat provides its new REST‐webservice on:

http://ec.europa.eu/eurostat/SDMX/diss‐web/rest/data/

datasetname/dimension1.dimension2.dimensions3.[…]/?startperiod=1980&endPeriod=2014

If the EuroStat webservice is parameterized correctly its response are the requested statistical data

(also in SDMX/XML notation) like this:

<?xml version="1.0" encoding="UTF-8" standalone="yes" ?> - <message:StructureSpecificDataxmlns:demo="urn:sdmx:org.sdmx.infomodel.datastructure.DataStructure=ESTAT:DEMOGRAPHY(1.0):ObsLevelDim:DEMO:explicit" xmlns:message="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/message" xmlns:data="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/data/structurespecific" xmlns:common="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/common"xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/message ../../schemas/SDMXMessage.xsd urn:sdmx:org.sdmx.infomodel.datastructure.DataStructure=ESTAT:DEMOGRAPHY(1.0):ObsLevelDim:DEMO:explicit demography_xs_ex.xsd"> - <message:Header> <message:ID>DEMO_XS_EX</message:ID> <message:Test>true</message:Test> <message:Prepared>2011-11-25T00:21:49-05:00</message:Prepared> <message:Sender id="ESTSAT" /> - <message:Structure structureID="STR1" dimensionAtObservation="DEMO"namespace="urn:sdmx:org.sdmx.infomodel.datastructure.DataStructure=ESTAT:DEMOGRAPHY(1.0):ObsLevelDim:DEMO:explicit" explicitMeasures="true"> - <common:Structure> <Ref agencyID="ESTAT" id="DEMOGRAPHY" version="1.0" /> </common:Structure> </message:Structure> </message:Header> - <message:DataSet data:structureRef="STR1" xsi:type="demo:DataSetType"data:dataScope="DataStructure"> + <Series FREQ="A" TIME_PERIOD="2007" SEX="T" COUNTRY="BE"> + <Series FREQ="A" TIME_PERIOD="2008" SEX="T" COUNTRY="BE"> - <Series FREQ="A" TIME_PERIOD="2009" SEX="T" COUNTRY="BE"> <Obs xsi:type="demo:TFRNSI" OBS_VALUE="1.83" OBS_STATUS="P" UNIT_MEASURE="CPW"UNIT_MULT="0" /> <Obs xsi:type="demo:LEXPNSIT" OBS_VALUE="80.6" OBS_STATUS="P" UNIT_MEASURE="YRS"UNIT_MULT="0" /> <Obs xsi:type="demo:LBIRTHST" OBS_VALUE="126000" OBS_STATUS="P"UNIT_MEASURE="PERS" UNIT_MULT="0" /> <Obs xsi:type="demo:DEATHST" OBS_VALUE="104000" OBS_STATUS="P" UNIT_MEASURE="PERS"UNIT_MULT="0" /> </Series> - <Series FREQ="A" TIME_PERIOD="2007" SEX="M" COUNTRY="BE">


<Obs xsi:type="demo:LEXPNSIT" OBS_VALUE="77.3" OBS_STATUS="A" UNIT_MEASURE="YRS"UNIT_MULT="0" /> <Obs xsi:type="demo:LBIRTHST" OBS_VALUE="63481" OBS_STATUS="A" UNIT_MEASURE="PERS"UNIT_MULT="0" /> <Obs xsi:type="demo:DEATHST" OBS_VALUE="49804" OBS_STATUS="A" UNIT_MEASURE="PERS"UNIT_MULT="0" /> </Series> - <Series FREQ="A" TIME_PERIOD="2008" SEX="M" COUNTRY="BE"> <Obs xsi:type="demo:LEXPNSIT" OBS_VALUE="77.5" OBS_STATUS="A" UNIT_MEASURE="YRS"UNIT_MULT="0" /> <Obs xsi:type="demo:LBIRTHST" OBS_VALUE="63926" OBS_STATUS="P" UNIT_MEASURE="PERS"UNIT_MULT="0" /> <Obs xsi:type="demo:DEATHST" OBS_VALUE="50270" OBS_STATUS="P" UNIT_MEASURE="PERS"UNIT_MULT="0" /> </Series> - <Series FREQ="A" TIME_PERIOD="2009" SEX="M" COUNTRY="BE"> <Obs xsi:type="demo:LEXPNSIT" OBS_VALUE="77.7" OBS_STATUS="P" UNIT_MEASURE="YRS"UNIT_MULT="0" /> <Obs xsi:type="demo:LBIRTHST" OBS_STATUS="M" UNIT_MEASURE="PERS" UNIT_MULT="0" /> <Obs xsi:type="demo:DEATHST" OBS_STATUS="M" UNIT_MEASURE="PERS" UNIT_MULT="0" /> </Series> - <Series FREQ="A" TIME_PERIOD="2007" SEX="F" COUNTRY="BE"> <Obs xsi:type="demo:LEXPNSIT" OBS_VALUE="83.3" OBS_STATUS="A" UNIT_MEASURE="YRS"UNIT_MULT="0" /> <Obs xsi:type="demo:LBIRTHST" OBS_VALUE="60614" OBS_STATUS="A" UNIT_MEASURE="PERS"UNIT_MULT="0" /> <Obs xsi:type="demo:DEATHST" OBS_VALUE="50854" OBS_STATUS="A" UNIT_MEASURE="PERS"UNIT_MULT="0" /> </Series> + <Series FREQ="A" TIME_PERIOD="2008" SEX="F" COUNTRY="BE"> + <Series FREQ="A" TIME_PERIOD="2009" SEX="F" COUNTRY="BE"> + <Series FREQ="A" TIME_PERIOD="2007" SEX="T" COUNTRY="EL"> + <Series FREQ="A" TIME_PERIOD="2008" SEX="T" COUNTRY="EL"> + <Series FREQ="A" TIME_PERIOD="2009" SEX="T" COUNTRY="EL"> + <Series FREQ="A" TIME_PERIOD="2007" SEX="M" COUNTRY="EL"> + <Series FREQ="A" TIME_PERIOD="2008" SEX="M" COUNTRY="EL"> + <Series FREQ="A" TIME_PERIOD="2009" SEX="M" COUNTRY="EL"> + <Series FREQ="A" TIME_PERIOD="2007" SEX="F" COUNTRY="EL"> + <Series FREQ="A" TIME_PERIOD="2008" SEX="F" COUNTRY="EL"> + <Series FREQ="A" TIME_PERIOD="2009" SEX="F" COUNTRY="EL"> </message:DataSet> </message:StructureSpecificData>

Figure 43. Example SDMX data responses that contains statistical data

Based on the introduced webservices it is possible to use the EuroStat data for the visualization

purposes. Unfortunately it has to be mentioned that only indicators from “Database by themes” can

be accessed through the named webservices. Indicators from e.g. “Tables by themes” have to be

accessed by the linked sdmx‐zip file in the hierarchy file. One of these files will be for instance the zip

file about the ‘Population density by NUTS 2 regions’ (datasetname is ‘tgs00024’):

http://epp.eurostat.ec.europa.eu/NavTree_prod/everybody/BulkDownloadListing?

file=data/tgs00024.sdmx.zip

Such a zip file contains two files:

datasetname.dsd.xml

datasetname.sdmx.xml

The dsd file contains the meta‐information about the indicator including the available dimensions. The

sdmx file contains the “real” statistical data.


4.3.2 TheSemaVisserviceIn general the visualizations could use the EuroStat webservices directly, but for a better performance

and error correction of performed requests, it is better to handle especially the dimension extraction

and the statistical requests by an own service. Another advantage is the ability to provide an emulation

mode for indicators from other parts then “Database by themes”, so that the visualization can also

show these indicators. The required performance to extract and process the large result files (up to

180MByte) is invested by the server instead of the performance‐limited client (the browser), which

will merely get the statistic results in a size range of Kbytes.

In the current integration we implemented three general request modes. The first is to request the

indicator hierarchy:

http://server.tld:8080/SemaService/jf/v1/eurostat.jsp?action=getindicatorlist&output=raw

The listed result of the hierarchy is very large and will looks like this:

<?xml version="1.0" encoding="UTF-8"?> <SemaService version="0.1.6"> <Request> <Bridge id="EuroStatBridge" version="0.1.3"> <bridgeParam name="action">getindicatorlist</bridgeParam>

<bridgeParam name="output">raw</bridgeParam> </Bridge> </Request> <Response> <Bridge id="EuroStatBridge" version="0.1.3"> <indicatorlist> <nt:tree xmlns:nt="urn:eu.europa.ec.eurostat.navtree" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" creationDate="20140911T1101" xsi:schemaLocation="urn:eu.europa.ec.eurostat.navtree http://epp.eurostat.ec.europa.eu/NavTree_prod/htdocs/xsd/TableOfContent.xsd"> <nt:branch> <nt:title language="en">Database by themes</nt:title> <nt:title language="fr">Base de données par thèmes</nt:title> <nt:title language="de">Datenbank nach Themen</nt:title> <nt:code>data</nt:code> <nt:children> <nt:branch> <nt:title language="en">General and regional statistics</nt:title> <nt:title language="fr">Statistiques générales et régionales</nt:title> <nt:title language="de">Allgemeine und Regionalstatistiken</nt:title> <nt:code>general</nt:code> <nt:children> <nt:branch> <nt:title language="en">European and national indicators for short-term analysis</nt:title> <nt:title language="fr">Indicateurs européens et nationaux pour l'analyse à court terme</nt:title> <nt:title language="de">Europäische und nationale Indikatoren fürkonjunkturelle Analysen</nt:title> <nt:code>euroind</nt:code> <nt:children> <nt:branch> <nt:title language="en">Business and consumer surveys (source: DG ECFIN)</nt:title> <nt:title language="fr">Enquêtes de conjoncture et de consommation (source: DG ECFIN)</nt:title> <nt:title language="de">Konjunktur- und Verbrauchererhebungen (Quelle: DG ECFIN)</nt:title> <nt:code>ei_bcs</nt:code> <nt:children> <nt:branch>


<nt:title language="en">Consumer surveys (source: DG ECFIN)</nt:title> <nt:title language="fr">Enquêtes de consommation (source: DG ECFIN)</nt:title> <nt:title language="de">Verbrauchererhebungen (Quelle: DG ECFIN)</nt:title> <nt:code>ei_bcs_cs</nt:code> <nt:children> <nt:leaf type="dataset"> <nt:title language="en">Consumers - monthly data</nt:title> <nt:title language="fr">Consommateurs - données mensuelles</nt:title> <nt:title language="de">Verbraucher - monatliche Daten</nt:title> <nt:code>ei_bsco_m</nt:code> <nt:lastUpdate>28.08.2014</nt:lastUpdate> <nt:lastModified>28.08.2014</nt:lastModified> <nt:dataStart>1985M01</nt:dataStart> <nt:dataEnd>2014M08</nt:dataEnd> <nt:values>204474</nt:values> <nt:unit language="en"/> <nt:unit language="fr"/> <nt:unit language="de"/> <nt:shortDescription language="en"/> <nt:shortDescription language="fr"/> <nt:shortDescription language="de"/> <nt:metadata format="html">http://epp.eurostat.ec.europa.eu/cache/ITY_SDDS/en/ei_bcs_esms.htm</nt:metadata> <nt:metadata format="sdmx">http://epp.eurostat.ec.europa.eu/NavTree_prod/everybody/BulkDownloadListing?file=metadata/ei_bcs_esms.sdmx.zip</nt:metadata> <nt:downloadLink format="tsv">http://epp.eurostat.ec.europa.eu/NavTree_prod/everybody/BulkDownloadListing?file=data/ei_bsco_m.tsv.gz</nt:downloadLink> <nt:downloadLink format="dft">http://epp.eurostat.ec.europa.eu/NavTree_prod/everybody/BulkDownloadListing?file=data/ei_bsco_m.dft.gz</nt:downloadLink> <nt:downloadLink format="sdmx">http://epp.eurostat.ec.europa.eu/NavTree_prod/everybody/BulkDownloadListing?file=data/ei_bsco_m.sdmx.zip</nt:downloadLink> </nt:leaf>

[…] </nt:children> </nt:branch> </nt:children> </nt:branch> </nt:children> </nt:branch> </nt:children> </nt:branch> </nt:children> </nt:branch> </nt:tree> </indicatorlist> </Bridge> </Response> </SemaService>

The second is the brief overview about the dimensions by requesting:

http://server.tld:8080/SemaService/jf/v1/eurostat.jsp?action=getdimensions&

datasetname=nama_gdp_c&output=simple


This request will list the available dimensions and codes in a short form (in contrast to the raw output

from EuroStat, which is shown in Figure 42):

<?xml version="1.0" encoding="UTF-8"?> <SemaService version="0.1.6"> <Request> <Bridge id="EuroStatBridge" version="0.1.3"> <bridgeParam name="datasetname">nama_gdp_c</bridgeParam>

<bridgeParam name="action">getdimensions</bridgeParam> <bridgeParam name="output">simple</bridgeParam>

</Bridge> </Request> <Response> <Bridge id="EuroStatBridge"> <dimensions> <dimension id="FREQ" index="0">

<refClass classID="CL_FREQ" /> <codeList>

<CodeListItem id="D"> Daily </CodeListItem> <CodeListItem id="W"> Weekly </CodeListItem> <CodeListItem id="Q"> Quarterly </CodeListItem>

[…] </codeList>

</dimension> <dimension id="UNIT" index="1">

<refClass classID="CL_UNIT" /> <codeList>

<CodeListItem id="EUR_HAB"> Euro per inhabitant </CodeListItem> <CodeListItem id="NAC_HAB"> National currency per inhabitant </CodeListItem> <CodeListItem id="PPS_HAB"> Purchasing Power Standard per inhabitant </CodeListItem>

[…] </codeList>

</dimension> <dimension id="INDIC_NA" index="2">

<refClass classID="CL_INDIC_NA" /> <codeList>

<CodeListItem id="B1GM"> Gross domestic product at market prices </CodeListItem> <CodeListItem id="P3"> Final consumption expenditure </CodeListItem> <CodeListItem id="P3_P5"> Domestic demand </CodeListItem>

[…] </codeList>

</dimension> <dimension id="GEO" index="3">

<refClass classID="CL_GEO" /> <codeList>

<CodeListItem id="EU28"> European Union (28 countries) </CodeListItem>


<CodeListItem id="EU27"> European Union (27 countries) </CodeListItem>

… <CodeListItem id="BE"> Belgium </CodeListItem> <CodeListItem id="BG"> Bulgaria </CodeListItem> <CodeListItem id="CZ"> Czech Republic </CodeListItem>

[…] </codeList>

</dimension> </dimensions> </Bridge> </Response> </SemaService>

Figure 44. Dimension listing of the SemaVis service to a specific dataset/indicator

The third is to request the concrete statistical data by mentioning the dimensions to filter the data on

the relevant pieces:

http://server:8080/SemaService/jf/v1/eurostat.jsp?action=getstatdata&

datasetname=nama_gdp_c&dim=A&dim=EUR_HAB&dim=B1GM&dim=DE,SE,BE&

startperiod=2010&endperiod=2014&output=raw

These requests will response the concrete statistic data results:

<?xml version="1.0" encoding="UTF-8"?> <SemaService version="0.1.6"> <Request> <Bridge id="EuroStatBridge" version="0.1.3">

<bridgeParam name="datasetname">nama_gdp_c</bridgeParam> <bridgeParam name="action">getstatdata</bridgeParam> <bridgeParam name="dim">A</bridgeParam> <bridgeParam name="dim">EUR_HAB</bridgeParam> <bridgeParam name="dim">B1GM</bridgeParam> <bridgeParam name="dim">DE,SE,BE</bridgeParam> <bridgeParam name="startperiod">2010</bridgeParam> <bridgeParam name="endperiod">2014</bridgeParam> <bridgeParam name="output">raw</bridgeParam>

</Bridge> </Request> <Response> <Bridge id="EuroStatBridge" version="0.1.3"> <statdata> <message:GenericData xmlns:message="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/message" xmlns:common="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/common" xmlns:footer="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/message/footer" xmlns:generic="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/data/generic" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <message:Header> <message:ID>123d9799c11edf9b19f48d519fd3cebc</message:ID> <message:Test>false</message:Test> <message:Prepared>2014-09-11T13:44:15</message:Prepared> <message:Sender id="ESTAT"> <common:Name xml:lang="en">Eurostat</common:Name>


<message:Timezone>+01:00</message:Timezone> </message:Sender> <message:Receiver id="RECEIVER"/> <message:Structure dimensionAtObservation="TIME_PERIOD" structureID="ESTAT_DSD_nama_gdp_c_1_0"> <common:Structure> <Ref agencyID="ESTAT" id="DSD_nama_gdp_c" version="1.0"/> </common:Structure> </message:Structure> <message:DataSetAction>Append</message:DataSetAction> <message:DataSetID>nama_gdp_c</message:DataSetID> </message:Header> <message:DataSet structureRef="ESTAT_DSD_nama_gdp_c_1_0"> <generic:Series> <generic:SeriesKey> <generic:Value id="UNIT" value="EUR_HAB"/> <generic:Value id="INDIC_NA" value="B1GM"/> <generic:Value id="GEO" value="BE"/> <generic:Value id="FREQ" value="A"/> </generic:SeriesKey> <generic:Obs> <generic:ObsDimension value="2013"/> <generic:ObsValue value="34500.0"/> </generic:Obs> <generic:Obs> <generic:ObsDimension value="2012"/> <generic:ObsValue value="34000.0"/> </generic:Obs> <generic:Obs> <generic:ObsDimension value="2011"/> <generic:ObsValue value="33600.0"/> </generic:Obs> <generic:Obs> <generic:ObsDimension value="2010"/> <generic:ObsValue value="32700.0"/> </generic:Obs> </generic:Series> <generic:Series>

[…] </message:DataSet> </message:GenericData> </statdata> </Bridge> </Response> </SemaService>

Figure 45. Statistic data result response from the SemaVis service to a specific dataset/indicator

After this last request the visualization could show the final results.

4.4 VisualizationofSimulationResults(final)PrototypeThe visualization of simulation data is similar to the visualization of statistical data and respectively

Open Government Data. The idea is to show the calculated results that were simulated, in interactive

visualization (in particular in charts and diagrams).

From the visualizations point of view the task is – as already mentioned – to visualize statistical

simulation result‐data that is close to (Open Government) statistic data visualization (see therefore the

Visualization of Statistical Data Prototype in section 4.3). However, the API is different. Because of the

absence of an API for simulation data at the CorePlatform from WP3, it was necessary to define an API


for the data exchange between WP4 and WP5. This specified API will be explained in the following

section.

4.4.1 TheStatisticDataSimulatorAPIThe current API consists only of the elements of WP5/SemaVis and WP4/Simulators and there servers.

Figure 46 shows the general workflow with inclusion of WP3 which will later act as proxy between the

systems. Currently this proxy is not used and it is also not necessary.

At the beginning of the visualization, SemaVis requires a simulation ID, based on which the correct

index file for the simulated indicators could be detected. The index file contains the semantic structure

about the simulation indicators through which the user can elaborate.

Figure 46. The technical data processing between the SemaVis visualization and the simulators of WP4. In

this figure WP3 provides a proxy between the SemaVis and Simulator servers, but this proxy is optional.

(Image by WP4/SocSim)

If the user chooses an indicator, the corresponding data in a CSV file format will be requested from a

server and visualized. Through an integration in the simulators also an advanced data provision, e.g.

for different simulation cycles could be considered for an advanced data visualization.


4.4.2 RetrievaloftheHierarchyaboutSimulationResultsTo specify the simulator ID a simple Interface was defined:

http://server.tld/FupolSimulation/?sim=URI

To show a concrete simulator, e.g. for Vodno Mountain, the definition can be like this:

http://server.tld/FupolSimulation/?sim=http://dev.fupol.lv/simulator/result.xml

The URI refers to a concrete simulation index file, which contains all visualization relevant information,

as for instance the final indicators and where the data are stored and can be retrieved. As a result, the

visualization requests the index file which will be like this:

<?xml version="1.0" encoding="UTF-8"?> <xml>

<geolocation>Skopje</geolocation> <category name="Occupancy"> <category name="By day">

<indicator name="Monday" url="http://dev.fupol.lv/simulator/day1.csv">

<param name="xAxis" type="string">category</param> <param name="unit" type="string">people</param>

</indicator> <indicator name="Tuesday" url="http://dev.fupol.lv/ simulator /day1.csv">

<param name="xAxis" type="string">category</param> <param name="unit" type="string">people</param>

</indicator> […]

</category> <category name="station3">

[…] </category>

[…] </category>

</xml>


The index file contains majorly the semantic structure of the generated results.

4.4.3 RetrievalofconcreteSimulationResultsThe concrete indicators are described in the index file like this:

<indicator name="Thursday" url="http://dev.fupol.lv/simulator/day1.csv"> <param name="xAxis" type="string">time</param> <param name="unit" type="string">people</param>

</indicator>


The provided information are general aspects as for instance the path to the file with the real statistical

data about this concrete indicator, as well as additional information as for instance the name, the used

units and what the x‐axis will represent (time or category based).

After the user has chosen a simulation indicator, the system tries to request the csv file, which is

mentioned to the indicator. The statistical results are provided in a simple CSV format (as for instance


shown in Figure 49). Based on this data the charts can be generated and will be displayed to the user.

In contrast to the simulator visualizations, SemaVis allows a dynamic orchestration of different

visualization, which enables user to explore the data based on their personal preferences.

Title;RH1;RH10;RH11;RH16;RH19;RH20;RH8;RL1;RL5;RP14;RT1;RT4;RT5 0:00;0;0;0;0;18;0;0;12;6;0;6;473;18 1:00;0;0;0;0;18;0;0;12;6;0;6;473;18 2:00;0;0;0;0;0;0;0;0;0;0;0;0;0 3:00;0;0;0;0;0;0;0;0;0;0;0;0;0 4:00;0;0;0;0;0;0;0;0;0;0;0;0;0 5:00;0;0;0;0;0;0;0;0;0;0;0;0;0 6:00;0;0;0;0;0;0;0;0;0;0;0;0;0 7:00;0;0;0;0;0;0;0;0;0;0;0;0;0 8:00;0;0;0;0;0;0;0;0;0;0;0;0;0 9:00;0;0;0;0;0;0;0;0;0;0;0;0;0 10:00;12;18;0;0;0;0;0;0;0;0;3;0;0 11:00;4;0;0;5;3;15;0;46;0;0;53;0;0 12:00;15;7;10;9;6;6;74;26;46;4;83;85;184 13:00;0;0;0;0;18;0;0;12;6;0;6;473;18 14:00;16;18;0;0;5;10;0;28;9;0;60;85;184 15:00;15;10;7;14;0;0;74;10;0;0;28;473;18 16:00;0;0;0;0;0;0;0;0;0;0;224;0;0 17:00;0;0;0;0;0;0;0;0;0;0;0;0;0 18:00;0;0;0;0;0;0;0;0;0;0;0;0;0 19:00;0;0;0;0;0;0;0;0;0;0;0;0;0 20:00;0;0;0;0;0;0;0;0;0;0;0;0;0 21:00;0;0;0;0;0;0;0;0;0;0;0;0;0 22:00;0;0;0;0;0;0;0;0;0;0;0;0;0 23:00;15;7;10;9;6;6;74;26;46;4;83;85;184


4.5 TechnicalRequirementsThe prototypes have some specific technical requirements that need to be considered on target

platforms.

4.5.1 PlatformSemaVis was developed with the help of the Software Development Kit (SDK) of Adobe Flex 4.5 and

the corresponding script language ActionScript 3.0 (AS3) for the Adobe Flash Player 16.0. It runs so far

system independent as far as the mentioned Flash Player and the version is available. Adobe Flash

Player is a system independent virtual machine (VM) and runs on most established operation systems,

e.g. Windows, Linux and OS X.

More information about the Flash player is available under:

[http://www.adobe.com/de/products/flashplayer/].

4.5.2 InstallationandUpgradeSemaVis was developed for the Adobe Flash Player 16.0, which is a requirement anyway. To install or

update the Adobe Flash Player on the required version, we have to refer on their installation

instructions. SemaVis as (web‐) application does not have an explicit installation routine.

System requirements


Webbrowser: IE 11, Firefox 38, or Chrome 43

Plugin:

Adobe Flash Player 16.0

(Even more the system requirements of the Adobe Flash Flayer 16.0 have to

be considered. They can be seen under http://www.adobe.com.)


5 Summary This document describes the final iteration of the developments in WP5 Advanced Visualizations. The

document reflects a revised documentation, which explains the faced visualizations scenarios: the

social data visualization, the statistical data visualization, the simulation and impact visualization and

the FUPOL knowledge database and visualization. In social media or social data visualization the

implemented results that use a refined interfacing to the real data were presented. In this context the

aspects of adaptation that were tested and evaluated with ground truth data, were introduced and

referred to the published conference proceedings. The second scenario introduced statistical data and

the combined view with semantics. Therefore it uses a new API to the EuroStat data library so that the

whole EuroStat library could be visualized. To complete the analysis based on statistical data, also an

explanation tool was described that allows finding rules based on Linked‐Open Data, which provide

reasons why certain statistical data are as they are. Even more the scenario about simulation data

visualization was described, which is similar to the statistic data visualization but uses forecast data. It

was extended for the visualization of the simulation model too, to allow analyst to compare the

forecast with the undelaying model. The progress on fourth scenario, the knowledge base visualization,

was also explained, which enables stakeholders of the policy making process to explore the knowledge

of Linked‐Open Data for further information about problems and solutions for political problems.

Afterwards, the Fraunhofer’s evaluation system was explained, with which the visualizations are being

evaluated in an empirical and practical manner. It allows, e.g. WP7 and the pilot user in the cities, to

test the visualizations in practical scenarios and give their feedbacks to it. This helps to improve the

visualization in a more accurate manner, because of the precise feedback in form explicit and implicit

system‐use feedbacks. The document concluded with the progress of development as a summarized

view in the section release notes.


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thesis, School of Information Sciences, University of Pittsburgh (2010)

[AnDW02] Anderson, C.R., Domingos, P., Weld, D.S.: Relational markov models and their

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SIGKDD international conference on Knowledge discovery and data mining, New York,

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[BNSS12] Dirk Burkhardt, Kawa Nazemi, Peter Sonntagbauer, Susanne Sonntagbauer (eds.):

Deliverable 2.19 ‐ Policy Design Process and FUPOL Component Integration, Public

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[BNSS13] Burkhardt, D., Nazemi, K., Sonntagbauer, P., Sonntagbauer, S., Kohlhammer, J. (2013).

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7 List of Figures

Figure 1. General Date Processing and use-cases within FUPOL project (taken from Deliverable 3.6 [Rumm13b]). ............................................................................................ 6

Figure 2. Screenshot of the integrated SemaVis visualization in the FUPOL CorePlatform, based on a Zagreb campaign. ............................................................................................. 7

Figure 3. Overview-to-Detail approach for the visualization interaction in Social Media Data 8

Figure 4. Concept for visualizing topics over time, including aspects as quantity of topics, relevancies and temporal spread. ....................................................................................... 9

Figure 5. Simplified abstract illustration of the hierarchical treemap ........................................ 9

Figure 6. Filtering by time through the timeline sliders in the SemaTime visualization ......... 10

Figure 7:Graph-based detail visualization (own development) ............................................... 11

Figure 8: adapted social data visualization .............................................................................. 12

Figure 9. Data processing pipeline from EuroStat through the service to the SemaVis visualization technology ................................................................................................... 14

Figure 10. General architecture of SemaVis to request statistical data based on the SDMX standard for the visualization. .......................................................................................... 16

Figure 11. The User Interface Design of the first statistical data visualization prototype, it orients strongly on the required function for statistical analysis. ..................................... 17

Figure 12. Overview-to-Detail approach for the visualization interaction in Statistical Data to provide an intuitive drill-down strategy in SemaVis to find relevant and necessary indicators based on exploration through the indicator network and hierarchy and stat analyzing on indicators of interest. .................................................................................. 18

Figure 13. Interaction and data flow diagram to process the data on the server, beginning with the statistic-data input and generation of the explanations. ..................................... 21

Figure 14. User-Interface Results of the used Explain-a-LOD service together with SemaVis 22

Figure 15. The visualization of the simulation model, which can be added to the visualization cockpit next to simulation results. .................................................................................... 24

Figure 16. A sketch of the specified API between the simulator technologies of WP4 and the advanced visualization technologies of WP5 and how the data is shared. In the normal operation simulators and advanced visualizations have a direct data connection, but maybe later a proxy can also be used at WP3 (Image by WP4/SocSim). ................................... 25

Figure 17. The comparative view on EuroStat data visualization and Simulation result visualization. .................................................................................................................... 25

Figure 18. Screenshot of the Simulation Result Visualization in SemaVis ............................. 26


Figure 19. SemaVis Visualization integration into Simulator ................................................. 27

Figure 20: Inclusion of semantics from Web data-bases ......................................................... 28

Figure 21: Adaptation based on search term: SemaVis adapts in this application scenario based on the searched term. In (a) the user entered the term Obama for search, the results are giving in categories and hierarchies on both data-bases. In (b) the user entered the more specific search terms Barack Obama. In this case SemaVis visualizes all results, but selects the most appropriate result based on weighing measure of the data-bases. ..................... 29

Figure 22: Automatic selection of sub-concepts ...................................................................... 30

Figure 23: Automatic Adaptation based on the Canonical User Model .................................. 31

Figure 24: Visual adaptation for differing user ........................................................................ 32

Figure 25. General workflow of the evaluation system for testing the visualizations ............. 33

Figure 26. Initial screen where the user gets asked to enter the participant-code. ................... 34

Figure 27. The introduction screen informs the user about the general procedure and what and witch data will be used and how it will be processed afterwards. ................................... 35

Figure 28. Demographic questionnaire where the user has to answer some questions about himself, like age and gender. ............................................................................................ 35

Figure 29. Questionnaire about the computer experience of the user ...................................... 36

Figure 30. The visualization introduction screen introduces into the general visualization system and where which functions are placed on the screen. .......................................... 36

Figure 31. Overview of the practical evaluation step is shown. On the top the question is shown with some possible answers the user should find with the visualizations. Underneath the question bar, the real prototype is integrated and should be used to find the correct answer in the visualized data. ....................................................................................................... 37

Figure 32. The use experience questionnaire is shown after the practical evaluation. ............ 38

Figure 33. The FUPOL questionnaire covers questions about the general use and scope in the project ............................................................................................................................... 38

Figure 34. Final screen to inform the user that evaluation was successfully performed. ........ 39

Figure 35. Example configuration of an experiment and the used questionnaire in the XML-based configuration .......................................................................................................... 40



Figure 38. Example CSV result (in Excel) for a conventional questionnaire .......................... 41


Figure 39. Example CSV result (in Excel) about a practical evaluation questionnaire, where the user has to find answers in the visualization software ..................................................... 42

Figure 40. The defined APIs of the FUPOL Core Platform the exchange data with clients. .. 43

Figure 41. The alignment and mapping of the various data for the visualizations within SemaVis. .......................................................................................................................... 44

Figure 42. Example SDMX-ML responses of meta-information about a data request ............ 49

Figure 43. Example SDMX data responses that contains statistical data ................................ 50

Figure 44. Dimension listing of the SemaVis service to a specific dataset/indicator .............. 54

Figure 45. Statistic data result response from the SemaVis service to a specific dataset/indicator .......................................................................................................................................... 55

Figure 46. The technical data processing between the SemaVis visualization and the simulators of WP4. In this figure WP3 provides a proxy between the SemaVis and Simulator servers, but this proxy is optional. (Image by WP4/SocSim) ........................................................ 56




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