evolving mashup interfaces using a distributed machine learning and model transformation methodology

Sixth International Workshop on Information Systems in Distributed Environment (ISDE’2015) Rhodes, Greece, 26–30 October 2015

Evolving Mashup Interfaces using a Distributed Machine Learning and Model Transformation

Methodology

Antonio Jesús Fernández-García1, Luis Iribarne1, Antonio Corral1

and James Z. Wang2

1 Applied Computing Group, University of Almería, Spain

2 The Pennsylvania State University, USA

Sixth International Workshop on Information Systems in Distributed Environment (ISDE’2015)

Rhodes, Greece, 26–30 October 2015

TIN2013-41576-R P10-TIC-6114


Index

•  Context and Motivation •  Methodology Proposal

•  Case Study •  Conclusions •  Future Work

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Background & Context Main problem: how to store and analyze the user interaction, which is provided, in many cases, by different kinds of users.

Hot topic on big data & machine learning in which we are involved

Background, Context and Motivation


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Concrete Model 1 Concrete Model 2

Abstract Model 1 Abstract Model 2

Rules

Model Transformation



•  Iribarne et al. and Criado et al. –  component-based architectures are able to compose both dynamically

and autonomously

•  Adjustments are based on a number of adaptation rules which are defined statically in a rule repository (Design Time)

•  Changes in requirements. Changes in context. New users. New components.

•  Dynamic adaptive appears to be insufficient

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•  Dynamic adaptation EVOLVE over time based on –  Changes in users and components

–  Changes in the information system

–  Changes in the context

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Discovery of behavioural

patterns

Create Prediction

Models

Update Set of Adaptation

Rules

Methodology Proposal

Machine Learning Model Transformation

Cloud Computing Big Data


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Let’s save it: -  Operations Performed

-  Workspace Situation

-  Internal System Information

-  Context Information

#1 Distributed Storage of User Behaviour


WorkSpace Services Menu

Services Component

Component Menu

Operations


Mashup UI


Mashup UI Mashup UI

{Services, Components, Operations} {Services Menu, WorkSpace, Component Menu}

Services = {Service 1, Service 2, …, Service N}

Components = {Component 1, Component 2, …, Component M} Component X = {PosX, PosY, Width, Height}

Operations = {Add, Delete, Move, Resize}


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Information of: -  Users Categories -  Users Subcategories -  Users Sessions -  Users Interactions -  Time of the Day -  How components are used -  When components are used -  Where components are used -  How components are positioned

Context & System Information Methodology Proposal


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#2 Creating Distributed Data Views

•  Data views are composed with information spread in the whole infrastructure

•  The intented objective is to organise data

•  The structure given to data tries to optimize the results of the execution of Machine Learning Experiments



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#3 Application of Machine Learning Algorithms

•  Study and Analyze Machine Learning Algorithms and apply them to the “Data Views”.

•  Objective: Generates new rules capable of improving the user interface adaptation

•  Clustering, Association Learning, Classication, Similarity Matching, Neural Networks, Bayesian Networks,Genetic Algorithms…



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#4 Conversion Rules

HOT = Higher Order Transformation Inferred rules have their own format. Must be transformed to the format suited

in the interface where they will be applied. Model Transformation



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#5 Evaluation of Rules

•  Should a new rule added to the repository or not?

•  Conflict Manager: Handle potential conflicts



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ENIA ENvironmental Information Agent

Case Study


Components Aggrupation COTSget Component 2: Geo Parks

Component 1: Cuttle Roads

Base Map

Commercial off-the-shell

+ Widgets


WorkSpace Services Menu

Services Component

COTSget Menu

COTSget

Operations


COScore Mashup UI


COScore Enia Mashup UI

{Services, CotsGET, Components, Operations} {Services Menu, WorkSpace, CotsGET Menu}

Services = {Service 1, Service 2, …, Service N}

CotsGETs= {CotsGET 1, CotsGET 2, …, CotsGET L} Components = {Component 1, Component 2, …, Component M}

CotsGET X = {PosX, PosY, Width, Height}

Operations = {Add, Delete, Move, ResizeBigger, ResizeSmaller} + {Group, Ungroup, AddGroup, UngroupDelete, UngroupGroup} +

{Maximize, Minimize}


#1 Distributed Storage of user Behaviour

•  Operation carried out on the interface: Add Component, Delete Component, Move Component, Resize Component…

•  User Information: Name, Category, Home Region, Personal data…

•  Temporal Information: Date, Time, Day, Month, Season…

•  Location Information: latitude, longitude, proximity to coast…

•  Other Information: Temperature, Humidity, Wind, Precipitation…

•  State of the Worspace: Location and size of all components

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Case Study


Acquisition Data Web Service

ENIA Core Web Services with Context Information

Client (ENIA)

2) ENIA Core. Information about Users and Components à http://www.enia.dreamhosters.com/webservices

1) Azure Cloud Web App. Written in PHP à http://getinteraction.azurewebsites.net/service.php

3) Context Information. I.E: Temperature à http://www.webservicex.net/globalweather.asmx?op=GetWeather

4) SQL Azure Database à basedatosinteraccion.database.windows.net

Schema Graphic JSON Schema


#2 Creating Distributed DataViews.

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Case Study Case Study

User Category Time of day Date Localization Tourist 15:00-17:00 Summer Near to Cost

User Category Date Operation Component Farmer From January to March Insert Component Tomate Seeds Providers

Set of structured data so they can serve as entry elements to machine learning algorithms

Insert “Beach Temperatures” Component

Insert “Tomate Seeds Prices” Component


idUser userType idSession idInteraction dateTime operationPerformed idComponent

idUser userType isNewUser : boolean season : list operationPerformed (filter = add) idComponent (ObjectiveField)

Before FE After FE

Decission Trees --- Ensemble --- Random Decision Forest

#2 Creating Distributed DataViews (Feature Engineering)

#3 Application of Machine Learning Algorithms.


#2 Creating Distributed DataViews (Feature Engineering)

idUser userType idSession idInteraction dateTime operationPerformed idComponent

idUser userType isNewUser : boolean season : list operationPerformed (filter = add) idComponent (ObjectiveField)

Before FE After FE

clusterField Clustering

idUser (let’s keep it)

Clustering --- Decission Trees --- Ensemble --- Random Decision Forest #3 Application of Machine Learning Algorithms.



•  We need to identify the time of day when a “tourist” user will need to find out the water temperature of a beach by using the “temperature of beaches" component.

Supervised Regression Algorithms

•  Supervised à we already have data of previous tourists who have used the “temperature of beaches" component.

•  Regression Algorithm à predict a continuous value output (time of day) depending on a series of attributes (user type, season, location…).

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•  We need to identify when a component, after apply the “ResizeBigger” operation, would be interesting to show it at FullScreen using the “Maximize” operation.

Supervised Classification Algorithms

•  Supervised à we already have data of previous tourists who have used the “temperature of beaches" component.

•  Classification Algorithm à predict a discrete value output (whether to show the component full screen or not) depending on a series of attributes (component size before resize, component size after resize, …).

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•  Divide users depending the component they manipulates and, based on that segmentation, suggest them services for this new type of user detected.

Unsupervised Clustering Algorithms

•  Unsupervised à A priori we have no idea what component are used for the users (or what components they will use in the future).

•  Clustering Algorithm à We expect the algorithm to “group” users based on the components they manipulate.

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#4 Conversion Rules •  The format of ENIA rules is ATL (a transformation model language

which also provides a number of tools for Model Driven Engineering)

•  We create a HOT (Higher Order Transformation) to transform the output of the algorithm to ATL Rules

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#5 Evaluation of Rules

•  In case of contradiction, the new rule is prioritized

•  Old rules are not deleted (just ignored). We keep track of the historical set of rules


•  A methodology is proposed to allow mashup user interfaces in distributed systems to adapt to user requirements at runtime, be intelligent and evolve over time through the use of machine learning and model transformations.

•  The methodology consists of 5 steps –  Save User Behaviour –  Create Data Views –  Apply ML Algorithms –  Rules Transformation using HOT –  Evaluate new generated Rules

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Conclusions

Conclusions


•  Define a Data Acquisition Process.

•  Creation of automatic learning systems (fed data automatically view Web Services and apply ML algorithms continuously updating the rules repository)

•  Statistical learning techniques will be used to classify or group different user intentions for individualized interface treatment.

•  Optimization or improvement of algorithms to optimize results.

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Future Work

Future Work


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Contraportada

Thank you for your attention


Evolving Mashup Interfaces using a Distributed Machine Learning and Model Transformation

Methodology

Antonio Jesús Fernández-García1, Luis Iribarne1, Antonio Corral1

and James Z. Wang2

1 Applied Computing Group, University of Almería, Spain

2 The Pennsylvania State University, USA

Sixth International Workshop on Information Systems in Distributed Environment (ISDE’2015)

Rhodes, Greece, 26–30 October 2015

TIN2013-41576-R P10-TIC-6114

evolving mashup interfaces using a distributed machine learning and model transformation methodology

Education