Date: 09/06/2014

User Requirements for Geospatial Provenance

Daniel Garijo, Andreas Harth, Yolanda Gil

Ontology Engineering Group. Universidad Politécnica de MadridInformation Sciences Institute, University of Southern California

Institute AIFB, Karlsruhe Institute of Technology

Problem statement

Maps can integrate many different sources• Open Street Maps• GeoNames• CIA World Factbook• Etc.

Interaction to standarize

2

Outline

1. Challenges

2. Assumptions

3. Types of provenance in the geospatial domain1. Provenance of datasets and sets of datasets2. Provenance of objects and sets of objects3. Provenance of properties and sets of properties4. Other requirements related to provenance

4. Modeling geospatial provenance with PROV-O1. Dataset level provenance

• Updating a map2. Object level provenance3. Property level provenance

5. Summary

6. Conclusions and Future work3

Challenges concerning provenance

Versioning and provenance (Map updates )

Trust based provenance Data integration and provenance

Crowdsourcing and provenance Granularity and provenance

Aggregation and provenance

4

Assumptions

Simplifying the problem…

• The entities across datasets have been mapped.

• The datasets share the same data model and vocabulary.

• Each dataset contains objects with unique identifiers.

• The integrated map is going to be presented to a user who is interested in using the information for some purpose.

5

Summary

1. Challenges

2. Assumptions

3. Types of provenance in the geospatial domain1. Provenance of datasets and sets of datasets2. Provenance of objects and sets of objects3. Provenance of properties and sets of properties4. Other requirements related to provenance

4. Modeling geospatial provenance with PROV-O1. Dataset level provenance

• Updating a map2. Object level provenance3. Property level provenance

5. Summary

6. Conclusions and Future work6

Types of provenance: Provenance of Datasets and sets of Datasets

Provenance of a map…

• Sources used to create the map• Creator of the map• Creation process used (algorithms, etc.) • Recent changes of the map• Reason why the map has been updated

Browsing different versions of a map…

• Most recent maps• Maps from an organization• Maps created from a version of a dataset or algorithm

Map release

June

OSM FAO GADM

Integration June

7

Types of provenance: Provenance of Objects and sets of Objects

Objects: lower granularity entities in the map

• Original data source of the object• Organizations responsible for the creation of the object• Date of creation of the object• Date of insertion of the object in the map• Process of inclusion in the dataset

Provenance of collections of objects…

• Source of the objects of a region/area• Objects from a specific organization• Objects belonging to a type of source (e.g., crowdsourced map)• Objects introduced in the last version of the map

A

B

C

bridge

stadiumintersection

8

Types of provenance: Provenance of Properties and sets of Properties

Properties: attributes of objects in a map

• Sources of the property• Creator of the property• Date of the creation/update of the property• Process by which the property was added

Provenance of sets of properties…

• Properties of objects coming from one data source• Properties of objects belonging to a crowdsourced

map• Properties of the selected objects that have the same source

9

Source A Source B

Height: 20 mLength: 1 kmName: 405 Fwy overpass

Other requirements related to provenance

10

Other requirements might not be straightforward to answer…

• How did a set of manual corrections help to improve the map?

• What is new in this map?

• What objects are integrated with a high confidence?

• Why is an object not appearing?

• General highlights of the map

…but they can be addressed having provenance records

Summary

1. Challenges

2. Assumptions

3. Types of provenance in the geospatial domain1. Provenance of datasets and sets of datasets2. Provenance of objects and sets of objects3. Provenance of properties and sets of properties4. Other requirements related to provenance

4. Modeling geospatial provenance with PROV-O1. Dataset level provenance

• Updating a map2. Object level provenance3. Property level provenance

5. Summary

6. Conclusions and Future work11

Modeling provenance in the geospatial domain: PROV-O extension

Simple PROV-O extension to model the dataset level

12

Dataset Level Provenance: Example

13

Dataset integration approaches

There are different alternatives for updating a map

14

Object level provenance: scalability

15

Property level provenance

16

Asserted properties do not have URIs!

• New entities for describing their provenance

Source A Source B

:Bridge :height 20m:Bridge :length 1 km

:Bridge :name “405 Fwy overpass”

:metadata1

:metadata2

prov:wasDerivedFrom

prov:wasDerivedFrom

Conclusions

17

Requirements and major challenges for geospatial provenance

4 main categories:•Provenance of datasets•Provenance of objects appearing in the map•Provenance of properties•OtherAnalogous questions are relevant for dataset/object/property provenance in non-geospatial domains.

Date: 09/06/2014

User Requirements for Geospatial Provenance

Daniel Garijo, Andreas Harth, Yolanda Gil

Ontology Engineering Group. Universidad Politécnica de MadridInformation Sciences Institute, University of Southern California

Institute AIFB, Karlsruhe Institute of Technology