Route planning using Linked Open Data

Presentation explaining the research question of my PhD

1. Route Planning using Linked Open Data ELIS - Multimedia Lab Ghent University - MMLab - iMinds 25th of May 2014 @pietercolpaert - #eswcphd2014Colpaert
2. How much data do you need when travelling?
3. Dataset 2 State o/t Art ELIS - Multimedia Lab «Server» Integrated dataset «Client» Asks difficult questions to the server Harvested each X days Dataset 1 Dataset 3 Dataset 4
4. Problem ELIS - Multimedia Lab «Server» Integrated dataset «Client» Asks difficult questions to the server Dataset 2 Dataset 1 Dataset 3 Dataset 4 Interoperability? Provenance? Data Quality? Trust? Has an Open World assumption
5. Solution ELIS - Multimedia Lab «Server» Integrated dataset «Client» Asks difficult questions to the server Harvested each X days Linked Open Data
6. Problem ELIS - Multimedia Lab «Server» Integrated dataset «Client» Asks difficult questions to the server Harvested each X days «Client» Asks difficult questions to the server «Client» Asks difficult questions to the server «Client» Asks difficult questions to the server «Client» Asks difficult questions to the server «Client» Asks difficult questions to the server «Client» Asks difficult questions to the server «Client» Asks difficult questions to the server Linked Open Data High Availability problems Expensive to host Not “Web” querying Hard to cache
7. ELIS - Multimedia Lab «User agent» Asks a set of simple smart questions Queried on last data published The LDF enabled Web: datasets with triple pattern support & counts Low cost, highly available HTTP caching Paper: http://LinkedDataFragments.org Querying the Web CONSTRUCT { … } WHERE { ?s1 ?p1 ?o1 . ?o1 ?p2 ?o2 . } ?s1 ?p1 ?o1 <o1> ?p2 ?o2
8. Research question ELIS - Multimedia Lab Can all Route Planning problems be fixed querying the Web in reasonable time? Evaluate by listing common problems and compare availability and user response times with state of the art
9. For example ELIS - Multimedia Lab Source: Dibbelt, Julian, et al. "Intriguingly simple and fast transit routing." Experimental Algorithms. Springer Berlin Heidelberg, 2013. 43-54. Minimum Expected Arrival Time (MEAT)
10. For example ELIS - Multimedia Lab «User agent» Asks a set of simple smart questions The LDF enabled Web: datasets with triple pattern support, counts & ordering handles? Low cost, highly available HTTP caching CONSTRUCT { … } WHERE { ?station transit:departures ?departures . ?departures ?p2 ?o2 . } ORDER BY ?departures Extra handles for helping route planning algorithms Querying the Web
11. Other problems ELIS - Multimedia Lab Minimum Expected Arrival Time (MEAT) Intermodal route planning (~federated querying) Real-time updates Travel time analysis ...
12. ELIS - Multimedia Lab Thank you Pieter Colpaert (pieter.colpaert@ugent.be) Twitter: @pietercolpaert #eswcphd2014Colpaert http://pieter.pm http://semweb.mmlab.be