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David Chaves-Fraga, Ontology Engineering Group
Universidad Politécnica de Madrid, Spain
Julian Rojas, IDLab - Ghent Univer...
The Tripscore Linked Data Client
The Tripscore Linked Data client: Problem and Motivation
2
▪ Public transport data is pub...
The Tripscore Linked Data Client
Linked Connections approach
3
The Tripscore Linked Data Client
From GTFS to Linked Connections
4
REAL-TIME
Historical data
from 5 weeks
How?
STATIC
Roja...
The Tripscore Linked Data Client
Architecture
5
SERVER
http://..../connections?
departureTime=20XX-XX-
XXT10:20:00.000Z
HT...
The Tripscore Linked Data Client
▪ Tripscore front-end
▪ Tripscore back-end
▪ All code is available at:
▪ Demo available a...
The Tripscore Linked Data Client
The Tripscore Linked Data client
7
The Tripscore Linked Data Client
Next Steps
8
static data static and real-time data
▪ New approaches (like GTFS2LC) to dea...
The Tripscore Linked Data Client
Open Questions
9
▪ Do we have to transform all transport data to LC?
▪ Could we propose t...
David Chaves-Fraga, Ontology Engineering Group
Universidad Politécnica de Madrid, Spain
Julian Rojas, IDLab - Ghent Univer...
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The tripscore Linked Data client: calculating specific summaries over large time series

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Demo paper about Tripscore, a Linked Data client that consumes historical data from a Linked Connections server and compares them with user preferences. Demo available at: http://tripscore.eu/

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The tripscore Linked Data client: calculating specific summaries over large time series

  1. 1. David Chaves-Fraga, Ontology Engineering Group Universidad Politécnica de Madrid, Spain Julian Rojas, IDLab - Ghent University Pieter-Jan Vandenberghe, IDLab - Ghent University Pieter Colpaert, IDLab - Ghent University Oscar Corcho, Ontology Engineering Group - UPM The tripscore Linked Data client: calculating specific summaries over large time series dchaves@email @dchavesf 22/10/2017 DeSemWeb - ISWC17
  2. 2. The Tripscore Linked Data Client The Tripscore Linked Data client: Problem and Motivation 2 ▪ Public transport data is published in a way in which analytical processing is too expensive ▪ Previous work: Linked Connections (LC) framework ▪ Cost-efficient publishing alternative to the de-facto GTFS standard ▪ Can Linked Data agents use LC to solve analytical queries over longer periods of time? ▪ New regulation by the EU Commission about discoverability and access to public transport data across Europe ▪ Why do not we use Google Maps? ▪ Centralized solution ▪ No Open data ▪ No Linked data ▪ No user preferences (or any other feature)
  3. 3. The Tripscore Linked Data Client Linked Connections approach 3
  4. 4. The Tripscore Linked Data Client From GTFS to Linked Connections 4 REAL-TIME Historical data from 5 weeks How? STATIC Rojas, J. et al. (2017). Providing Reliable Access to Real-Time and Historic Public Transport Data Using Linked Connections. Proceedings of the 16th International Semantic Web Conference on Posters & Demos (ISWC17).
  5. 5. The Tripscore Linked Data Client Architecture 5 SERVER http://..../connections? departureTime=20XX-XX- XXT10:20:00.000Z HTTP Responses 10 min of connections Calculate connections and results Move the server effort to the client Server availability
  6. 6. The Tripscore Linked Data Client ▪ Tripscore front-end ▪ Tripscore back-end ▪ All code is available at: ▪ Demo available at: ▪ Paper available at: What we developed? 6 http://tripscore.eu/ https://github.com/osoc17 https://cef-oasis.github.io/tripscore
  7. 7. The Tripscore Linked Data Client The Tripscore Linked Data client 7
  8. 8. The Tripscore Linked Data Client Next Steps 8 static data static and real-time data ▪ New approaches (like GTFS2LC) to deal with heterogeneity in the transport domain ▪ Improve discoverability of the datasets: TransportDCAT-AP by OASIS team ▪ Transform the open transport data from Madrid (tram, train, buses and metro)
  9. 9. The Tripscore Linked Data Client Open Questions 9 ▪ Do we have to transform all transport data to LC? ▪ Could we propose transformations “on the fly”? ▪ How these “on the fly” transformations would influence the performance? ▪ Are summaries of the data the way to improve the performance? ▪ How does this solution can help to solve the heterogeneity problem of the domain? Will the companies publish their data in this way? ▪ How can we demonstrate the relevance of our metadata profile for starting to include it in the (transport) open data portals?
  10. 10. David Chaves-Fraga, Ontology Engineering Group Universidad Politécnica de Madrid, Spain Julian Rojas, IDLab - Ghent University Pieter-Jan Vandenberghe, IDLab - Ghent University Pieter Colpaert, IDLab - Ghent University Oscar Corcho, Ontology Engineering Group - UPM The tripscore Linked Data client: calculating specific summaries over large time series dchaves@email @dchavesf 22/10/2017 DeSemWeb - ISWC17

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