Ruben Verborgh’s data

Matches in Ruben’s data for { <https://dx.doi.org/10.3233/SW-223116> ?p ?o }

• SW-223116 author me.
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• SW-223116 about public_transport.
• SW-223116 about Linked_Connections.
• SW-223116 about route_planning.
• SW-223116 about Web_API.
• SW-223116 about World_Wide_Web.
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• SW-223116 title "Publishing Public Transport Data on the Web with the Linked Connections Framework".
• SW-223116 isPartOf semantic_web_journal.
• SW-223116 name "Publishing Public Transport Data on the Web with the Linked Connections Framework".
• SW-223116 label "Publishing Public Transport Data on the Web with the Linked Connections Framework".
• SW-223116 name "Publishing Public Transport Data on the Web with the Linked Connections Framework".
• SW-223116 topic public_transport.
• SW-223116 topic Linked_Connections.
• SW-223116 topic route_planning.
• SW-223116 topic Web_API.
• SW-223116 topic World_Wide_Web.
• SW-223116 subject public_transport.
• SW-223116 subject Linked_Connections.
• SW-223116 subject route_planning.
• SW-223116 subject Web_API.
• SW-223116 subject World_Wide_Web.
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• SW-223116 topic public_transport.
• SW-223116 topic Linked_Connections.
• SW-223116 topic route_planning.
• SW-223116 topic Web_API.
• SW-223116 topic World_Wide_Web.
• SW-223116 abstract "Publishing transport data on the Web for consumption by others poses several challenges for data publishers. In addition to planned schedules, access to live schedule updates (e.g. delays or cancellations) and historical data is fundamental to enable reliable applications and to support machine learning use cases. However publishing such dynamic data further increases the computational burden for data publishers, resulting in often unavailable historical data and live schedule updates for most public transport networks. In this paper we apply and extend the current Linked Connections approach for static data to also support cost-efficient live and historical public transport data publishing on the Web. Our contributions include (i) a reference specification and system architecture to support cost-efficient publishing of dynamic public transport schedules and historical data; (ii) empirical evaluations on route planning query performance based on data fragmentation size, publishing costs and a comparison with a traditional route planning engine such as OpenTripPlanner; (iii) an analysis of potential correlations of query performance with particular public transport network characteristics such as size, average degree, density, clustering coefficient and average connection duration. Results confirm that fragmentation size influences route planning query performance and converges on an optimal fragment size per network. Size (stops), density and connection duration also show correlation with route planning query performance. Our approach proves to be more cost-efficient and in some cases outperforms OpenTripPlanner when supporting the earliest arrival time route planning use case. Moreover, the cost of publishing live and historical schedules remains in the same order of magnitude for server-side resources compared to publishing planned schedules only. Yet, further optimizations are needed for larger networks (> 1000 stops) to be useful in practice. Additional dataset fragmentation strategies (e.g. geospatial) may be studied for designing more scalable and performant Web API s that adapt to particular use cases, not only limited to the public transport domain.".
• SW-223116 datePublished "2022".
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