Continuously Self-Updating Query Results over Dynamic Heterogeneous Linked Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9678)


Our society is evolving towards massive data consumption from heterogeneous sources, which includes rapidly changing data like public transit delay information. Many applications that depend on dynamic data consumption require highly available server interfaces. Existing interfaces involve substantial costs to publish rapidly changing data with high availability, and are therefore only possible for organisations that can afford such an expensive infrastructure. In my doctoral research, I investigate how to publish and consume real-time and historical Linked Data on a large scale. To reduce server-side costs for making dynamic data publication affordable, I will examine different possibilities to divide query evaluation between servers and clients. This paper discusses the methods I aim to follow together with preliminary results and the steps required to use this solution. An initial prototype achieves significantly lower server processing cost per query, while maintaining reasonable query execution times and client costs. Given these promising results, I feel confident this research direction is a viable solution for offering low-cost dynamic Linked Data interfaces as opposed to the existing high-cost solutions.


Linked Data Triple Pattern Fragments sparql Continuous querying Real-time querying 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Data Science Lab (Ghent University - iMinds)GhentBelgium

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