Scheduling Refresh Queries for Keeping Results from a SPARQL Endpoint Up-to-Date (Short Paper)

  • Magnus Knuth
  • Olaf HartigEmail author
  • Harald Sack
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10033)


Many datasets change over time. As a consequence, long-running applications that cache and repeatedly use query results obtained from a SPARQL endpoint may resubmit the queries regularly to ensure up-to-dateness of the results. While this approach may be feasible if the number of such regular refresh queries is manageable, with an increasing number of applications adopting this approach, the SPARQL endpoint may become overloaded with such refresh queries. A more scalable approach would be to use a middle-ware component at which the applications register their queries and get notified with updated query results once the results have changed. Then, this middle-ware can schedule the repeated execution of the refresh queries without overloading the endpoint. In this paper, we study the problem of scheduling refresh queries for a large number of registered queries by assuming an overload-avoiding upper bound on the length of a regular time slot available for testing refresh queries. We investigate a variety of scheduling strategies and compare them experimentally in terms of time slots needed before they recognize changes and number of changes that they miss.


Time Slot Schedule Strategy Query Result Query Execution SPARQL Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Hasso Plattner InstituteUniversity of PotsdamPotsdamGermany
  2. 2.Department of Computer and Information Science (IDA)Linköping UniversityLinköpingSweden

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