Conference paper Closed Access

Efficient Spatio-temporal RDF Query Processing in Large Dynamic Knowledge Bases

A. Vlachou; C. Doulkeridis; A. Glenis; G. M. Santipantakis; G. A. Vouros


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  <identifier identifierType="DOI">10.5281/zenodo.2392417</identifier>
  <creators>
    <creator>
      <creatorName>A. Vlachou</creatorName>
      <affiliation>University of Piraeus</affiliation>
    </creator>
    <creator>
      <creatorName>C. Doulkeridis</creatorName>
      <affiliation>University of Piraeus</affiliation>
    </creator>
    <creator>
      <creatorName>A. Glenis</creatorName>
      <affiliation>University of Piraeus</affiliation>
    </creator>
    <creator>
      <creatorName>G. M. Santipantakis</creatorName>
      <affiliation>University of Piraeus</affiliation>
    </creator>
    <creator>
      <creatorName>G. A. Vouros</creatorName>
      <affiliation>University of Piraeus</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Efficient Spatio-temporal RDF Query Processing in Large Dynamic Knowledge Bases</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Spatio-temporal RDF, query processing, encoding</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-04-08</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2392417</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2392416</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/h2020_datacron</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/closedAccess">Closed Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;An ever-increasing number of real-life applications produce spatio-temporal data that record the position of moving objects (persons, cars, vessels, aircrafts, etc.). In order to provide integrated views with other relevant data sources (e.g., weather, vessel databases, etc.), this data is represented in RDF and stored in knowledge bases with the following notable features: (a) the data is dynamic, since new spatio-temporal data objects are recorded every second, and (b) the size of the data is vast and can easily lead to scalability issues. As a result, this raises the need for efficient management of large-scale, dynamic, spatio-temporal RDF data. In this paper, we propose boosting the performance of spatio-temporal RDF queries by compressing the spatio-temporal information of each RDF entity into a unique integer value. We exploit this encoding in a filter-and-refine framework for processing of spatio-temporal RDF data efficiently. By means of an extensive evaluation on real-life data sets, we demonstrate the merits of our framework.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/687591/">687591</awardNumber>
      <awardTitle>Big Data Analytics for Time Critical Mobility Forecasting</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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