Conference paper Open Access

BigCAB: Distributed Hot Spot Analysis over Big Spatio-temporal Data using Apache Spark (GIS Cup)

Panagiotis Nikitopoulos; Aris-Iakovos Paraskevopoulos; Christos Doulkeridis; Nikos Pelekis; Yannis Theodoridis

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  <identifier identifierType="DOI">10.5281/zenodo.814792</identifier>
      <creatorName>Panagiotis Nikitopoulos</creatorName>
      <creatorName>Aris-Iakovos Paraskevopoulos</creatorName>
      <creatorName>Christos Doulkeridis</creatorName>
      <creatorName>Nikos Pelekis</creatorName>
      <creatorName>Yannis Theodoridis</creatorName>
    <title>BigCAB: Distributed Hot Spot Analysis over Big Spatio-temporal Data using Apache Spark (GIS Cup)</title>
    <date dateType="Issued">2016-11-02</date>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.814791</relatedIdentifier>
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    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;Hot spot analysis is the problem of identifying statistically significant spatial clusters from an underlying data set. In this paper, we target the problem of hot spot analysis of massive spatio-temporal data, which raises the need for a parallel and scalable solution that operates on data distributed over a set of nodes. We propose an algorithm, called BigCAB, implemented in Spark, that solves the problem in a parallel and scalable way. Our experiments on real data representing taxi trips demonstrate both the efficiency as well as the nice scaling properties of our algorithm.&lt;/p&gt;</description>
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