P2EST: Parallelization philosophies for evaluating spatio-temporal queries

Xiling Sun, Anan Yaagoub, Goce Trajcevski, Peter I Scheuermann, Hao Chen, Abhinav Kachhwaha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This work considers the impact of different contexts when attempting to exploit parallelization approaches for processing continuous spatio-temporal queries. More specifically, we are interested in various trade-off aspects that may arise due to differences of the computing environments like, for example, multicore vs. cloud. Algorithmic solutions for parallel processing of spatio-temporal queries cater to splitting the load among units - be it based on the data or the query (or both) - relying to a bigger or lesser degree on a certain set of features of a given environment. We postulate that incorporating the service-features should be coupled with the algorithms/heuristics for processing particular queries, in addition to the volume of the data. We present the current version of the implementation of our P2EST system and analyze the execution of different heuristics for parallel processing of spatio-temporal range queries.

Original languageEnglish (US)
Title of host publicationProceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2013
PublisherAssociation for Computing Machinery
Pages47-54
Number of pages8
ISBN (Print)9781450325349
DOIs
StatePublished - Jan 1 2013
Event2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2013 - Orlando, FL, United States
Duration: Nov 4 2013Nov 4 2013

Publication series

NameProceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2013

Other

Other2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2013
CountryUnited States
CityOrlando, FL
Period11/4/1311/4/13

Keywords

  • cloud
  • multi-core
  • spatio-temporal queries

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Information Systems

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  • Cite this

    Sun, X., Yaagoub, A., Trajcevski, G., Scheuermann, P. I., Chen, H., & Kachhwaha, A. (2013). P2EST: Parallelization philosophies for evaluating spatio-temporal queries. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2013 (pp. 47-54). (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2013). Association for Computing Machinery. https://doi.org/10.1145/2534921.2534929