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  1. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSpatial '13)
  2. $P^{2}EST:$ parallelization philosophies for evaluating spatio-temporal queries
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$P^{2}EST:$ parallelization philosophies for evaluating spatio-temporal queries
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$P^{2}EST:$ parallelization philosophies for evaluating spatio-temporal queries

Content Provider ACM Digital Library
Author Sun, Xiling Yaagoub, Anan Trajcevski, Goce Scheuermann, Peter Kachhwaha, Abhinav Chen, Hao
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 $P^{2}EST$ system and analyze the execution of different heuristics for parallel processing of spatio-temporal range queries.
Starting Page 47
Ending Page 54
Page Count 8
File Format PDF
ISBN 9781450325349
DOI 10.1145/2534921.2534929
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-11-04
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Spatio-temporal queries Multi-core Cloud
Content Type Text
Resource Type Article
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