Query processing of massive trajectory data based on MapReduce

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69 Scopus citations

Abstract

With the development of positioning technologies and the boosting deployment of inexpensive location-aware sensors, large volumes of trajectory data have emerged. However, efficient and scalable query processing over trajectory data remains a big challenge. We explore a new approach to this target in this paper, presenting a new framework for query processing over trajectory data based on MapReduce. Traditional trajectory data partitioning, indexing, and query processing technologies are extended so that they may fully utilize the highly parallel processing power of large-scale clusters. We also show that the append-only scheme of MapReduce storage model can be a nice base for handling updates of moving objects. Preliminary experiments show that this framework scales well in terms of the size of trajectory data set. It is also discussed the limitation of traditional trajectory data processing techniques and our future research directions.

Original languageEnglish
Title of host publication1st International Workshop on Cloud Data Management, CloudDB 2009, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009
Pages9-16
Number of pages8
DOIs
StatePublished - 2009
Event1st International Workshop on Cloud Data Management, CloudDB 2009, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, China
Duration: 2 Nov 20096 Nov 2009

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference1st International Workshop on Cloud Data Management, CloudDB 2009, Co-located with the 18th ACM International Conference on Information and Knowledge Management, CIKM 2009
Country/TerritoryChina
CityHong Kong
Period2/11/096/11/09

Keywords

  • Indexing
  • Location based services
  • MapReduce
  • Moving objects
  • Spatio-temporal queries

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