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On-Line Discovery of Dense Areas in Spatio-temporal Databases

  • Marios Hadjieleftheriou
  • George Kollios
  • Dimitrios Gunopulos
  • Vassilis J. Tsotras
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2750)

Abstract

Moving object databases have received considerable attention recently. Previous work has concentrated mainly on modeling and indexing problems, as well as query selectivity estimation. Here we introduce a novel problem, that of addressing density-based queries in the spatio-temporal domain. For example: “Find all regions that will contain more than 500 objects, ten minutes from now”. The user may also be interested in finding the time period (interval) that the query answer remains valid. We formally define a new class of density-based queries and give approximate, on-line techniques that answer them efficiently. Typically the threshold above which a region is considered to be dense is part of the query. The difficulty of the problem lies in the fact that the spatial and temporal predicates are not specified by the query. The techniques we introduce find all candidate dense regions at any time in the future. To make them more scalable we subdivide the spatial universe using a grid and limit queries within a pre-specified time horizon. Finally, we validate our approaches with a thorough experimental evaluation.

Keywords

Hash Function Bloom Filter Density Threshold Horizon Length Move Object Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Marios Hadjieleftheriou
    • 1
  • George Kollios
    • 2
  • Dimitrios Gunopulos
    • 1
  • Vassilis J. Tsotras
    • 1
  1. 1.Computer Science DepartmentUniversity of CaliforniaRiverside
  2. 2.Computer Science DepartmentBoston University 

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