Scalable Continuous Query Processing and Moving Object Indexing in Spatio-temporal Databases

  • Xiaopeng Xiong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4254)


Spatio-temporal database systems aim to answer continuous spatio-temporal queries issued over moving objects. In many scenarios such as in a wide area, the number of outstanding queries and the number of moving objects are so large that a server fails to process queries promptly. In our work, we aim to develop scalable techniques for spatio-temporal database systems. We focus on two aspects of spatio-temporal database systems: 1) the query processing algorithms for a large set of concurrent queries, and 2) the underlying indexing structures for constantly moving objects. For continuous query processing, we explore the techniques of Incremental Evaluation and Shared Execution, especially to k-nearest-neighbor queries. For moving object indexing, we utilize Update Memos to support frequent updates efficiently in spatial indexes such as R-trees. In this paper, we first identify the challenges towards scalable spatio-temporal databases, then review the current contributions we have achieved so far and discuss future research directions.


Query Processing Continuous Query Query Answer Neighbor Query Query Processing Algorithm 
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 2006

Authors and Affiliations

  • Xiaopeng Xiong
    • 1
  1. 1.Department of Computer SciencePurdue University 

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