Incremental Aggregation on Multiple Continuous Queries

  • Chun Jin
  • Jaime Carbonell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


Continuously monitoring large-scale aggregates over data streams is important for many stream processing applications, e.g. collaborative intelligence analysis, and presents new challenges to data management systems. The first challenge is to efficiently generate the updated aggregate values and provide the new results to users after new tuples arrive. We implemented an incremental aggregation mechanism for doing so for arbitrary algebraic aggregate functions including user-defined ones by keeping up-to-date finite data summaries. The second challenge is to construct shared query evaluation plans to support large-scale queries effectively. Since multiple query optimization is NP-complete and the queries generally arrive asynchronously, we apply an incremental sharing approach to obtain the shared plans that perform reasonably well. The system is built as a part of ARGUS, a stream processing system atop of a DBMS. The evaluation study shows that our approaches are effective and efficient on typical collaborative intelligence analysis data and queries.


Execution Time Data Stream Algebraic Function Query Plan Continuous Query 
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

  • Chun Jin
    • 1
  • Jaime Carbonell
    • 1
  1. 1.Language Technologies Institute, School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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