Temporal Aggregation over Data Streams Using Multiple Granularities

  • Donghui Zhang
  • Dimitrios Gunopulos
  • Vassilis J. Tsotras
  • Bernhard Seeger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2287)


Temporal aggregation is an important but costly operation for applications that maintain time-evolving data (data warehouses, temporal databases, etc.). In this paper we examine the problem of computing temporal aggregates over data streams. Such aggregates are maintained using multiple levels of temporal granularities: older data is aggregated using coarser granularities while more recent data is aggregated with finer detail. We present specialized indexing schemes for dynamically and progressively maintaining temporal aggregates. Moreover, these schemes can be parameterized. The levels of granularity as well as their corresponding index sizes (or validity lengths) can be dynamically adjusted. This provides a useful trade-o. between aggregation detail and storage space. Analytical and experimental results show the efficiency of the proposed structures. Moreover, we discuss how the indexing schemes can be extended to solve the more general range temporal and spatiotemporal aggregation problems.


Data Stream Data Warehouse Query Time Index Size Temporal Aggregation 
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 2002

Authors and Affiliations

  • Donghui Zhang
    • 1
  • Dimitrios Gunopulos
    • 1
  • Vassilis J. Tsotras
    • 1
  • Bernhard Seeger
    • 2
  1. 1.Computer Science DepartmentUniversity of CaliforniaRiverside
  2. 2.Fachbereich Mathematik & InformatikPhilipps Universität MarburgGermany

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