Temporal and spatio-temporal aggregations over data streams using multiple time granularities

  • Authors:

  • Donghui Zhang;Dimitrios Gunopulos;Vassilis J. Tsotras;Bernhard Seeger

  • Affiliations:

  • College of Computer Science, Northeastern University, 360 Huntington Avenue #161CN, Boston, MA;Computer Science Department, University of California, Riverside, CA;Computer Science Department, University of California, Riverside, CA;Fachbereich Mathematik & Informatik, Philipps Universitäät, Marburg, Germany

  • Venue:
  • Information Systems - Special issue: Best papers from EDBT 2002
  • Year:
  • 2003

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Abstract

Temporal and spatio-temporal aggregations are important but costly operations for applications that maintain time-evolving data (data warehouses, temporal databases, etc.). In this paper, we examine the problem of computing such aggregates over data streams. The 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 and spatio-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-off between aggregation detail and storage space. Analytical and experimental results show the efficiency of the proposed structures. We first address the temporal aggregation problem. A general framework of aggregating at multiple time granularities is then proposed. Finally, we show how to utilize this framework to solve the range-temporal and spatio-temporal aggregation problems.