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Answering Range-Aggregate Queries over Objects Generating Data Streams

  • Marcin Gorawski
  • Rafal Malczok
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5982)

Abstract

Nowadays computer systems process various types of data such as images, videos, maps, data streams to name a few. In this paper we focus on a problem of answering range-aggregate queries over objects generating data streams. Our motivating example is a network of meters monitoring utilities consumption and continuously reporting the readings to central gathering points. An answer to a range-aggregate query is a merged stream of aggregates allowing analyses of utilities consumption in a given region. In order to calculate the answer we integrate MAL (Materialized Aggregates List) with spatial aggregating index, e.g. aR-Tree. The result we obtain is a spatial aggregating index with functionality of answering range queries over objects generating data streams. The index is embedded in an experimental stream data warehouse system implemented in Java. The implementation provided us with the possibility of presenting the index operation and also carrying out a number of tests.

Keywords

Data Warehouse Index Node Query Region Utility Consumption Stream Data Category 
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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References

  1. 1.
    Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: Popa [6], pp. 1–16Google Scholar
  2. 2.
    Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The R*-Tree: An efficient and robust access method for points and rectangles. In: Garcia-Molina, H., Jagadish, H.V. (eds.) SIGMOD Conference, pp. 322–331. ACM Press, New York (1990)Google Scholar
  3. 3.
    Bonnet, P., Gehrke, J., Seshadri, P.: Towards sensor database systems. In: Tan, K.-L., Franklin, M.J., Lui, J.C.-S. (eds.) MDM 2001. LNCS, vol. 1987, pp. 3–14. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. 4.
    Gorawski, M., Malczok, R.: On efficient storing and processing of long aggregate lists. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2005. LNCS, vol. 3589, pp. 190–199. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Papadias, D., Kalnis, P., Zhang, J., Tao, Y.: Efficient OLAP operations in spatial data warehouses. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 443–459. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  6. 6.
    Popa, L. (ed.): Proceedings of the Twenty-first ACM SIGACT-SIGMOD- SIGART Symposium on Principles of Database Systems, Madison, Wisconsin, USA, June 3-5. ACM, New York (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marcin Gorawski
    • 1
  • Rafal Malczok
    • 1
  1. 1.Institute of Computer ScienceSilesian University of TechnologyGliwicePoland

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