Exploiting Cluster Analysis for Constructing Multi-dimensional Histograms on Both Static and Evolving Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3896)


Density-based clusterization techniques are investigated as a basis for constructing histograms in multi-dimensional scenarios, where traditional techniques fail in providing effective data synopses. The main idea is that locating dense and sparse regions can be exploited to partition the data into homogeneous buckets, preventing dense and sparse regions from being summarized into the same aggregate data. The use of clustering techniques to support the histogram construction is investigated in the context of either static and dynamic data, where the use of incremental clustering strategies is mandatory due to the inefficiency of performing the clusterization task from scratch at each data update.


Storage Space Range Query Incremental Approach Core Point Region 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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  1. 1.
    Acharya, S., Poosala, V., Ramaswamy, S.: Selectivity estimation in spatial databases. In: Proc. ACM SIGMOD Conf (1999)Google Scholar
  2. 2.
    Babu, S., Garofalakis, M.N., Rastogi, R.: SPARTAN: A Model-Based Semantic Compression System for Massive Data Tables. In: Proc. ACM SIGMOD Conf. (2001)Google Scholar
  3. 3.
    Bruno, N., Chaudhuri, S., Gravano, L.: STHoles: a multi-dimensional workload aware histogram. In: Proc. ACM SIGMOD Conf. (2001)Google Scholar
  4. 4.
    Chaudhuri, S.: An Overview of Query Optimization in Relational Systems. In: Proc. PODS (1998)Google Scholar
  5. 5.
    Donjerkovic, D., Ioannidis, Y.E., Ramakrishnan, R.: Dynamic Histograms: Capturing Evolving Data Sets. In: Proc. ICDE (2000)Google Scholar
  6. 6.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discorvering clusters in large spatial databases with noise. In: Proc. KDD 1996 (1996)Google Scholar
  7. 7.
    Ester, M., Kriegel, H.P., Wimmer, M., Xu, X.: Incremental clustering for mining in a data warehousing environment. In: Proc. VLDB (1998)Google Scholar
  8. 8.
    Furfaro, F., Mazzeo, G.: Clustering-Based Histograms for Multidimensional Data. In: Proc. DaWaK (2005)Google Scholar
  9. 9.
    Garofalakis, M., Gibbons, P.B.: Wavelet Synopses with Error Guarantees. In: Proc. ACM SIGMOD Conf. (2002)Google Scholar
  10. 10.
    Gibbons, P.B., Matias, Y., Poosala, V.: Fast Incremental Maintenance of Approximate Histograms. In: Proc. VLDB (1997)Google Scholar
  11. 11.
    Guha, S., Indyk, P., Muthukrishnan, M., Strauss, M.: Histogramming Data Streams with Fast Per-Item Processing. In: Proc. ICALP (2002)Google Scholar
  12. 12.
    Gunopulos, D., Kollios, G., Tsotras, V.J., Domeniconi, C.: Selectivity estimators for multidimensional range queries over real attributes. The VLDB Journal 14(2) (April 2005)Google Scholar
  13. 13.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, Chichester (2005)Google Scholar
  14. 14.
    Korn, F., Johnson, T., Jagadish, H.V.: Range Selectivity Estimation for Continuous Attributes. In: Proc. SSDBM (1999)Google Scholar
  15. 15.
    Mamoulis, N., Papadias, D.: Selectivity estimation of complex spatial queries. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, p. 155. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  16. 16.
    Poosala, V., Ioannidis, Y.E.: Selectivity estimation without the attribute value independence assumption. In: Proc. VLDB (1997)Google Scholar
  17. 17.
    Shanmugasundaram, J., Fayyad, U., Bradley, P.S.: Compressed data cubes for OLAP aggregate query approximation on continuous dimensions. In: Proc. KDD 1999 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  1. 1.DEISUniversity of CalabriaRendeItaly

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