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

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Advances in Database Technology - EDBT 2006 (EDBT 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3896))

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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.

This work was supported by a grant from the Italian Research Project FIRB “ – Enabling ICT Platforms for Distributed High-Performance Computational Grids”, funded by MIUR and coordinated by the National Research Council (CNR).

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  1. Acharya, S., Poosala, V., Ramaswamy, S.: Selectivity estimation in spatial databases. In: Proc. ACM SIGMOD Conf (1999)

    Google Scholar 

  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. Bruno, N., Chaudhuri, S., Gravano, L.: STHoles: a multi-dimensional workload aware histogram. In: Proc. ACM SIGMOD Conf. (2001)

    Google Scholar 

  4. Chaudhuri, S.: An Overview of Query Optimization in Relational Systems. In: Proc. PODS (1998)

    Google Scholar 

  5. Donjerkovic, D., Ioannidis, Y.E., Ramakrishnan, R.: Dynamic Histograms: Capturing Evolving Data Sets. In: Proc. ICDE (2000)

    Google Scholar 

  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. 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. Furfaro, F., Mazzeo, G.: Clustering-Based Histograms for Multidimensional Data. In: Proc. DaWaK (2005)

    Google Scholar 

  9. Garofalakis, M., Gibbons, P.B.: Wavelet Synopses with Error Guarantees. In: Proc. ACM SIGMOD Conf. (2002)

    Google Scholar 

  10. Gibbons, P.B., Matias, Y., Poosala, V.: Fast Incremental Maintenance of Approximate Histograms. In: Proc. VLDB (1997)

    Google Scholar 

  11. Guha, S., Indyk, P., Muthukrishnan, M., Strauss, M.: Histogramming Data Streams with Fast Per-Item Processing. In: Proc. ICALP (2002)

    Google Scholar 

  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. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, Chichester (2005)

    Google Scholar 

  14. Korn, F., Johnson, T., Jagadish, H.V.: Range Selectivity Estimation for Continuous Attributes. In: Proc. SSDBM (1999)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  16. Poosala, V., Ioannidis, Y.E.: Selectivity estimation without the attribute value independence assumption. In: Proc. VLDB (1997)

    Google Scholar 

  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 

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Furfaro, F., Mazzeo, G.M., Sirangelo, C. (2006). Exploiting Cluster Analysis for Constructing Multi-dimensional Histograms on Both Static and Evolving Data. In: Ioannidis, Y., et al. Advances in Database Technology - EDBT 2006. EDBT 2006. Lecture Notes in Computer Science, vol 3896. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32960-2

  • Online ISBN: 978-3-540-32961-9

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