Efficient Computation of Multi-feature Data Cubes

  • Shichao Zhang
  • Rifeng Wang
  • Yanping Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4092)


A Multi-Feature Cube (MF-Cube) query is a complex-data-mining query based on data cubes, which computes the dependent complex aggregates at multiple granularities. Existing computations designed for simple data cube queries can be used to compute distributive and algebraic MF-Cubes queries. In this paper we propose an efficient computation of holistic MF-Cubes queries. This method computes holistic MF-Cubes with PDAP (Part Distributive Aggregate Property). The efficiency is gained by using dynamic subset data selection strategy (Iceberg query technique) to reduce the size of materialized data cube. Also for efficiency, this approach adopts the chunk-based caching technique to reuse the output of previous queries. We experimentally evaluate our algorithm using synthetic and real-world datasets, and demonstrate that our approach delivers up to about twice the performance of traditional computations.


Decision Support System Efficient Computation Complex Query Data Cube Aggregate Function 
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  1. 1.
    Ross, K.A., Srivastava, D., Chatziantoniou, D.: Complex Aggregation at Multiple Granularities. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 263–277. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  2. 2.
    Ross, K., Srivastava, D.: Fast computation of sparse datacubes. In: Proc. Int. Conf. Very Large Data Bases, August 1997, Athens, Greece, pp. 116–125 (1997)Google Scholar
  3. 3.
    Zhao, Y., Deshpande, P.M., Naughton, J.F.: An Array-Based Algorithm for Simultaneous Multidimensional Aggregates. In: Proc. of ACM SIGMOD, pp. 159–170 (1997)Google Scholar
  4. 4.
    Beyer, K.S., Ramakrishnan, R.: Bottom-Up Computation of Sparse and Iceberg Cubes. In: Proc. Of ACM SIGMOD, pp. 359–370 (1999)Google Scholar
  5. 5.
    Agarwal, S., Agrawal, R., Deshpande, P.M., Gupta, A., Naughton, J.F., Ramakrishnan, R., Sarawagi, S.: On the Computation of Multidimensional Aggregates. In: Proc. Int’ Conf. Very Large Data Bases, pp. 506–521 (1996)Google Scholar
  6. 6.
    Gray, A., Bosworth, A., Layman, A., Pirahesh, H.: Datacube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals. In: Proc. of the IEEE ICDE, pp. 152–159 (1996)Google Scholar
  7. 7.
    Fang, M., Shivakumar, N., Garcia-Molina, H., Motwani, R., Ullman, J.D.: Computing iceberg queries efficiently. In: Proc. of 24th VLDB Conf., New York, pp. 299–310 (1998)Google Scholar
  8. 8.
    Han, J.P., Dong, G., Wang, K.: Efficient Computation of Iceberg Cubes with Complex Measures. In: Proc. ACM-SIGMOD Int’l Conf. Management of Data, pp. 1–12 (2001)Google Scholar
  9. 9.
    Deshpande, P.M., Ramasamy, K., Shukla, A.: Caching Multidimensional Queries Using Chunks. In: Proc. ACM SIGMOD, pp. 259–270 (1998)Google Scholar
  10. 10.
    Dong, G., Han, J., Joyce, M.W.L.: Mining Constrained Gradients in Large Databases. In: IEEE TKDE (2003)Google Scholar
  11. 11.
    Hahn, C., Warren, S., London, J.: Edited synoptic cloud reports from ships and land stations over the globe (1996), Available on:

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shichao Zhang
    • 1
    • 2
  • Rifeng Wang
    • 1
  • Yanping Guo
    • 1
  1. 1.Department of Computer ScienceGuangxi Normal UniversityGuilinChina
  2. 2.Faculty of Information TechnologyUniversity of TechnologySydneyAustralia

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