DOI QR코드

DOI QR Code

A Filter Lining Scheme for Efficient Skyline Computation

  • Kim, Ji-Hyun (Department of Computer Science and Engineering Ewha Womans University) ;
  • Kim, Myung (Department of Computer Science and Engineering Ewha Womans University)
  • Received : 2011.10.10
  • Accepted : 2011.12.13
  • Published : 2011.12.31

Abstract

The skyline of a multidimensional data set is the maximal subset whose elements are not dominated by other elements of the set. Skyline computation is considered to be very useful for a decision making system that deals with multidimensional data analyses. Recently, a great deal of interests has been shown to improve the performance of skyline computation algorithms. In order to speedup, the number of comparisons between data elements should be reduced. In this paper, we propose a filter lining scheme to accomplish such objectives. The scheme divides the multidimensional data space into angle-based partitions, and places a filter for each partition, and then connects them together in order to establish the final filter line. The filter line can be used to eliminate data, that are not part of the skyline, from the original data set in the preprocessing stage. The filter line is adaptively improved during the data scanning stage. In addition, skylines are computed for each remaining data partition, and are then merged to form the final skyline. Our scheme is an improvement of the previously reported simple preprocessing scheme using simple filters. The performance of the scheme is shown by experiments.

Keywords

References

  1. H. T. Kung, F. Luccio, and F. P. Preparata. "On Finding the Maxima of a Set of Vectors," Journal of the ACM, Vol.22, No.4, pp. 469-476, 1975. https://doi.org/10.1145/321906.321910
  2. I. Bartolini, P. Ciaccia, and M. Patella, "SaLSa:Computing the Skyline Without Scanning the Whole Sky," In CIKM 2006, pp. 405-414, Arlington, Virginia, USA, 2006.
  3. Sung Koo, Lee, "A Recommendation System Based on Customer Preference Analysis and Filter Management," Journal of Korea Multimedia Society, Vol.7, No.4, pp. 592-600, 2004.
  4. S. Borzsonyi, D. Kossmann, and K. Stocker, "The Skyline Operator," In ICDE 2001, pp. 421-430, Heidelberg, Germany, 2001.
  5. J. Chomicki, P. Godfrey, J. Gryz, and D. Liang, "Skyline with Presorting," In ICDE 2003, pp. 717-719, India, 2003.
  6. P. Godfrey, R. Shipley, and J. Gryz, "Maximal Vector Computation in Large Data Sets," In VLDB 2005, pp. 229-240, Trondheim, Norway, 2005.
  7. D. Papadias, Y. Tao, G. Fu, and B. Seeger, "Progressive Skyline Computation in Database Systems," ACM Transactions on Database Systems, Vol.30, No.1, pp. 41-82, 2005. https://doi.org/10.1145/1061318.1061320
  8. A. Siddique and Y. Morimoto, "K-Dominant Skyline Computation by Using Sort-Filtering Method," In PAKDD 2009, pp. 839-848, 2009.
  9. K.-L. Tan, P.-K. Eng, and B. C. Ooi, "Efficient Progressive Skyline Computation," In VLDB 2001, pp. 301-310, Roma, Italy, 2001.
  10. A. Vlachou, C. Doulkeridis, and Y. Kotidis, "Angle-based space partitioning for efficient parallel skyline computation," In ACM SIGMOD 2008, pp. 227-238, Vancouver, Canada, 2008.

Cited by

  1. Data Partitioning on MapReduce by Leveraging Data Utility vol.16, pp.5, 2013, https://doi.org/10.9717/kmms.2013.16.5.657