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Computing and Summarizing the Negative Skycube

Published:24 October 2016Publication History

ABSTRACT

Given a table T with a set of dimensions D, the skycube of T is the union of all skylines obtained by considering each of the subsets of D (subspaces). The number of these skylines is exponential w.r.t D. To make the skycube practically useful, two lines of research have been pursued so far: the first one aims to propose efficient algorithms for computing it and the second one considers either that the skycube is too large to be computed in a reasonable time or it requires too much memory space to be stored. They therefore propose skycube summarization techniques to reduce time and space consumption. Intuitively, previous efforts have been devoted to compute or summarize the following information: ``for every tuple t, list the skylines where t belongs to". In this paper, we consider the complementary statement, i.e., ``for every tuple t, list the skylines where t does not belong to". This is what we call the negative skycube. Despite the apparent equivalence between these two statements, our analysis and extensive experiments show that these two points of views do not lead to the same behavior of the related algorithms. More specifically, our proposal shows that (i) the negative summary can be obtained much faster than state of the art techniques for positive summaries, (ii) in general, it consumes less space, (iii) skyline queries evaluation using this summary are much faster, (iv) the positive skycube can be obtained much more rapidly than state of the art algorithms, and (v) it can be used for a larger class of queries, namely k-domination skylines.

References

  1. I. Bartolini, P. Ciaccia, and M. Patella. Efficient sort-based skyline evaluation. ACM Trans. Database Syst., 33(4), 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. K. S. Bøgh, S. Chester, D. Sidlauskas, and I. Assent. Hashcube: A data structure for space and query efficient skycube compression. In Proceedings of CIKM conference, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. Börzsönyi, D. Kossmann, and K. Stocker. The skyline operator. In Proc. of ICDE conf., 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. C. Y. Chan, H. V. Jagadish, K. Tan, A. K. H. Tung, and Z. Zhang. Finding k-dominant skylines in high dimensional space. In Proceedings of SIGMOD International Conference, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. C. Y. Chan, H. V. Jagadish, K. Tan, A. K. H. Tung, and Z. Zhang. On high dimensional skylines. In Proceedings of EDBT conference, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. S. Chester, D. Sidlauskas, I. Assent, and K. S. Bøgh. Scalable parallelization of skyline computation for multi-core processors. In Proceedings of ICDE conference, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  7. J. Chomicki, P. Godfrey, J. Gryz, and D. Liang. Skyline with presorting. In Proc. of ICDE conference, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  8. V. Chvatal. A Greedy Heuristic for the Set-Covering Problem. Mathematics of Operations Research, 4(3):233--235, 1979. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. L. Dong, X. Cui, Z. Wang, and S. Cheng. Finding k-dominant skyline cube based on sharing-strategy. In Proceedings of FSKD conference, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  10. B. Groz and T. Milo. Skyline queries with noisy comparisons. In Proceedings of PODS conference, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. J. Lee and S. won Hwang. BSkyTree: scalable skyline computation using a balanced pivot selection. In Proc. of EDBT conf., 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. J. Lee and S. won Hwang. Toward efficient multidimensional subspace skyline computation. VLDB Journal, 23(1):129--145, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. X. Lin, Y. Yuan, Q. Zhang, and Y. Zhang. Selecting stars: The k most representative skyline operator. In Proceedings of ICDE conference, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  14. J. Liu, L. Xiong, J. Pei, J. Luo, and H. Zhang. Finding pareto optimal groups: Group-based skyline. PVLDB, 8(13):2086--2097, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. S. Md. Anisuzzaman and M. Yasuhiko. k-dominant and extended k-dominant skyline computation by using statistics. International Journal on Computer Science and Engineering, (5):1934, 2010.Google ScholarGoogle Scholar
  16. M. D. Morse, J. M. Patel, and H. V. Jagadish. Efficient skyline computation over low-cardinality domains. In Proceedings of VLDB conf., 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. D. Papadias, Y. Tao, G. Fu, and B. Seeger. Progressive skyline computation in database systems. ACM Trans. Database Syst., 30(1), 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. J. Pei, B. Jiang, X. Lin, and Y. Yuan. Probabilistic skylines on uncertain data. In Proceedings of the 33rd International Conference on Very Large Data Bases, University of Vienna, Austria, September 23-27, 2007, pages 15--26, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. J. Pei, W. Jin, M. Ester, and Y. Tao. Catching the best views of skyline: A semantic approach based on decisive subspaces. In Proc. of VLDB conf., 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. J. Pei, Y. Yuan, X. Lin, W. Jin, M. Ester, Q. Liu, W. Wang, Y. Tao, J. X. Yu, and Q. Zhang. Towards multidimensional subspace skyline analysis. ACM TODS, 31(4):1335--1381, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. C. Raässi, J. Pei, and T. Kister. Computing closed skycubes. Proc. of VLDB conf., 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. H. Shang and M. Kitsuregawa. Skyline operator on anti-correlated distributions. PVLDB, 6(9):649--660, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. M. Siddique and Y. Morimoto. Efficient k-dominant skyline computation for high dimensional space with domination power index. Journal of Computers, 7(3):608--615, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  24. M. A. Siddique and Y. Morimoto. K-dominant skyline computation by using sort-filtering method. In Proceedings of PAKDD conference, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. T. Xia, D. Zhang, Z. Fang, C. X. Chen, and J. Wang. Online subspace skyline query processing using the compressed skycube. ACM TODS, 37(2), 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library

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        cover image ACM Conferences
        CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
        October 2016
        2566 pages
        ISBN:9781450340731
        DOI:10.1145/2983323

        Copyright © 2016 ACM

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        Publication History

        • Published: 24 October 2016

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