research-article

Online subspace skyline query processing using the compressed skycube

Authors:

Jie WangAuthors Info & Claims

ACM Transactions on Database Systems (TODS), Volume 37, Issue 2

Article No.: 15, Pages 1 - 36

https://doi.org/10.1145/2188349.2188357

Published: 04 June 2012 Publication History

Abstract

The skyline query can help identify the “best” objects in a multi-attribute dataset. During the past decade, this query has received considerable attention in the database research community. Most research focused on computing the “skyline” of a dataset, or the set of “skyline objects” that are not dominated by any other object. Such algorithms are not appropriate in an online system, which should respond in real time to skyline query requests with arbitrary subsets of the attributes (also called subspaces). To guarantee real-time response, an online system should precompute the skylines for all subspaces, and look up a skyline upon query. Unfortunately, because the number of subspaces is exponential to the number of attributes, such pre computation has very expensive storage cost and update cost. We propose the Compressed SkyCube (CSC) that is much more compact, yet can still return the skyline of any subspace without consulting the base table. The CSC therefore combines the advantage of precomputation in that it can respond to queries in real time, and the advantage of no-precomputation in that it has efficient space cost and update cost. This article presents the CSC data structures, the CSC query algorithm, the CSC update algorithm, and the CSC initial computation scheme. A solution to extend to high-dimensional data is also proposed.

References

[1]

Agarwal, S., Agrawal, R., Deshpande, P., Gupta, A., Naughton, J., Ramakrishnan, R., and Sarawagi, S. 1996. On the computation of multidimensional aggregates. In Proceedings of the International Conference on Very Large Data Bases (VLDB). 506--521.

Digital Library

[2]

Balke, W.-T., Güntzer, U., and Zheng, J. X. 2004. Efficient distributed skylining for web information systems. In Proceedings of the International Conference on Extending Database Technology (EDBT). 256--273.

[3]

Barndorff-Nielsen, O. and Sobel, M. 1966. On the distribution of the number of admissable points in a vector random sample. Theory Probab. Appl. 11, 2, 249--269.

[4]

Bentley, J. L., Clarkson, K. L., and Levine, D. B. 1990. Fast linear expected-time algorithms for computing maxima and convex hulls. In Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). 179--187.

Digital Library

[5]

Bentley, J. L., Kung, H. T., Schkolnick, M., and Thompson, C. D. 1978. On the average number of maxima in a set of vectors and applications. J. Assoc. Comput. Mach. 25, 4, 536--543.

Digital Library

[6]

Börzsönyi, S., Kossmann, D., and Stocker, K. 2001. The skyline operator. In Proceedings of the International Conference on Data Engineering (ICDE). 421--430.

Digital Library

[7]

Buchta, C. 1989. On the average number of maxima in a set of vectors. Inf. Process. Lett. 33, 2, 63--65.

Digital Library

[8]

Chan, C. Y., Eng, P.-K., and Tan, K.-L. 2005. Stratified computation of skylines with partially-ordered domains. In Proceedings of the ACM/SIGMOD Annual Conference on Management of Data (SIGMOD). 203--214.

Digital Library

[9]

Chan, C. Y., Jagadish, H. V., Tan, K.-L., Tung, A. K. H., and Zhang, Z. 2006a. Finding k-dominant skylines in high dimensional space. In Proceedings of the ACM/SIGMOD Annual Conference on Management of Data (SIGMOD). 503--514.

Digital Library

[10]

Chan, C. Y., Jagadish, H. V., Tan, K.-L., Tung, A. K. H., and Zhang, Z. 2006b. On high dimensional skylines. In Proceedings of the International Conference on Extending Database Technology (EDBT). 478--495.

Digital Library

[11]

Chaudhuri, S., Dalvi, N. N., and Kaushik, R. 2006. Robust cardinality and cost estimation for skyline operator. In Proceedings of the International Conference on Data Engineering (ICDE). 64.

Digital Library

[12]

Chomicki, J. 2002. Querying with intrinsic preferences. In Proceedings of the International Conference on Extending Database Technology (EDBT). 34--51.

Digital Library

[13]

Chomicki, J., Godfrey, P., Gryz, J., and Liang, D. 2003. Skyline with presorting. In Proceedings of the International Conference on Data Engineering (ICDE). 717--816.

[14]

Dellis, E., Vlachou, A., Vladimirskiy, I., Seeger, B., and Theodoridis, Y. 2006. Constrained subspace skyline computation. In Proceedings of the International Conference on Information and Knowledge Management (CIKM).

Digital Library

[15]

Fang, Z., Wang, J., and Zhang, D. 2009. Workload-driven compressed skycube queries in wireless applications. In Proceedings of the International Conference on Wirelss Algorithms, Systems, and Applications (WASA). 244--253.

Digital Library

[16]

Godfrey, P. 2004. Skyline cardinality for relational processing. In Proceedings of the International Symposium on Foundations of Information and Knowledge Systems (FoIKS). 78--97.

[17]

Godfrey, P., Shipley, R., and Gryz, J. 2005. Maximal vector computation in large data sets. In Proceedings of the International Conference on Very Large Data Bases (VLDB). 229--240.

Digital Library

[18]

Hose, K., Lemke, C., and Sattler, K.-U. 2006. Processing relaxed skylines in pdms using distributed data summaries. In Proceedings of the International Conference on Information and Knowledge Management (CIKM).

Digital Library

[19]

Hristidis, V., Koudas, N., and Papakonstantinou, Y. 2001. PREFER: A system for the efficient execution of multi-parametric ranked queries. In Proceedings of the ACM/SIGMOD Annual Conference on Management of Data (SIGMOD). 259--270.

Digital Library

[20]

Huang, X. and Jensen, C. S. 2004. In-route skyline querying for location-based services. In Proceedings of the International Symposium on Web and Wireless Geographical Information Systems (W2GIS). 120--135.

Digital Library

[21]

Huang, Z., Jensen, C. S., Lu, H., and Ooi, B. C. 2006a. Skyline queries against mobile lightweight devices in manets. In Proceedings of the International Conference on Data Engineering (ICDE). 66.

Digital Library

[22]

Huang, Z., Lu, H., Ooi, B. C., and Tung, A. K. H. 2006b. Continuous skyline queries for moving objects. IEEE Trans. Knowl. Data Engin. 18, 12, 1645--1658.

Digital Library

[23]

Jin, W., Han, J., and Ester, M. 2004. Mining thick skylines over large databases. In Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery (PKDD). 255--266.

Digital Library

[24]

Jin, W., Morse, M. D., Patel, J. M., Ester, M., and Hu, Z. 2010. Evaluating skylines in the presence of equijoins. In Proceedings of the International Conference on Data Engineering (ICDE). 249--260.

[25]

Kossmann, D., Ramsak, F., and Rost, S. 2002. Shooting stars in the sky: an online algorithm for skyline queries. In Proceedings of the International Conference on Very Large Data Bases (VLDB). 275--286.

Digital Library

[26]

Kung, H. T., Luccio, F., and Preparata, F. P. 1975. On finding the maxima of a set of vectors. J. Assoc. Comput. Mach. 22, 4, 469--476.

Digital Library

[27]

Lee, J. and Hwang, S. 2011. Qskycube: efficient skycube computation using point-based space partitioning. In Proceedings of the International Conference on Very Large Data Bases (VLDB). 185--196.

Digital Library

[28]

Lin, X., Yuan, Y., Wang, W., and Lu, H. 2005. Stabbing the sky: efficient skyline computation over sliding windows. In Proceedings of the International Conference on Data Engineering (ICDE). 502--513.

Digital Library

[29]

Lin, X., Yuan, Y., Zhang, Q., and Zhang, Y. 2007. Selecting stars: The k most representative skyline operator. In Proceedings of the International Conference on Data Engineering (ICDE).

[30]

Matousek, J. 1991. Computing dominances in Eⁿ. Inf. Process. Lett. 38, 5, 277--278.

Digital Library

[31]

Morse, M. D., Patel, J. M., and Grosky, W. I. 2006. Efficient continuous skyline computation. In Proceedings of the International Conference on Data Engineering (ICDE). 108.

Digital Library

[32]

Papadias, D., Tao, Y., Fu, G., and Seeger, B. 2003. An optimal and progressive algorithm for skyline queries. In Proceedings of the ACM/SIGMOD Annual Conference on Management of Data (SIGMOD). 467--478.

Digital Library

[33]

Pei, J., Fu, A. W.-C., Lin, X., and Wang, H. 2007. Computing compressed multidimensional skyline cubes efficiently. In Proceedings of the International Conference on Data Engineering (ICDE).

[34]

Pei, J., Jin, W., Ester, M., and Tao, Y. 2005. Catching the best views of skyline: a semantic approach based on decisive subspaces. In Proceedings of the International Conference on Very Large Data Bases (VLDB). 253--264.

Digital Library

[35]

Pei, J., Yuan, Y., Lin, X., Jin, W., Ester, M., Liu, Q., Wang, W., Tao, Y., Yu, J. X., and Zhang, Q. 2006. Towards multidimensional subspace skyline analysis. ACM Trans. Datab. Syst. 31, 4, 1335--1381.

Digital Library

[36]

Preparata, F. and Shamos, M. 1985. Computational Geometry: An Introduction. Springer.

Digital Library

[37]

Raïssi, C., Pei, J., and Kister, T. 2010. Computing closed skycubes. Proc. VLDB Endow. 3, 838--847.

Digital Library

[38]

Rhee, C. J., Dhall, S. K., and Lakshmivarahan, S. 1995. The minimum weight dominating set problem for permutation graphs is in nc. J. Parallel Distrib. Comput. 28, 2, 109--112.

Digital Library

[39]

Sharifzadeh, M. and Shahabi, C. 2006. The spatial skyline queries. In Proceedings of the International Conference on Very Large Data Bases (VLDB).

Digital Library

[40]

Sheng, C. and Tao, Y. 2011. On finding skylines in external memory. In Proceedings of the ACM International Symposium on Principles of Database Systems (PODS). 107--116.

Digital Library

[41]

Stojmenovic, I. and Miyakawa, M. 1988. An optimal parallel algorithm for solving the maximal elements problem in the plane. Parallel Comput. 7, 2, 249--251.

[42]

Tan, K.-L., Eng, P. K., and Ooi, B. C. 2001. Efficient progressive skyline computation. In Proceedings of the International Conference on Very Large Data Bases (VLDB). 301--310.

Digital Library

[43]

Tao, Y. and Papadias, D. 2006. Maintaining sliding window skylines on data streams. IEEE Trans. Knowl. Data Engin. 18, 3, 377--391.

Digital Library

[44]

Tao, Y., Xiao, X., and Pei, J. 2006. Subsky: efficient computation of skylines in subspaces. In Proceedings of the International Conference on Data Engineering (ICDE).

Digital Library

[45]

Wong, R. C.-W., Fu, A. W.-C., Pei, J., Ho, Y. S., Wong, T., and Liu, Y. 2008. Efficient skyline querying with variable user preferences on nominal attributes. Proc. VLDB Endow. 1, 1, 1032--1043.

Digital Library

[46]

Wu, P., Agrawal, D., Egecioglu, Ö., and El Abbadi, A. 2007. Deltasky: optimal maintenance of skyline deletions without exclusive dominance region generation. In Proceedings of the International Conference on Data Engineering (ICDE). 486--495.

[47]

Wu, P., Zhang, C., Feng, Y., Zhao, B. Y., Agrawal, D., and Abbadi, A. E. 2006. Parallelizing skyline queries for scalable distribution. In Proceedings of the International Conference on Extending Database Technology (EDBT). 112--130.

Digital Library

[48]

Xia, T. and Zhang, D. 2006. Refreshing the sky: the compressed skycube with efficient support for frequent updates. In Proceedings of the ACM/SIGMOD Annual Conference on Management of Data (SIGMOD). 491--502.

Digital Library

[49]

Yuan, Y., Lin, X., Liu, Q., Wang, W., Yu, J. X., and Zhang, Q. 2005. Efficient computation of the skyline cube. In Proceedings of the International Conference on Very Large Data Bases (VLDB). 241--252.

Digital Library

[50]

Zhang, S., Mamoulis, N., and Cheung, D. W. 2009a. Scalable skyline computation using object-based space partitioning. In Proceedings of the ACM/SIGMOD Annual Conference on Management of Data (SIGMOD). 483--494.

Digital Library

[51]

Zhang, Z., Yang, Y., Cai, R., Papadias, D., and Tung, A. K. H. 2009b. Kernel-Based skyline cardinality estimation. In Proceedings of the ACM/SIGMOD Annual Conference on Management of Data (SIGMOD). 509--522.

Digital Library

Cited By

Wu DZhang ZJensen CLu K(2024)Efficient Skyline Keyword-Based Tree Retrieval on Attributed GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338898836:11(6056-6070)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3388988
Khames WHadjali ALagha M(2024)Parallel continuous skyline query over high-dimensional data stream windowsDistributed and Parallel Databases10.1007/s10619-024-07443-742:4(469-524)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10619-024-07443-7
Alami KMaabout S(2021)Using Multidimensional Skylines for Regret MinimizationModel and Data Engineering10.1007/978-3-030-78428-7_23(293-304)Online publication date: 14-Jun-2021
https://doi.org/10.1007/978-3-030-78428-7_23
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Index Terms

Online subspace skyline query processing using the compressed skycube
1. Information systems
  1. Information systems applications

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cover image ACM Transactions on Database Systems

ACM Transactions on Database Systems Volume 37, Issue 2

May 2012

326 pages

ISSN:0362-5915

EISSN:1557-4644

DOI:10.1145/2188349

Issue’s Table of Contents

Copyright © 2012 ACM.

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Publication History

Published: 04 June 2012

Accepted: 01 February 2012

Revised: 01 December 2011

Received: 01 December 2010

Published in TODS Volume 37, Issue 2

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Cited By

Wu DZhang ZJensen CLu K(2024)Efficient Skyline Keyword-Based Tree Retrieval on Attributed GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338898836:11(6056-6070)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3388988
Khames WHadjali ALagha M(2024)Parallel continuous skyline query over high-dimensional data stream windowsDistributed and Parallel Databases10.1007/s10619-024-07443-742:4(469-524)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10619-024-07443-7
Alami KMaabout S(2021)Using Multidimensional Skylines for Regret MinimizationModel and Data Engineering10.1007/978-3-030-78428-7_23(293-304)Online publication date: 14-Jun-2021
https://doi.org/10.1007/978-3-030-78428-7_23
Dong FLuo JJin JShi JYang YShen J(2020)Accelerating Skycube Computation with Partial and Parallel Processing for Service SelectionIEEE Transactions on Services Computing10.1109/TSC.2017.276268113:6(969-984)Online publication date: 1-Nov-2020
https://doi.org/10.1109/TSC.2017.2762681
Barfar APadmanabhan BHevner A(2020)Peak cubes in service operations: Bringing multidimensionality into decision support systemsDecision Support Systems10.1016/j.dss.2020.113442(113442)Online publication date: Nov-2020
https://doi.org/10.1016/j.dss.2020.113442
Alami KMaabout S(2020)A framework for multidimensional skyline queries over streaming dataData & Knowledge Engineering10.1016/j.datak.2020.101792127(101792)Online publication date: May-2020
https://doi.org/10.1016/j.datak.2020.101792
Jiang TZhang BLin DGao YLI Q(2020)Efficient column-oriented processing for mutual subspace skyline queriesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-04875-y24:20(15427-15445)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1007/s00500-020-04875-y
Alami KHanusse NKamnang-Wanko PMaabout S(2019)The negative skycubeInformation Systems10.1016/j.is.2019.101443(101443)Online publication date: Sep-2019
https://doi.org/10.1016/j.is.2019.101443
Han XLi XWang BGao H(2019)PRS: efficient range skyline computation on massive data via presortingKnowledge and Information Systems10.1007/s10115-018-1310-y60:3(1511-1548)Online publication date: 1-Sep-2019
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Rahman MAsudeh AKoudas NDas GLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Efficient Computation of Subspace Skyline over Categorical DomainsProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133012(407-416)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3133012
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