ABSTRACT
The skyline query is important in many applications such as multi-criteria decision making, data mining, and user-preference queries. Given a set of d-dimensional objects, the skyline query finds the objects that are not dominated by others. In practice, different users may be interested in different dimensions of the data, and issue queries on any subset of d dimensions. This paper focuses on supporting concurrent and unpredictable subspace skyline queries in frequently updated databases. Simply to compute and store the skyline objects of every subspace in a skycube will incur expensive update cost. In this paper, we investigate the important issue of updating the skycube in a dynamic environment. To balance the query cost and update cost, we propose a new structure, the compressed skycube, which concisely represents the complete skycube. We thoroughly explore the properties of the compressed skycube and provide an efficient object-aware update scheme. Experimental results show that the compressed skycube is both query and update efficient.
- {1} S. Agarwal, R. Agrawal, P. Deshpande, A. Gupta, J. Naughton, R. Ramakrishnan, and S. Sarawagi. On the Computation of Multidimensional Aggregates. In VLDB, pages 506-521, 1996. Google ScholarDigital Library
- {2} W.-T. Balke and U. Güntzer. Multi-objective Query Processing for Database Systems. In VLDB, pages 936-947, 2004. Google ScholarDigital Library
- {3} W.-T. Balke, U. Güntzer, and J. X. Zheng. Efficient Distributed Skylining for Web Information Systems. In EDBT, pages 256-273, 2004.Google ScholarCross Ref
- {4} O. Barndorff-Nielsen and M. Sobel. On the Distribution of the Number of Admissable Points in a Vector Random Sample. Theory of Probability and its Application, 11(2):249-269, 1966.Google ScholarCross Ref
- {5} J. L. Bentley, K. L. Clarkson, and D. B. Levine. Fast Linear Expected-time Algorithms for Computing Maxima and Convex Hulls. In Proc. of Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 179-187, 1990. Google ScholarDigital Library
- {6} J. L. Bentley, H. T. Kung, M. Schkolnick, and C. D. Thompson. On the Average Number of Maxima in a Set of Vectors and Applications. Journal of ACM, 25(4):536-543, 1978. Google ScholarDigital Library
- {7} S. Börzsönyi, D. Kossmann, and K. Stocker. The Skyline Operator. In ICDE, pages 421-430, 2001. Google ScholarDigital Library
- {8} C. Y. Chan, P.-K. Eng, and K.-L. Tan. Stratified Computation of Skylines with Partially-Ordered Domains. In SIGMOD, pages 203-214, 2005. Google ScholarDigital Library
- {9} J. Chomicki, P. Godfrey, J. Gryz, and D. Liang. Skyline with Presorting. In ICDE, pages 717-816, 2003.Google ScholarCross Ref
- {10} P. Godfrey, R. Shipley, and J. Gryz. Maximal Vector Computation in Large Data Sets. In VLDB, pages 229-240, 2005. Google ScholarDigital Library
- {11} V. Hristidis, N. Koudas, and Y. Papakonstantinou. PREFER: A System for the Efficient Execution of Multi-parametric Ranked Queries. In SIGMOD, pages 259-270, 2001. Google ScholarDigital Library
- {12} W. Jin, J. Han, and M. Ester. Mining Thick Skylines over Large Databases. In European Conf. on Principles of Data Mining and Knowledge Discovery (PKDD), pages 255-266, 2004. Google ScholarDigital Library
- {13} D. Kossmann, F. Ramsak, and S. Rost. Shooting Stars in the Sky: An Online Algorithm for Skyline Queries. In VLDB, pages 275-286, 2002. Google ScholarDigital Library
- {14} H. T. Kung, F. Luccio, and F. P. Preparata. On Finding the Maxima of a Set of Vectors. Journal of ACM, 22(4):469-476, 1975. Google ScholarDigital Library
- {15} X. Lin, Y. Yuan, W. Wang, and H. Lu. Stabbing the Sky: Efficient Skyline Computation over Sliding Windows. In ICDE, pages 502-513, 2005. Google ScholarDigital Library
- {16} D. Papadias, Y. Tao, G. Fu, and B. Seeger. An Optimal and Progressive Algorithm for Skyline Queries. In SIGMOD, pages 467-478, 2003. Google ScholarDigital Library
- {17} J. Pei, W. Jin, M. Ester, and Y. Tao. Catching the Best Views of Skyline: A Semantic Approach Based on Decisive Subspaces. In VLDB, pages 253-264, 2005. Google ScholarDigital Library
- {18} F. Preparata and M. Shamos. Computational Geometry: An Introduction. Sprinter Verlag, 1985. Google ScholarDigital Library
- {19} K.-L. Tan, P. K. Eng, and B. C. Ooi. Efficient Progressive Skyline Computation. In VLDB, pages 301-310, 2001. Google ScholarDigital Library
- {20} Y. Tao, X. Xiao, and J. Pei. SUBSKY: Efficient Computation of Skylines in Subspaces. In ICDE, 2006. Google ScholarDigital Library
- {21} Y. Yuan, X. Lin, Q. Liu, W. Wang, J. X. Yu, and Q. Zhang. Efficient Computation of the Skyline Cube. In VLDB, pages 241-252, 2005. Google ScholarDigital Library
Index Terms
- Refreshing the sky: the compressed skycube with efficient support for frequent updates
Recommendations
Finding k-dominant skylines in high dimensional space
SIGMOD '06: Proceedings of the 2006 ACM SIGMOD international conference on Management of dataGiven a d-dimensional data set, a point p dominates another point q if it is better than or equal to q in all dimensions and better than q in at least one dimension. A point is a skyline point if there does not exists any point that can dominate it. ...
D-SKY: A Framework for Processing Skyline Queries in a Dynamic and Incomplete Database
iiWAS2018: Proceedings of the 20th International Conference on Information Integration and Web-based Applications & ServicesProcessing skyline queries in incomplete data is challenging, particularly, for a database with dynamic contents in which the database is frequently updated. These update operations not only affect the skyline computation, but also influence the skyline ...
Ranking uncertain sky: The probabilistic top-k skyline operator
Many recent applications involve processing and analyzing uncertain data. In this paper, we combine the feature of top-k objects with that of skyline to model the problem of top-k skyline objects against uncertain data. The problem of efficiently ...
Comments