Abstract
Given a set of data elements \(\mathcal D\) in a d-dimensional space, a k-skyband query reports the set of elements which are dominated by at most k − 1 other elements in \(\mathcal D\). k-skyband query is a fundamental query type in data analyzing as it keeps a minimum candidate set for all top-k ranking queries where the ranking functions are monotonic. In this paper, we study the problem of k-skyband over uncertain data streams following the possible world semantics where each data element is associated with an occurrence probability. Firstly, a dynamic programming based algorithm is proposed to identify k-skyband results for a given set of uncertain elements regarding a pre-specified probability threshold. Secondly, we characterize the minimum set of elements to be kept in the sliding window to guarantee correct computing of k-skyband. Thirdly, efficient update techniques based on R-tree structures are developed to handle frequent updates of the elements over the sliding window. Extensive empirical studies demonstrate the efficiency and effectiveness of our techniques.
Wenjie Zhang was partially supported by ARC DE120102144 and DP120104168. Ying Zhang was partially supported by DP110104880 and UNSW ECR grant PSE1799. Yunjun Gao was supported in part by NSFC 61003049 and the Fundamental Research Funds for the Central Universities under Grant 2012QNA5018.
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References
Aggarwal, C.C., Yu, P.S.: A framework for clustering uncertain data streams. In: ICDE 2008 (2008)
Borzsonyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE 2001 (2001)
Cormode, G., Garofalakis, M.: Sketching probabilistic data streams. In: SIGMOD 2007 (2007)
Dellis, E., Seeger, B.: Efficient computation of reverse skyline queries. In: VLDB 2007 (2007)
Jayram, T., Kale, S., Vee, E.: Efficient aggregation algorithms for probabilistic data. In: SODA 2007 (2007)
Jayram, T.S., McGregor, A., Muthukrishan, S., Vee, E.: Estimating statistical aggregrates on probabilistic data streams. In: PODS 2007 (2007)
Jin, C., Yi, K., Chen, L., Yu, J.X., Lin, X.: Sliding-window top-k queries on uncertain streams. In: VLDB 2008 (2008)
Lian, X., Chen, L.: Monochromatic and bichromatic reverse skyline search over uncertain databases. In: SIGMOD 2008 (2008)
Lin, X., Yuan, Y., Wang, W., Lu, H.: Stabbing the sky: Efficient skyline computation over sliding windows. In: ICDE 2005 (2005)
Liu, Q., Gao, Y., Chen, G., Li, Q., Jiang, T.: On efficient reverse k-skyband query processing. In: Lee, S.-G., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012, Part I. LNCS, vol. 7238, pp. 544–559. Springer, Heidelberg (2012)
Mouratidis, K., Bakiras, S., Papadias, D.: Continuous monitoring of top-k queries over sliding windows. In: SIGMOD 2006 (2006)
Papadias, D., Tao, Y., Fu, G., Seeger, B.: An optimal progressive algorithm for skyline queries. In: SIGMOD 2003 (2003)
Pei, J., Jiang, B., Lin, X., Yuan, Y.: Probabilistic skylines on uncertain data. In: VLDB 2007 (2007)
Tao, Y., Papadias, D.: Maintaining sliding window skylines on data streams. In: TKDE 2006 (2006)
Zhang, Q., Li, F., Yi, K.: Finding frequent items in probabilistic data. In: SIGMOD 2008 (2008)
Zhang, W., Lin, X., Zhang, Y., Wang, W., Yu, J.X.: Probabilistic skyline operator over sliding windows. In: Information Systems 2012 (2012)
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Feng, X., Zhang, W., Zhao, X., Zhang, Y., Gao, Y. (2013). Probabilistic k-Skyband Operator over Sliding Windows. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds) Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38562-9_20
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DOI: https://doi.org/10.1007/978-3-642-38562-9_20
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