Abstract
By definition, objects that are skyline points cannot be compared with each other. Yet, thanks to the probabilistic skyline model, skyline points with repeated observations can now be compared. In this model, each object will be assigned a value to denote for its probability of being a skyline point. When we are using this model, some questions will naturally be asked: (1) Which of the objects have skyline probabilities larger than a given object? (2) Which of the objects are the K nearest neighbors to a given object according to their skyline probabilities? (3) What is the ranking of these objects based on their skyline probabilities? Up to our knowledge, no existing work answers any of these questions. Yet, answering them is not trivial. For just a medium-size dataset, it may take more than an hour to obtain the skyline probabilities of all the objects in there. In this paper, we propose a tree called SPTree that answers all these queries efficiently. SPTree is based on the idea of space partition. We partition the dataspace into several subspaces so that we do not need to compute the skyline probabilities of all objects. Extensive experiments are conducted. The encouraging results show that our work is highly feasible.
Wei Lu’s and Xiaoyong Du’s research is partially supported by National Natural Science Foundation of China under Grant 60573092 and Grant 60873017.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Pei, J., Jiang, B., Lin, X., Yuan, Y.: Probabilistic skylines on uncertain data. In: Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB 2007) (2007)
Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive skyline computation in database systems. ACM Trans. Database Syst (TODS) 30(1), 41–82 (2005)
Borzsonyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proceedings of the 17th International Conference on Data Engineering (ICDE 2001) (2001)
Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with presorting. In: Proceedings of the 19th International Conference on Data Engineering (ICDE 2003) (2003)
Tan, K.L., Eng, P.K., Ooi, B.C.: Efficient progressive skyline computation. In: VLDB 2001, Proceedings of 27th International Conference on Very Large Data Bases (VLDB 2001) (2001)
Kossmann, D., Ramsak, F., Rost, S.: Shooting stars in the sky: An online algorithm for skyline queries. In: Proceedings of the 28th International Conference on Very Large Data Bases (VLDB 2002) (2002)
Papadias, D., Tao, Y., Fu, G., Seeger, B.: An optimal and progressive algorithm for skyline queries. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data (SIGMOD 2003) (2003)
Chan, C.Y., Jagadish, H.V., Tan, K.L., Tung, A.K.H., Zhang, Z.: Finding k-dominant skylines in high dimensional space. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD 2006) (2006)
Lin, X., Yuan, Y., Zhang, Q., Zhang, Y.: Selecting stars: The k most representative skyline operator. In: Proceedings of the 23rd International Conference on Data Engineering (ICDE 2007) (2007)
Dellis, E., Seeger, B.: Efficient computation of reverse skyline queries. In: Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB 2007) (2007)
Deng, K., Zhou, X., Shen, H.T.: Multi-source skyline query processing in road networks. In: Proceedings of the 23rd International Conference on Data Engineering (ICDE 2007) (2007)
Yuan, Y., Lin, X., Liu, Q., Wang, W., Yu, J.X., Zhang, Q.: Efficient computation of the skyline cube. In: Proceedings of the 31st International Conference on Very Large Data Bases (VLDB 2005) (2005)
Pei, J., Jin, W., Ester, M., Tao, Y.: Catching the best views of skyline: A semantic approach based on decisive subspaces. In: Proceedings of the 31st International Conference on Very Large Data Bases (VLDB 2005) (2005)
Tao, Y., Xiaokui Xiao, J.P.: Subsky: Efficient computation of skylines in subspaces. In: Proceedings of the 22nd International Conference on Data Engineering (ICDE 2006) (2006)
Tian Xia, D.Z.: Refreshing the sky: the compressed skycube with efficient support for frequent updates. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD 2006) (2006)
Mouratidis, K., Bakiras, S., Papadias, D.: Continuous monitoring of top-k queries over sliding windows. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD 2006) (2006)
Lin, X., Yuan, Y., Wang, W., Lu, H.: Stabbing the sky: Efficient skyline computation over sliding windows. In: Proceedings of the 21st International Conference on Data Engineering (ICDE 2005) (2005)
Tao, Y., Papadias, D.: Maintaining sliding window skylines on data streams. IEEE Trans. Knowl. Data Eng. (TKDE) 18(2), 377–391 (2006)
Cui, B., Lu, H., Xu, Q., Chen, L., Dai, Y., Zhou, Y.: Stabbing the sky: Efficient skyline computation over sliding windows. In: Proceedings of the 24th International Conference on Data Engineering (ICDE 2008) (2008)
Huang, Z., Jensen, C.S., Lu, H., Ooi, B.C.: Skyline queries against mobile lightweight devices in manets. In: Proceedings of the 22nd International Conference on Data Engineering (ICDE 2006) (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fung, G.P.C., Lu, W., Du, X. (2009). Dominant and K Nearest Probabilistic Skylines. In: Zhou, X., Yokota, H., Deng, K., Liu, Q. (eds) Database Systems for Advanced Applications. DASFAA 2009. Lecture Notes in Computer Science, vol 5463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00887-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-00887-0_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00886-3
Online ISBN: 978-3-642-00887-0
eBook Packages: Computer ScienceComputer Science (R0)