Abstract
Skyline queries aim at finding a set of skyline objects from the given database. For categorical data, the notion of preferences is used to determine skyline objects. There are many real world applications where the preference can be uncertain. In such contexts, it is relevant to determine the probability that an object is a skyline object in a database with uncertain pairwise preferences. Skyline query is to determine a set of objects having skyline probability greater than a threshold. In this paper, we address this problem. To the best of our knowledge, there has not been any technique which handles this problem directly. There have been proposals to compute skyline probability of individual objects but applying these for skyline query is computationally expensive. In this paper, we propose a holistic algorithm that determines the set of skyline objects for a given threshold and a database of uncertain preferences. We establish the relationship between skyline probability and the probability of the union of events. We guide our search to prune objects which are unlikely to be skyline objects. We report extensive experimental analysis to justify the efficiency of our algorithm.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bonferroni, C.E.: Teoria statistica delle classi e calcolo delle probabilita. Libreria internazionale Seeber, Florence (1936)
Bonos, E., Prekopa, A.: Closed form two-sided bounds for probabilities that exactly r and atleast r out of n events occur. Math. Oper. Res. 14, 317–342 (1989)
Dawson, D.A., Sankoff, D.: An inequality for probabilities. Proc. Am. Math. Soc. 18(3), 504–507 (1967)
Han, X., Li, J., Yang, D., Wang, J.: Efficient skyline computation on big data. IEEE Trans. Knowl. Data Eng. 25(11), 2521–2535 (2013)
Kwerel, S.M.: Bounds on the probability of the union and intersection of m events. Adv. Appl. Probab. 7, 431–448 (1975)
Kwerel, S.M.: Most stringent bounds on aggregated probabilities of partially specified dependent probability systems. J. Am. Stat. Assoc. 70(350), 472–479 (1975)
Morse, M., Patel, J.M., Grosky, W.I.: Inf. Sci. Efficient continuous skyline computation 177(17), 3411–3437 (2007)
Papadias, D., Tao, Y., Greg, F., Seeger, B.: Progressive skyline computation in database systems. ACM Trans. Database Syst. (TODS) 30(1), 41–82 (2005)
Papapetrou, O., Garofalakis, M.: Continuous fragmented skylines over distributed streams. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 124–135. IEEE (2014)
Pujari, A.K., Kagita, V.R., Garg, A., Padmanabhan, V.: Bi-directional search for skyline probability. In: Ganguly, S., Krishnamurti, R. (eds.) CALDAM 2015. LNCS, vol. 8959, pp. 250–261. Springer, Heidelberg (2015)
Pujari, A.K., Kagita, V.R., Garg, A., Padmanabhan, V.: Efficient computation for probabilistic skyline over uncertain preferences. Inf. Sci. 324, 146–162 (2015)
Sathe, Y.S., Pradhan, M., Shah, S.P.: Inequalities for the probability of the occurrence of at least m out of n events. J. Appl. Prob. 17, 1127–1132 (1980)
Zhang, Q., Ye, P., Lin, X., Zhang, Y.: Skyline probability over uncertain preferences. In: EDBT/ICDT, pp. 395–405 (2013)
Zhang, S., Mamoulis, N., Cheung, D.W.: Scalable skyline computation using object-based space partitioning. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, pp. 483–494. ACM (2009)
Acknowledgements
Part of this work is carried out at Central University of Rajasthan. Authors acknowledge Central University of Rajasthan for providing facilities.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Kagita, V.R., Pujari, A.K., Padmanabhan, V., Kumar, V., Sahu, S.K. (2016). Threshold-Based Direct Computation of Skyline Objects for Database with Uncertain Preferences. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-42911-3_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42910-6
Online ISBN: 978-3-319-42911-3
eBook Packages: Computer ScienceComputer Science (R0)