Abstract
A probabilistic relational database is a probability distribution over a set of deterministic relational databases (namely, possible worlds). Efficient processing of updating information in probabilistic databases is required in several applications, such as sensor networking, data cleaning. As an important class of updating probabilistic databases, conditioning refines probability distribution of possible worlds, and possibly removing some of the possible worlds based on general knowledge, such as primary key constraints, functional dependencies and others. The existing methods for conditioning are exponential over the number of variables in the probabilistic database for an arbitrary constraint. In this paper, a constraint-based conditioning algorithm is proposed by only considering the variables in the given constraint without enumerating the truth values of all the variables in the formulae of tuples. Then we prove the correctness of the algorithm. The experimental study shows our proposed algorithm is more efficient comparing the work in the literatures.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abiteboul, S., Grahne, G.: Update semantics for incomplete databases. In: Proceedings of the 11th International Conference on Very Large Data Bases, vol. 11, pp. 1–12. VLDB Endowment (1985)
Aggarwal, C.C., Yu, P.S.: A survey of uncertain data algorithms and applications. IEEE Transactions on Knowledge and Data Engineering 21(5), 609–623 (2009)
Cheng, R., Chen, J., Xie, X.: Cleaning uncertain data with quality guarantees. In: Proceedings of the VLDB Endowment, vol. 1(1), pp. 722–735 (2008)
Elnahrawy, E., Nath, B.: Cleaning and querying noisy sensors. In: Proceedings of the 2nd ACM International Conference on Wireless Sensor Networks and Applications, pp. 78–87. ACM (2003)
Feng, H., Wang, H., Li, J., Gao, H.: Entity resolution on uncertain relations. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds.) WAIM 2013. LNCS, vol. 7923, pp. 77–86. Springer, Heidelberg (2013)
Fuhr, N., Rölleke, T.: A probabilistic relational algebra for the integration of information retrieval and database systems. ACM Transactions on Information Systems (TOIS) 15(1), 32–66 (1997)
Hegner, S.: Specification and implementation of programs for updating incomplete information databases. In: Proceedings of the Sixth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 146–158. ACM (1987)
Koch, C., Olteanu, D.: Conditioning probabilistic databases. In: Proceedings of the VLDB Endowment, vol. 1(1), pp. 313–325 (2008)
Soliman, M.A., Ilyas, I.F., Chang, K.C.C.: Probabilistic top-k and ranking-aggregate queries. ACM Transactions on Database Systems (TODS)Â 33(3), 13 (2008)
Song, W., Yu, J.X., Cheng, H., Liu, H., He, J., Du, X.: Bayesian network structure learning from attribute uncertain data. In: Gao, H., Lim, L., Wang, W., Li, C., Chen, L. (eds.) WAIM 2012. LNCS, vol. 7418, pp. 314–321. Springer, Heidelberg (2012)
Tang, R., Cheng, R., Wu, H., Bressan, S.: A framework for conditioning uncertain relational data. In: Liddle, S.W., Schewe, K.-D., Tjoa, A.M., Zhou, X. (eds.) DEXA 2012, Part II. LNCS, vol. 7447, pp. 71–87. Springer, Heidelberg (2012)
Widom, J.: Trio: A system for integrated management of data, accuracy, and lineage. Technical Report (2004)
Moving rating system, http://infolab.stanford.edu/trio/code/index.html#examples
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhu, H., Zhang, C., Cao, Z., Tang, R., Yang, M. (2014). An Efficient Conditioning Method for Probabilistic Relational Databases. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-08010-9_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08009-3
Online ISBN: 978-3-319-08010-9
eBook Packages: Computer ScienceComputer Science (R0)