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Probabilistic databases: diamonds in the dirt

Published: 01 July 2009 Publication History

Abstract

Treasures abound from hidden facts found in imprecise data sets.

References

[1]
Andritsos, P. and Fuxman, A., Miller, R.J. Clean answers over dirty databases. In ICDE (2006).
[2]
Antova, L., Jansen, T., Koch, C. and Olteanu, D. Fast and simple relational processing of uncertain data. In ICDE (2008).
[3]
Barbara, D., Garcia-Molina, H. and Porter, D. The management of probabilistic data. IEEE Trans. Knowl. Data Eng. 4, 5 (1992), 487--502.
[4]
Benjelloun, O., Sarma, A.D., Halevy, A., Theobald, M. and Widom, J. Databases with uncertainty and lineage. VLDBJ 17, 2 (2008), 243--264.
[5]
Burdick, D., Deshpande, P., Jayram, T.S., Ramakrishnan, R. and Vaithyanathan, S. Efficient allocation algorithms for OLAP over imprecise data. In VLDB (2006), 391--402.
[6]
Cavallo, R. and Pittarelli, M. The theory of probabilistic databases. In Proceedings of VLDB (1987), 71--81.
[7]
Cheng, R., Kalashnikov, D. and Prabhakar, S. Evaluating probabilistic queries over imprecise data. In SIGMOD (2003), 551--562.
[8]
Codd, E.F. Relational completeness of data base sublanguages. In Database Systems (1972), Prentice-Hall, 65--98.
[9]
Cowell, R., Dawid, P., Lauritzen, S. and Spiegelhalter D., eds. Probabilistic Networks and Expert Systems (1999), Springer.
[10]
Dalvi, N. and Suciu, D. The dichotomy of conjunctive queries on probabilistic structures. In PODS (2007), 293--302.
[11]
Dalvi, N. and Suciu, D. Efficient query evaluation on probabilistic databases. VLDB J. 16, 4 (2007), 523--544.
[12]
Dalvi, N. and Suciu, D. Management of probabilistic data: Foundations and challenges. In PODS (Beijing, China, 2007) 1--12 (invited talk).
[13]
Darwiche, A. A differential approach to inference in bayesian networks. J. ACM 50, 3 (2003), 280--305.
[14]
DeRose, P., Shen, W., Chen, F., Lee, Y., Burdick, D., Doan, A. and Ramakrishnan, R. Dblife: A community information management platform for the database research community. In CIDR (2007), 169--172.
[15]
Deshpande, A., Guestrin, C., Madden, S., Hellerstein, J.M. and Hong, W. Model-driven data acquisition in sensor networks. In VLDB (2004), 588--599.
[16]
Fagin, R., Lotem, A. and Naor, M. Optimal aggregation algorithms for middleware. In Proceedings of the 20th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (2001), ACM Press, 102--113.
[17]
Friedman, N., Getoor, L., Koller, D. and Pfeffer A. Learning probabilistic relational models. In IJCAI (1999), 1300--1309.
[18]
Fuhr, N. and Roelleke, T. A probabilistic relational algebra for the integration of information retrieval and database systems. ACM Trans. Inf. Syst. 15, 1 (1997), 32--66.
[19]
Grädel, E., Gurevich, Y. and Hirsch, C. The complexity of query reliability. In PODS (1998), 227--234.
[20]
Gupta, R. and Sarawagi, S. Creating probabilistic databases from information extraction models. In VLDB (2006), 965--976.
[21]
Halevy, A. Answering queries using views: A survey. VLDB J. 10, 4 (2001), 270--294.
[22]
Imielinski, T. and Lipski, W. Incomplete information in relational databases. J. ACM 31 (Oct. 1984), 761--791.
[23]
Jampani, R., Xu, F., Wu, M., Perez, L., Jermaine, C. and Haas, P. MCDB: A Monte Carlo approach to managing uncertain data. In SIGMOD (2008), 687--700.
[24]
Jayram, T., Kale, S. and Vee, E. Efficient aggregation algorithms for probabilistic data. In SODA (2007).
[25]
Kanagal, B. and Deshpande, A. Online filtering, smoothing and probabilistic modeling of streaming data. In ICDE (2008), 1160--1169.
[26]
Lafferty, J., McCallum, A. and Pereira, F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML (2001).
[27]
Lakshmanan, L., Leone, N., Ross, R. and Subrahmanian, V. Probview: A flexible probabilistic database system. ACM Trans. Database Syst. 22, 3 (1997).
[28]
Nierman, A. and Jagadish, H. ProTDB: Probabilistic data in XML. In VLDB (2002), 646--657.
[29]
Olteanu, D., Huang, J. and Koch, C. SPROUT: Lazy vs. eager query plans for tuple independent probabilistic databases. In ICDE (2009).
[30]
Rastogi, V., Suciu, D. and Hong, S. The boundary between privacy and utility in data publishing. In VLDB (2007).
[31]
Ré, C., Dalvi, N. and Suciu, D. Efficient Top-k query evaluation on probabilistic data. In ICDE (2007).
[32]
Ré, C., Suciu, D. Efficient evaluation of having queries on a probabilistic database. In Proceedings of DBPL (2007).
[33]
Ré, C. and Suciu, D. Materialized views in probabilistic databases for information exchange and query optimization. In Proceedings of VLDB (2007)
[34]
Ré, C., Letchner, J., Balazinska, M. and Suciu, D. Event queries on correlated probabilistic streams. In SIGMOD (Vancouver, Canada, 2008).
[35]
Roth, D. On the hardness of approximate reasoning. Artif. Intell. 82, 1--2 (1996), 273--302.
[36]
Sen, P. and Deshpande, A. Representing and querying correlated tuples in probabilistic databases. In ICDE, 2007.
[37]
Soliman, M.A., Ilyas, I.F. and Chang, K.C.-C. Probabilistic top- and ranking-aggregate queries. ACM Trans. Database Syst. 33, 3 (2008).
[38]
Vardi, M.Y. The complexity of relational query languages. In Proceedings of 14th ACM SIGACT Symposium on the Theory of Computing (San Francisco, California, 1982), 137--146.
[39]
Verma, T. and Pearl, J. Causal networks: Semantics and expressiveness. Uncertainty Artif. Intell. 4 (1990), 69--76.
[40]
Wong, E. A statistical approach to incomplete information in database systems. ACM Trans. Database Syst. 7, 3 (1982), 470--488.

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Published In

cover image Communications of the ACM
Communications of the ACM  Volume 52, Issue 7
Barbara Liskov: ACM's A.M. Turing Award Winner
July 2009
141 pages
ISSN:0001-0782
EISSN:1557-7317
DOI:10.1145/1538788
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 July 2009
Published in CACM Volume 52, Issue 7

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