A Unifying Framework for Incompleteness, Inconsistency, and Uncertainty in Databases
Abstract
Deploying possible world semantics and the challenge of computing the certain answers to queries.
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References
[1]
Abiteboul, S., Hull, R. and Vianu, V. Foundations of Databases. Addison-Wesley, 1995; http://webdam.inria.fr/Alice/
[2]
Aho, A.V., Beeri, C. and Ullman, J.D. The theory of joins in relational databases. ACM Trans. Database Syst. 4, 3 (1979), 297--314
[3]
Arenas, M., Barceló, P., Libkin, L. and Murlak, F. Foundations of Data Exchange. Cambridge University Press, 2014. http://www.cambridge.org/9781107016163
[4]
Arenas, M., Bertossi, L.E. and Chomicki, J. Consistent query answers in inconsistent databases. In Proceedings of the Eighteenth ACM SIGACTSIGMODSIGART Symp. on Principles of Database Systems, May 31 - June 2, 1999, Philadelphia, Pennsylvania, USA. V. Vianu and C.H. Papadimitriou (eds). ACM Press, 1999, 68--79
[5]
Arenas, M. et al. Scalar aggregation in inconsistent databases. Theor. Comput. Sci. 296, 3 (2003), 405--434
[6]
Baumeister, D. and Rothe, J. Taking the final step to a full dichotomy of the possible winner problem in pure scoring rules. Inf. Process. Lett. 112, 5 (2012), 186--190.
[7]
Beeri, C. and Vardi, M.Y. A proof procedure for data dependencies. J. ACM 31, 4 (1984), 718--741
[8]
Bertossi, L.E. Database Repairing and Consistent Query Answering. Morgan & Claypool Publishers, 2011.
[9]
Bienvenu, M., Bourgaux, C. and Goasdoué, F. Computing and explaining query answers over inconsistent DL-lite knowledge bases. J. Artif. Intell. Res. 64 (2019), 563--644.
[10]
Handbook of Computational Social Choice. F. Brandt et al (eds). Cambridge University Press, 2016
[11]
Dalvi, N. and Suciu, D. Efficient query evaluation on probabilistic databases. VLDB J. 16, 4 (2007), 523--544.
[12]
Dalvi, N.N. and Suciu, D. The dichotomy of probabilistic inference for unions of conjunctive queries. J. ACM 59, 6 (2012), 30:1--30:87.
[13]
Dixit, A.A. and Kolaitis, P.G. Consistent answers of aggregatio queries via SAT. In Proceedings of the 38th Intern. Conf. on Data Engineering (ICDE 2022). IEEE, 2022, 924--937.
[14]
Fagin, R., Kolaitis, P.G., J. Miller, R. and Popa, L. Data exchange: Semantics and query answering. Theor. Comput. Sci. 336, 1 (2005), 89--124
[15]
Fagin, R. and Vardi, M.Y. The theory of data dependencies - A survey. In Symposia in Applied Mathematics, 1986, 19--71.
[16]
Fink, R., Han, L. and Olteanu, D. Aggregation in probabilistic databases via knowledge compilation. Proc. VLDB Endow. 5, 5 (2012), 490--501.
[17]
Fuxman, A. and Miller, R.J. First-order query rewriting for inconsistent databases. J. Comput. Syst. Sci. 73, 4 (2007), 610--635
[18]
Garey, M.R. and Johnson, D.S. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman, 1979.
[19]
Grädel, E., Gurevich, Y. and Hirsch, C. The complexity of query reliability. In PODS. ACM Press, 1998, 227--234.
[20]
Imielinski, T. and Lipski, W. Jr. Incomplete information in relational databases. J. ACM 31, 4 (1984), 761--791
[21]
Kimelfeld, B., Kolaitis, P.G. and Stoyanovich, J. Computational social choice meets databases. In IJCAI. ijcai.org, 2018, 317--323.
[22]
Kimelfeld, B., Kolaitis, P.G. and Tibi, M. Query evaluation in election databases. In PODS. ACM, 2019, 32--46.
[23]
Dagstuhl follow-ups. Data Exchange, Integration, and Streams 5. P.G. Kolaitis, M. Lenzerini and N. Schweikardt (eds). Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2013; http://www.dagstuhl.de/dagpub/978-3-939897-61-3
[24]
Kolaitis, P.G., Panttaja, J. and Tan, W.C. The complexity of data exchange. In Proceedings of the Twenty-Fifth ACM SIGACT-SIGMOD-SIGART Symp. on Principles of Database Systems, June 26--28, 2006, Chicago, Illinois, USA. S. Vansummeren (ed). ACM, 2006, 30--39
[25]
Kolaitis, P.G. and Pema, E. A dichotomy in the complexity of consistent query answering for queries with two atoms. Inf. Process. Lett. 112, 3 (2012), 77--85
[26]
Konczak, K. and Lang, J. Voting procedures with incomplete preferences. In Proc. IJCAI-05 Multidisciplinary Workshop on Advances in Preference Handling 20, 2005.
[27]
Koutris, P. and Wijsen, J. Consistent query answering for self-join-free conjunctive queries under primary key constraints. ACM Trans. Database Syst. 42, 2 (2017), 9:1--9:45
[28]
Libkin, L. SQL's three-valued logic and certain answers. In ICDT (LIPIcs, Vol. 31). Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2015, 94--109.
[29]
Maier, D., Mendelzon, A.O. and Sagiv, Y. Testing implications of data dependencies. ACM Trans. Database Syst. 4, 4 (1979), 455--469
[30]
Manna, M., Ricca, F. and Terracina, G. Taming primary key violations to query large inconsistent data via ASP. Theory Pract. Log. Program 15, 4--5 (2015), 696--710.
[31]
Pottinger, R. and Halevy, A.Y. MiniCon: A scalable algorithm for answering queries using views. VLDB J. 10, 2--3 (2001), 182--198
[32]
Provan, J.S. and Ball, M.O. The complexity of counting cuts and of computing the probability that a graph is connected. SIAM J. Comput. 12, 4 (1983), 777--788.
[33]
Senellart, P., Jachiet, L., Maniu, S. and Ramusat, Y. ProvSQL: Provenance and probability management in postgresql. In Proc. VLDB Endow 11, 12 (2018), 2034--2037.
[34]
Suciu, D., Olteanu, D., Ré, C. and Koch, C. Probabilistic Databases. Morgan & Claypool Publishers, 2011.
[35]
Vardi, M.Y. The complexity of relational query languages (extended abstract). In Proceedings of the 14th Annual ACM Symp. on Theory of Computing, May 5--7, 1982, San Francisco, California, USA. H.R. Lewis, B.B. Simons, W.A. Burkhard and L.H. Landweber (Eds). ACM, 1982, 137--146
[36]
Wijsen, J. A remark on the complexity of consistent conjunctive query answering under primary key violations. Inf. Process. Lett. 110, 21 (2010), 950--955
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- A Unifying Framework for Incompleteness, Inconsistency, and Uncertainty in Databases
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Published: 22 February 2024
Online First: 05 February 2024
Published in CACM Volume 67, Issue 3
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