Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-32597-7_7
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dc.titleA framework for conditioning uncertain relational data
dc.contributor.authorTang, R.
dc.contributor.authorCheng, R.
dc.contributor.authorWu, H.
dc.contributor.authorBressan, S.
dc.date.accessioned2013-07-04T07:52:19Z
dc.date.available2013-07-04T07:52:19Z
dc.date.issued2012
dc.identifier.citationTang, R.,Cheng, R.,Wu, H.,Bressan, S. (2012). A framework for conditioning uncertain relational data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7447 LNCS (PART 2) : 71-87. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-32597-7_7" target="_blank">https://doi.org/10.1007/978-3-642-32597-7_7</a>
dc.identifier.isbn9783642325960
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39906
dc.description.abstractWe propose a framework for representing conditioned probabilistic relational data. In this framework the existence of tuples in possible worlds is determined by Boolean expressions composed from elementary events. The probability of a possible world is computed from the probabilities associated with these elementary events. In addition, a set of global constraints conditions the database. Conditioning is the formalization of the process of adding knowledge to a database. Some worlds may be impossible given the constraints and the probabilities of possible worlds are accordingly re-defined. The new constraints can come from the observation of the existence or non-existence of a tuple, from the knowledge of a specific rule, such as the existence of an exclusive set of tuples, or from the knowledge of a general rule, such as a functional dependency. We are therefore interested in computing a concise representation of the possible worlds and their respective probabilities after the addition of new constraints, namely an equivalent probabilistic database instance without constraints after conditioning. We devise and present a general algorithm for this computation. Unfortunately, the general problem involves the simplification of general Boolean expressions and is NP-hard. We therefore identify specific practical families of constraints for which we devise and present efficient algorithms. © 2012 Springer-Verlag.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-32597-7_7
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/978-3-642-32597-7_7
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume7447 LNCS
dc.description.issuePART 2
dc.description.page71-87
dc.identifier.isiutNOT_IN_WOS
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