Scalable Uncertainty Management

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dc.contributor.authorPrade Henri
dc.contributor.authorJ. Siekmann
dc.contributor.authorW. Wahlster
dc.date.accessioned2018-05-24T09:30:50Z
dc.date.accessioned2023-08-20T10:57:12Z
dc.date.available2018-05-24T09:30:50Z
dc.date.available2023-08-20T10:57:12Z
dc.date.issued2007-10
dc.descriptionUsually, the data in a database instance is supposed to be consistent and satisfy the database integrity constraints. However, it is quite common for the two to diverge. The data may fail to satisfy the constraints for various reasons: it may be erroneous, out of date, or come from mutually inconsistent sources. In order to deal with inconsistency in a flexible manner, database research and practice have developed different approaches: prevention (usual constraint enforcement), active rules, isolation of inconsistent data, exceptions etcen_US
dc.description.abstractOriginally, managing uncertainty and inconsistency has especially been explored in the field of artificial intelligence. During recent years, particularly with theavailability of massive amounts of data in different repositories and the possibility of integrating and exploiting these data, technologies for managing uncertainty and inconsistency have started to play a key role in databases and the Web. Some of the most prominent of these technologies are probably the ranking algorithms behind Web search engines. Techniques for handling uncertainty and inconsistency are expected to play a similarly important role in the Semantic Web.en_US
dc.identifier.citationBeierle, C., & Dekhtyar, A. (2007). Scalable Uncertainty Management. Springer.en_US
dc.identifier.doihttps://doi.org/10.1007/978-3-540-75410-7
dc.identifier.issn0302-9743
dc.identifier.urihttps://edms.wexl.in/handle/1/1392
dc.language.isoen_USen_US
dc.publisherSpringeren_US
dc.subjectFuzzyen_US
dc.subjectAlgorithmic learningen_US
dc.subjectAnswer set programs
dc.subjectAI
dc.subjectBayesian networks
dc.titleScalable Uncertainty Managementen_US
dc.typeArticleen_US

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