Probabilistic Data: A Tiny Survey

Ander de Keijzer

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

Abstract

In this survey, we will visit existing projects and proposals for uncertain data, all supporting probabilistic handling of confidence scores. Several models for uncertain data have been proposed over the years. Initial efforts all focused on relational data [3] and also currently efforts are being made in the relational setting [10,4,5,7,2]. With relational data models, two methods to associate confidences with data are commonly used.
Original languageEnglish
Title of host publicationScalable uncertainty management
EditorsAmol Deshpande, Anthony Hunter
PublisherSpringer
Pages7-10
ISBN (Print)9783642159503
DOIs
Publication statusPublished - 2010

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume6379

Fingerprint

Data structures

Keywords

  • IR-73965

Cite this

de Keijzer, A. (2010). Probabilistic Data: A Tiny Survey. In A. Deshpande, & A. Hunter (Eds.), Scalable uncertainty management (pp. 7-10). (Lecture Notes in Computer Science; Vol. 6379). Springer. https://doi.org/10.1007/978-3-642-15951-0_4
de Keijzer, Ander. / Probabilistic Data: A Tiny Survey. Scalable uncertainty management. editor / Amol Deshpande ; Anthony Hunter. Springer, 2010. pp. 7-10 (Lecture Notes in Computer Science).
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de Keijzer, A 2010, Probabilistic Data: A Tiny Survey. in A Deshpande & A Hunter (eds), Scalable uncertainty management. Lecture Notes in Computer Science, vol. 6379, Springer, pp. 7-10. https://doi.org/10.1007/978-3-642-15951-0_4

Probabilistic Data: A Tiny Survey. / de Keijzer, Ander.

Scalable uncertainty management. ed. / Amol Deshpande; Anthony Hunter. Springer, 2010. p. 7-10 (Lecture Notes in Computer Science; Vol. 6379).

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

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de Keijzer A. Probabilistic Data: A Tiny Survey. In Deshpande A, Hunter A, editors, Scalable uncertainty management. Springer. 2010. p. 7-10. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-15951-0_4