Special issue on uncertain and probabilistic databases

Special issue on uncertain and probabilistic databases The VLDB Journal (2009) 18:987–988 DOI 10.1007/s00778-009-0158-x EDITORIAL Peter J. Haas · Dan Suciu Published online: 13 August 2009 © Springer-Verlag 2009 A diverse class of applications needs to manage large the query-answer distribution, such as the probability that a volumes of uncertain data. Examples of such data include given record appears in the query answer, or the k most likely incompletely cleaned data, data from acquisition systems records in the query answer. such as sensor nets or RFID deployments, data generated by The field of probabilistic databases is concerned with information extraction systems, data inferred from a synopsis tools and techniques for compactly specifying and then pro- or statistical summary, data obtained as a result of imprecise cessing large volumes of probabilistic data. Recently, there data mappings, data that has been blurred or anonymized has been considerable interest in this area, motivated both to protect privacy, and missing data that has been interpo- by the wealth of applications and by the sharp rise in the lated or extrapolated. The ability to manage uncertain data is amount of uncertain data as database systems have expanded crucial for evaluating the quality of query results, assessing beyond their traditional confines. Probabilistic http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Special issue on uncertain and probabilistic databases

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Publisher
Springer-Verlag
Copyright
Copyright © 2009 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-009-0158-x
Publisher site
See Article on Publisher Site

Abstract

The VLDB Journal (2009) 18:987–988 DOI 10.1007/s00778-009-0158-x EDITORIAL Peter J. Haas · Dan Suciu Published online: 13 August 2009 © Springer-Verlag 2009 A diverse class of applications needs to manage large the query-answer distribution, such as the probability that a volumes of uncertain data. Examples of such data include given record appears in the query answer, or the k most likely incompletely cleaned data, data from acquisition systems records in the query answer. such as sensor nets or RFID deployments, data generated by The field of probabilistic databases is concerned with information extraction systems, data inferred from a synopsis tools and techniques for compactly specifying and then pro- or statistical summary, data obtained as a result of imprecise cessing large volumes of probabilistic data. Recently, there data mappings, data that has been blurred or anonymized has been considerable interest in this area, motivated both to protect privacy, and missing data that has been interpo- by the wealth of applications and by the sharp rise in the lated or extrapolated. The ability to manage uncertain data is amount of uncertain data as database systems have expanded crucial for evaluating the quality of query results, assessing beyond their traditional confines. Probabilistic

Journal

The VLDB JournalSpringer Journals

Published: Oct 1, 2009

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