File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Capturing continuous data and answering aggregate queries in probabilistic XML

TitleCapturing continuous data and answering aggregate queries in probabilistic XML
Authors
KeywordsAggregate queries
Aggregation
Continuous distributions
Probabilistic databases
Probabilistic XML
Issue Date2011
PublisherAssociation for Computing Machinery, Inc.
Citation
ACM Transactions on Database Systems, 2011, v. 36 n. 4, article no. 25, p. 25:1-25:45 How to Cite?
AbstractSources of data uncertainty and imprecision are numerous. A way to handle this uncertainty is to associate probabilistic annotations to data. Many such probabilistic database models have been proposed, both in the relational and in the semi-structured setting. The latter is particularly well adapted to the management of uncertain data coming from a variety of automatic processes. An important problem, in the context of probabilistic XML databases, is that of answering aggregate queries (count, sum, avg, etc.), which has received limited attention so far. In a model unifying the various (discrete) semi-structured probabilistic models studied up to now, we present algorithms to compute the distribution of the aggregation values (exploiting some regularity properties of the aggregate functions) and probabilistic moments (especially expectation and variance) of this distribution. We also prove the intractability of some of these problems and investigate approximation techniques. We finally extend the discrete model to a continuous one, in order to take into account continuous data values, such as measurements from sensor networks, and extend our algorithms and complexity results to the continuous case.
Persistent Identifierhttp://hdl.handle.net/10722/160534
ISSN
2015 Impact Factor: 0.633
2015 SCImago Journal Rankings: 0.792
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorAbiteboul, Sen_US
dc.contributor.authorChan, HTHen_US
dc.contributor.authorKharlamov, Een_US
dc.contributor.authorNutt, Wen_US
dc.contributor.authorSenellart, Pen_US
dc.date.accessioned2012-08-16T06:13:30Z-
dc.date.available2012-08-16T06:13:30Z-
dc.date.issued2011en_US
dc.identifier.citationACM Transactions on Database Systems, 2011, v. 36 n. 4, article no. 25, p. 25:1-25:45en_US
dc.identifier.issn0362-5915-
dc.identifier.urihttp://hdl.handle.net/10722/160534-
dc.description.abstractSources of data uncertainty and imprecision are numerous. A way to handle this uncertainty is to associate probabilistic annotations to data. Many such probabilistic database models have been proposed, both in the relational and in the semi-structured setting. The latter is particularly well adapted to the management of uncertain data coming from a variety of automatic processes. An important problem, in the context of probabilistic XML databases, is that of answering aggregate queries (count, sum, avg, etc.), which has received limited attention so far. In a model unifying the various (discrete) semi-structured probabilistic models studied up to now, we present algorithms to compute the distribution of the aggregation values (exploiting some regularity properties of the aggregate functions) and probabilistic moments (especially expectation and variance) of this distribution. We also prove the intractability of some of these problems and investigate approximation techniques. We finally extend the discrete model to a continuous one, in order to take into account continuous data values, such as measurements from sensor networks, and extend our algorithms and complexity results to the continuous case.-
dc.languageengen_US
dc.publisherAssociation for Computing Machinery, Inc.-
dc.relation.ispartofACM Transactions on Database Systemsen_US
dc.rightsACM Transactions on Database Systems. Copyright © Association for Computing Machinery, Inc.-
dc.subjectAggregate queries-
dc.subjectAggregation-
dc.subjectContinuous distributions-
dc.subjectProbabilistic databases-
dc.subjectProbabilistic XML-
dc.titleCapturing continuous data and answering aggregate queries in probabilistic XMLen_US
dc.typeArticleen_US
dc.identifier.emailChan, HTH: hubert@cs.hku.hken_US
dc.identifier.authorityChan, HTH=rp01312en_US
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1145/2043652.2043658-
dc.identifier.scopuseid_2-s2.0-84855216736-
dc.identifier.hkuros202977en_US
dc.identifier.volume36-
dc.identifier.issue4, article no. 25-
dc.identifier.spage25:1-
dc.identifier.epage25:45-
dc.identifier.eissn1557-4644-
dc.identifier.isiWOS:000298291800006-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats