Probabilistic models to reconcile complex data from inaccurate data sources

Lorenzo Blanco, Valter Crescenzi, Paolo Merialdo, Paolo Papotti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Citations (Scopus)

Abstract

Several techniques have been developed to extract and integrate data from web sources. However, web data are inherently imprecise and uncertain. This paper addresses the issue of characterizing the uncertainty of data extracted from a number of inaccurate sources. We develop a probabilistic model to compute a probability distribution for the extracted values, and the accuracy of the sources. Our model considers the presence of sources that copy their contents from other sources, and manages the misleading consensus produced by copiers. We extend the models previously proposed in the literature by working on several attributes at a time to better leverage all the available evidence. We also report the results of several experiments on both synthetic and real-life data to show the effectiveness of the proposed approach.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages83-97
Number of pages15
Volume6051 LNCS
DOIs
Publication statusPublished - 1 Dec 2010
Externally publishedYes
Event22nd International Conference on Advanced Information Systems Engineering, CAiSE 2010 - Hammamet, Tunisia
Duration: 7 Jun 20109 Jun 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6051 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other22nd International Conference on Advanced Information Systems Engineering, CAiSE 2010
CountryTunisia
CityHammamet
Period7/6/109/6/10

Fingerprint

Inaccurate
Probabilistic Model
Probability distributions
Leverage
Experiments
Probability Distribution
Attribute
Integrate
Statistical Models
Uncertainty
Model
Experiment

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Blanco, L., Crescenzi, V., Merialdo, P., & Papotti, P. (2010). Probabilistic models to reconcile complex data from inaccurate data sources. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6051 LNCS, pp. 83-97). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6051 LNCS). https://doi.org/10.1007/978-3-642-13094-6_8

Probabilistic models to reconcile complex data from inaccurate data sources. / Blanco, Lorenzo; Crescenzi, Valter; Merialdo, Paolo; Papotti, Paolo.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6051 LNCS 2010. p. 83-97 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6051 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Blanco, L, Crescenzi, V, Merialdo, P & Papotti, P 2010, Probabilistic models to reconcile complex data from inaccurate data sources. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 6051 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6051 LNCS, pp. 83-97, 22nd International Conference on Advanced Information Systems Engineering, CAiSE 2010, Hammamet, Tunisia, 7/6/10. https://doi.org/10.1007/978-3-642-13094-6_8
Blanco L, Crescenzi V, Merialdo P, Papotti P. Probabilistic models to reconcile complex data from inaccurate data sources. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6051 LNCS. 2010. p. 83-97. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-13094-6_8
Blanco, Lorenzo ; Crescenzi, Valter ; Merialdo, Paolo ; Papotti, Paolo. / Probabilistic models to reconcile complex data from inaccurate data sources. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6051 LNCS 2010. pp. 83-97 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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