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Online data fusion

Authors:

Divesh SrivastavaAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 4, Issue 11

Pages 932 - 943

https://doi.org/10.14778/3402707.3402731

Published: 01 August 2011 Publication History

Abstract

The Web contains a significant volume of structured data in various domains, but a lot of data are dirty and erroneous, and they can be propagated through copying. While data integration techniques allow querying structured data on the Web, they take the union of the answers retrieved from different sources and can thus return conflicting information. Data fusion techniques, on the other hand, aim to find the true values, but are designed for offline data aggregation and can take a long time.

This paper proposes Solaris, the first online data fusion system. It starts with returning answers from the first probed source, and refreshes the answers as it probes more sources and applies fusion techniques on the retrieved data. For each returned answer, it shows the likelihood that the answer is correct, and stops retrieving data for it after gaining enough confidence that data from the unprocessed sources are unlikely to change the answer. We address key problems in building such a system and show empirically that the system can start returning correct answers quickly and terminate fast without sacrificing the quality of the answers.

References

[1]

L. Berti-Equille. Quality Awareness for Managing and Mining Data. PhD thesis, Universite de Rennes 1, 2007.

[2]

L. Blanco, V. Crescenzi, P. Merialdo, and P. Papotti. Probabilistic models to reconcile complex data from inaccurate data sources. In CAiSE, 83--97, 2010.

[3]

J. Bleiholder, S. Khuller, F. Naumann, L. Raschid, and Y. Wu. Query planning in the presence of overlapping sources. In EDBT, 811--828, 2006.

[4]

M. J. Cafarella, A. Y. Halevy, and J. Madhavan. Structured data on the web. Commun. ACM, 54(2):72--79, 2011.

Digital Library

[5]

X. Dong, L. Berti-Equille, Y. Hu, and D. Srivastava. Global detection of complex copying relationships between sources. PVLDB, 3(1):1358--1369, 2010.

Digital Library

[6]

X. L. Dong, L. Berti-Equille, and D. Srivastava. Integrating conflicting data: The role of source dependence. PVLDB, 2(1):550--561, 2009.

Digital Library

[7]

X. L. Dong and F. Naumann. Data fusion - resolving data conflicts for integration. PVLDB, 2(2):1654--1655, 2009.

Digital Library

[8]

A. Galland, S. Abiteboul, A. Marian, and P. Senellart. Corroborating information from disagreeing views. In WSDM, 131--140, 2010.

[9]

J. M. Hellerstein, P. J. Haas, and H. J. Wang. Online aggregation. In SIGMOD, 171--182, 1997.

[10]

G. A. Mihaila, L. Raschid, and M.-E. Vidal. Using quality of data metadata for source selection and ranking. In WebDB, 93--98, 2000.

[11]

F. Naumann. Quality-Driven Query Answering for Integrated Information Systems. Springer, 2002.

Digital Library

[12]

A. D. Sarma, X. L. Dong, and A. Y. Halevy. Data integration with dependent sources. In EDBT, 401--412, 2011.

[13]

M. A. Suryanto, E.-P. Lim, A. Sun, and R. H. L. Chiang. Quality-aware collaborative question answering: methods and evaluation. In WSDM, 142--151, 2009.

[14]

M. Wu and A. Marian. A framework for corroborating answers from multiple web sources. Inf. Syst., 36(2):431--449, 2011.

Digital Library

[15]

N. K. Yeganeh, S. Sadiq, K. Deng, and X. Zhou. Data quality aware queries in collaborative information systems. Lecture Notes in Computer Science, 5446:39--50, 2009.

Digital Library

[16]

X. Yin, J. Han, and P. S. Yu. Truth discovery with multiple conflicting information providers on the web. IEEE Trans. Knowl. Data Eng., 20:796--808, 2008.

Digital Library

Cited By

Fang XWang XSheng QYao L(2024)Generalizing truth discovery by incorporating multi-truth featuresComputing10.1007/s00607-024-01288-9106:5(1557-1583)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s00607-024-01288-9
Azzalini FPiantella DRabosio ETanca L(2022)Enhancing domain-aware multi-truth data fusion using copy-based source authority and value similarityThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-022-00757-x32:3(475-500)Online publication date: 19-Jul-2022
https://dl.acm.org/doi/10.1007/s00778-022-00757-x
Fang XSheng QWang XZhang WNgu AYang J(2020)From Appearance to EssenceACM Transactions on Intelligent Systems and Technology10.1145/341174911:6(1-24)Online publication date: 11-Sep-2020
https://dl.acm.org/doi/10.1145/3411749
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Online data fusion
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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 4, Issue 11

August 2011

520 pages

ISSN:2150-8097

Editor:
H. V. Jagadish

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VLDB Endowment

Publication History

Published: 01 August 2011

Published in PVLDB Volume 4, Issue 11

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Cited By

Fang XWang XSheng QYao L(2024)Generalizing truth discovery by incorporating multi-truth featuresComputing10.1007/s00607-024-01288-9106:5(1557-1583)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s00607-024-01288-9
Azzalini FPiantella DRabosio ETanca L(2022)Enhancing domain-aware multi-truth data fusion using copy-based source authority and value similarityThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-022-00757-x32:3(475-500)Online publication date: 19-Jul-2022
https://dl.acm.org/doi/10.1007/s00778-022-00757-x
Fang XSheng QWang XZhang WNgu AYang J(2020)From Appearance to EssenceACM Transactions on Intelligent Systems and Technology10.1145/341174911:6(1-24)Online publication date: 11-Sep-2020
https://dl.acm.org/doi/10.1145/3411749
Deng YLi YShen YDu NFan WYang MLei KZhu WTao DCheng XCui PRundensteiner ECarmel DHe QXu Yu J(2019)MedTruthProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3357934(719-728)Online publication date: 3-Nov-2019
https://dl.acm.org/doi/10.1145/3357384.3357934
Lin XChen L(2018)Domain-aware multi-truth discovery from conflicting sourcesProceedings of the VLDB Endowment10.1145/3187009.317773911:5(635-647)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1145/3187009.3177739
Lin XChen L(2018)Domain-aware multi-truth discovery from conflicting sourcesProceedings of the VLDB Endowment10.1145/3177732.317773911:5(635-647)Online publication date: 5-Oct-2018
https://dl.acm.org/doi/10.1145/3177732.3177739
Li LFeng XShao HLi J(2018)Source Selection for Inconsistency DetectionDatabase Systems for Advanced Applications10.1007/978-3-319-91458-9_22(370-385)Online publication date: 21-May-2018
https://dl.acm.org/doi/10.1007/978-3-319-91458-9_22
Pradhan RBykau SPrabhakar SChirkova RYang JSuciu D(2017)Staging User Feedback toward Rapid Conflict Resolution in Data FusionProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3035941(603-618)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3035941
Li YGao JMeng CLi QSu LZhao BFan WHan J(2016)A Survey on Truth DiscoveryACM SIGKDD Explorations Newsletter10.1145/2897350.289735217:2(1-16)Online publication date: 25-Feb-2016
https://dl.acm.org/doi/10.1145/2897350.2897352
Gao JLi QZhao BFan WHan J(2015)Truth discovery and crowdsourcing aggregationProceedings of the VLDB Endowment10.14778/2824032.28241368:12(2048-2049)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.14778/2824032.2824136
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