Characterizing and selecting fresh data sources

Theodoros Rekatsinas · Xin Luna Dong · Divesh Srivastava

60 citations0/0 files storedACM SIGMOD International Conference on Management of Data (SIGMOD)

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Data Quality and Management Web Data Mining and Analysis Mobile Crowdsensing and Crowdsourcing

Abstract

Data integration is a challenging task due to the large numbers of autonomous data sources. This necessitates the development of techniques to reason about the benefits and costs of acquiring and integrating data. Recently the problem of source selection (i.e., identifying the subset of sources that maximizes the profit from integration) was introduced as a preprocessing step before the actual integration. The problem was studied for static sources and used the accuracy of data fusion to quantify the integration profit.

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References

2012A Bayesian approach to discovering truth from conflicting sources for data integration 2012Truth finding on the deep web 2009Truth discovery and copying detection in a dynamic world 2013Data Curation at Scale: The Data Tamer System 2012Less is more 2013Analyzing Civil Unrest through Social Media

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2015Finding Quality in Quantity: The Challenge of Discovering Valuable Sources for Integration

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