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Truth discovery with multiple conflicting information providers on the web
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ACM Computing Surveys (CSUR)
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Data Quality Aware Queries in Collaborative Information Systems
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Incorporating Domain-Specific Information Quality Constraints into Database Queries
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Proceedings of the Sixth International Workshop on Data Management for Sensor Networks
Data fusion: resolving data conflicts for integration
Proceedings of the VLDB Endowment
Integrating conflicting data: the role of source dependence
Proceedings of the VLDB Endowment
Corroborating information from disagreeing views
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Advanced Metasearch Engine Technology
Advanced Metasearch Engine Technology
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COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
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Making better informed trust decisions with generalized fact-finding
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Truth finding on the deep web: is the problem solved?
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10th international workshop on quality in databases: QDB 2012
ACM SIGMOD Record
Truth finding on the deep web: is the problem solved?
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We are often thrilled by the abundance of information surrounding us and wish to integrate data from as many sources as possible. However, understanding, analyzing, and using these data are often hard. Too much data can introduce a huge integration cost, such as expenses for purchasing data and resources for integration and cleaning. Furthermore, including low-quality data can even deteriorate the quality of integration results instead of bringing the desired quality gain. Thus, "the more the better" does not always hold for data integration and often "less is more". In this paper, we study how to select a subset of sources before integration such that we can balance the quality of integrated data and integration cost. Inspired by the Marginalism principle in economic theory, we wish to integrate a new source only if its marginal gain, often a function of improved integration quality, is higher than the marginal cost, associated with data-purchase expense and integration resources. As a first step towards this goal, we focus on data fusion tasks, where the goal is to resolve conflicts from different sources. We propose a randomized solution for selecting sources for fusion and show empirically its effectiveness and scalability on both real-world data and synthetic data.