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Title: Exploring the power of source reliability in information integration Authors:  Houping Xiao - Robinson College of Business/GSU (United States) [presenting]
Shiyu Wang - University of Georgia (United States)
Abstract: In the era of Big Data, data entries, even describing the same objects or events, can come from a variety of sources. There are some sources that typically provide accurate information, but due to various reasons such as recording errors, device malfunction, background noise, or even intent to manipulate the data, some other sources may contain noisy or even erroneous information. Therefore, during information integration, it is critical to identify reliable sources that more often provide accurate information. Unfortunately, there is no oracle telling us which information source is more reliable a priori. Novel information integration methods are developed that incorporate the estimation of source reliability in both data-level and model-level information integration. In both works, we prove some nice properties of the proposed approaches via theoretical analysis and demonstrate their impact on some real applications such as indoor floorplan construction and crowdsourced question answering.