Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values, and different sources can often provide conflicting values. To present quality data to users, it is critical to resolve conflicts and discover values that reflect the real world; this task is called data fusion. Typically, we expect a true value to be provided by more sources than any particular false one, so we can take the value provided by the largest number of sources as the truth. Unfortunately, a false value can be spread through copying and that makes truth discovery extremely tricky. In this chapter, we consider how to find true values from conflicting information when there are a large number of sources, among which some may copy from others. We describe a novel approach that considers copying between data sources in truth discovery. Intuitively, if two data sources provide a large number of common values and many of these values are unlikely to be provided by other sources (e.g., particular false values), it is very likely that one copies from the other. We apply Bayesian analysis to decide copying between sources and design an algorithm that iteratively detects dependence and discovers truth from conflicting information.We also consider accuracy of data sources and similarity between values in fusion to further improve the results.We present a case study on real-world data showing that the described algorithm can significantly improve accuracy of truth discovery and is scalable when there are a large number of data sources.
|Title of host publication||Handbook of Data Quality|
|Subtitle of host publication||Research and Practice|
|Publisher||Springer Berlin Heidelberg|
|Number of pages||26|
|Publication status||Published - 1 Jan 2013|
ASJC Scopus subject areas
- Computer Science(all)