Data fusion - Resolving Data Conflicts for Integration

The amount of information produced in the world increases by 30% every year and this rate will only go up. With advanced network technology, more and more sources are available either over the Internet or in enterprise intranets. Modern data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, often require integrating available data sources and providing a uniform interface for users to access data from different sources; such requirements have been driving fruitful research on data integration over the last two decades [13, 15]. Data integration systems face two folds of challenges. First, data from disparate sources are often heterogeneous. Heterogeneity can exist at the schema level, where different data sources often describe the same domain using different schemas; it can also exist at the instance level, where different sources can represent the same real-world entity in different ways. There has been rich body of work on resolving heterogeneity in data, including, at the schema level, schema mapping and matching [17], model management [1], answering queries using views [14], data exchange [10], and at the instance level, record linkage (a.k.a., entity resolution, object matching, reference linkage, etc.) [9, 18], string similarity comparison [6], etc. Second, different sources can provide conflicting data. Conflicts can arise because of incomplete data, erroneous data, and out-of-date data. Returning incorrect data in a query result can be misleading and even harmful: one may contact a person by an out-of-date phone number, visit a clinic at a wrong address, carry wrong knowledge of the real world, and even make poor business decisions. It is thus critical for data integration systems to resolve conflicts from various sources and identify true values from false ones. This problem becomes especially prominent with the ease of publishing and spreading false information on the Web and has recently received increasing attention. This tutorial focuses on data fusion, which addresses the second challenge by fusing records on the same real-world