Detecting Broken Mappings for Deep Web Integration

The deep Web integration system employs a set of semantic mappings between the mediated schema and the schemas of data sources. In this dynamic distributed environment, sources often undergo changes that invalidate the mappings. Such continuous monitoring is extremely labor intensive, and poses a key bottleneck to the widespread deployment of data integration systems in practice. The paper describes detecting broken mappings based on fuzzy reasoning (we called DBMFR in short), an automatic solution to detecting broken mappings, which can highly improve the reliable of the data integration system. At the heart of DBMFR is a set of computationally inexpensive modules called sensors, which capture salient characteristics of data sources, like Maveric system. We develop two novel improvements: disjunction-weighted average operators are leveraged to calculate the score, which implies whether the mapping is broken and change weight operators used to combine artificial data with real data in training phase. Experiments over real-world sources demonstrate the effectiveness of our fuzzy-based approach over existing solutions, as well as the utility of our improvements.