NDLI: Detecting Broken Mappings for Deep Web Integration

Content Provider	IEEE Xplore Digital Library
Author	Jia-Jia Miao Kai Du Ai-Ping Li Yan Jia
Copyright Year	2007
Description	Author affiliation: Nat. Univ. of Defense Technol., Changsha (Jia-Jia Miao; Kai Du; Ai-Ping Li; Yan Jia)
Abstract	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.
Starting Page	56
Ending Page	61
File Size	364595
Page Count	6
File Format	PDF
ISBN	0769530079
DOI	10.1109/SKG.2007.47
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Condition monitoring Computer science Heart Costs Computerized monitoring Distributed databases Sensor phenomena and characterization Spatial databases Sensor systems Fuzzy reasoning
Content Type	Text
Resource Type	Article

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