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A Scalable Approach to Integrating Heterogeneous Aggregate Views of Distributed Databases

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

McClean, SI, Scotney, BW and Greer, KRC (2003) A Scalable Approach to Integrating Heterogeneous Aggregate Views of Distributed Databases. IEEE Transactions on Knowledge and Data Engineering, 15 (1). pp. 232-236. [Journal article]

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DOI: 10.1109/TKDE.2003.1161592

Abstract

Aggregate views are commonly used for summarizing information held in very large databases such as those encountered in data warehousing, large scale transaction management, and statistical databases. Such applications often involve distributed databases that have developed independently and therefore may exhibit incompatibility, heterogeneity, and data inconsistency. We are here concerned with the integration of aggregates that have heterogeneous classification schemes where local ontologies, in the form of such classification schemes, may be mapped onto a common ontology. In previous work, we have developed a method for the integration of such aggregates; the method previously developed is efficient, but cannot handle innate data inconsistencies that are likely to arise when a large number of databases are being integrated. In this paper, we develop an approach that can handle data inconsistencies and is thus inherently much more scalable. In our new approach, we first construct a dynamic shared ontology by analyzing the correspondence graph that relates the heterogeneous classification schemes; the aggregates are then derived by minimization of the Kullback-Leibler information divergence using the EM (Expectation-Maximization) algorithm. Thus, we may assess whether global queries on such aggregates are answerable, partially answerable, or unanswerable in advance of computing the aggregates themselves.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:46
Deposited By:Professor Sally McClean
Deposited On:23 Sep 2009 17:12
Last Modified:15 Jun 2011 11:07

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