Intelligent data integration from heterogeneous relational databases containing incomplete and uncertain information

Abstract

The integration of incomplete and uncertain information has emerged as a crucial issue in many application domains, including data warehousing, data mining, data analysis, and artificial intelligence. This paper proposes a novel approach of mediation-based integration for integrating these types of information from heterogeneous relational databases. We present in detail the different processes in the layered architecture of the proposed flexible mediator system. The integration process of our mediator is based on the use of fuzzy logic and semantic similarity measures for more effective integration of incomplete and uncertain information. We also define fuzzy views over the mediator’s global fuzzy schema to express incomplete and uncertain databases and specify the mappings between this global schema and these sources. Moreover, our approach provides intelligent data integration, enabling efficient generation of cooperative answers from similar ones, retrieved by queried flexible wrappers. These answers contain information that is more detailed and complete than the information contained in the initial answers. A thorough experiment verifies our approach improves the performance of data integration under various configurations.

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