关键词: 模式匹配, 数据导向, 信息论模型

Abstract: The development of network has led to larger and more heterogeneous datasets. In order to employ these heterogeneous data, people usually use data integration, and schema matching is the core technology of data integration. However, these data sets are always with typical heterogeneity and may have problems such as duplicate records, missing values or lack of schema information, which lead to the inapplicability of traditional schema matching technology. For this end, this paper focuses on the study of schema matching in a situation where the schema information is unknown or incomplete, and proposes a schema matching model utilizing information theory. The model is totally based on the characteristics of the data distribution they contain, and without assumption of the existence of any external knowledge. It can compute all the similarities between columns accurately, and describe the characteristics of data distribution of each attribute column and the relations between them. This paper provides algorithms for constructing original data distribution graph and evolutive data distribution graph, which help to describe the relationship between attribute columns formally. The comprehensive experimental evaluation on real datasets verifies the feasibility and effectiveness of the proposed method.

Key words: schema matching, data-oriented, information theory model