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A Method for Automatic Schema Matching Using Characteristic of Data Distribution

Li Yo

Open publisher page 4 citations

Abstract

Schema mapping has been a basic problem in many application domains, such as data integration, data ware- house, E-business. This paper introduces a schema matching method called SMDD which is based on neural network. By analyzing the characteristics of data distribution, SMDD automatically fulfills the task of schema mapping. It can be used independently or combined with other schema mapping methods to improve the accuracy of schema matching from the view of data contents.

About this research paper

What this paper is about

Schema mapping has been a basic problem in many application domains, such as data integration, data ware- house, E-business. This paper introduces a schema matching method called SMDD which is based on neural network. By analyzing the characteristics of data distribution, SMDD automatically fulfills the task of schema mapping. It can be used independently or combined with other schema mapping methods to improve the accuracy of schema matching from the view of data contents.

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OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Schema mapping has been a basic problem in many application domains, such as data integration, data ware- house, E-business. This paper introduces a schema matching method called SMDD which is based on neural network. By analyzing the characteristics of data distribution, SMDD automatically fulfills the task of schema mapping. It can be used independently or combined with other schema mapping methods to improve the accuracy of schema matching from the view of data contents.

Key concepts: Schema matching, Computer science, Star schema, Schema (genetic algorithms), Conceptual schema, Data integration, Semi-structured model, Data mining

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