Open AccessPower Data Integration Method Based on Database-table Metadata Semantic
Author(s) -
Lei Zheng,
Jianhong Pan,
Kai Zhang
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2179/1/012028
Subject(s) - metadata , computer science , data element , jaccard index , metadata repository , semantic grid , data mining , data integration , information retrieval , table (database) , semantic integration , semantic similarity , data mapping , meta data services , database , similarity (geometry) , matching (statistics) , semantic computing , semantic web , cluster analysis , artificial intelligence , world wide web , mathematics , statistics , image (mathematics)
Aiming at the characteristic of power data with multiple sources, wide distribution and complex association, this paper proposes a power data integration method based on Database-Table Metadata Semantic (DTMS), which can realize the integration of grid data at the metadata layer. Firstly, based on the structure of the database, the semantic expression is established between database and table; Secondly, this paper combines the Jaccard distance similarity method and the edit distance similarity method to achieve semantic matching between metadata and technical terms, which can solve the shortcomings of the single similarity method. Finally, according to the characteristics of power data assets, a power data integration scheme based on DTMS is proposed for multi-source power data integration. Through experiments, the accuracy of the algorithm in this paper has reached 95.8%.