Heterogeneous data source integration for smart grid ecosystems based on metadata mining. (15th August 2017)

• Record Type: Journal Article
• Authors: Guerrero, Juan I.; García, Antonio; Personal, Enrique; Luque, Joaquín; León, Carlos
• Is Part Of: Expert systems with applications. Volume 79(2017)
• Page Start: 254
• Page End: 268
• Publication Date: 2017-08-15
• Subjects: Smart grids -- Large-scale integration -- Data mining -- Standards -- Metadata mining -- Big data; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.03.007
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A new technique based on metadata is proposed: metadata mining. An intelligent integration system for heterogeneous data sources is described. An adaptive data mining tool for the integrated data sources is proposed. Successful results are obtained in application in real data bases from research projects. Abstract: The arrival of new technologies related to smart grids and the resulting ecosystem of applications and management systems pose many new problems. The databases of the traditional grid and the various initiatives related to new technologies have given rise to many different management systems with several formats and different architectures. A heterogeneous data source integration system is necessary to update these systems for the new smart grid reality. Additionally, it is necessary to take advantage of the information smart grids provide. In this paper, the authors propose a heterogeneous data source integration based on IEC standards and metadata mining. Additionally, an automatic data mining framework is applied to model the integrated information.