Global Data Fusion versus Local Pattern Fusion in Mining Multiple Databases: A Comparative Review

Abstract

With the emergence of big data, mining distributed databases has become a critical task in the domain of discovery of knowledge from databases. Many of the traditional multiple-database mining methods developed until now have emphasized mining the mono-database, which is a pool of all the local databases merged at a central site; local patterns discovered at local sites are not analyzed in mono-database mining. However, in real-world applications, data collected from multiple databases may be duplicitous and unreliable. Therefore, developing methods to discover reliable, high-quality knowledge from multiple databases is a challenging task when mining multi-sourced data. This paper scrupulously reviews all the existing methods for mining multiple and distributed databases based on global data fusion and local pattern fusion techniques. The research issues and recently developed methods, which involves local pattern analysis in multi-database mining, are also discussed.