Distributed computing environment: Approaches and applications

Abstract

In information systems in particular BusinessIntelligence systems, for data produced at physically distributed locations most traditional data mining approaches require data to be transmitted to a single location for centralized processing and mining. However, the continual transmission of a large number of data to a central location must be impractical and expensive. Thus, distributed and parallel data mining algorithms and applications were rapidly developed. The paper surveys the-state-of-the art in approaches and applications of distributed computing environment. The goal is to summarize a brief introduction to this field with pointers for further exploration.

Distributed data mining: a survey

“…Bandwidth limitation and privacy concerns are also the factors to hinder data centralization. To solve the above problems, Distributed Data Mining (DDM) has become a hot research area [34,61,62]. Distributed data mining has become popular as business intelligence market is one of fastest growing and most profitable areas in software industry.…”