A Survey and Taxonomy of Distributed Data Mining Research Studies: A Systematic Literature Review

TL;DR

This paper systematically reviews the evolution and current state of Distributed Data Mining (DDM) research from 2000 to 2015, providing a taxonomy, identifying research gaps, and summarizing key trends to guide future studies.

Contribution

It offers a comprehensive taxonomy and analysis of DDM research, highlighting gaps and trends to motivate further investigation in the field.

Findings

Growth of DDM publications from 2000 to 2015

Identification of key research areas and gaps

Presentation of a taxonomy of DDM research topics

Abstract

Context: Data Mining (DM) method has been evolving year by year and as of today there is also the enhancement of DM technique that can be run several times faster than the traditional one, called Distributed Data Mining (DDM). It is not a new field in data processing actually, but in the recent years many researchers have been paying more attention on this area. Problems: The number of publication regarding DDM in high reputation journals and conferences has increased significantly. It makes difficult for researchers to gain a comprehensive view of DDM that require further research. Solution: We conducted a systematic literature review to map the previous research in DDM field. Our objective is to provide the motivation for new research by identifying the gap in DDM field as well as the hot area itself. Result: Our analysis came up with some conclusions by answering 7 research questions proposed in this literature review. In addition, the taxonomy of DDM research area is presented in this paper. Finally, this systematic literature review provides the statistic of development of DDM since 2000 to 2015, in which this will help the future researchers to have a comprehensive overview of current situation of DDM.