论文标题
分布式数据挖掘研究的调查和分类学研究：系统文献综述
A Survey and Taxonomy of Distributed Data Mining Research Studies: A Systematic Literature Review
论文作者
论文摘要
上下文：数据挖掘（DM）方法已经逐年发展，截至今天，DM技术的增强也可以比传统的数据挖掘（DDM）快几倍。实际上，这并不是数据处理的新领域，但是近年来，许多研究人员一直对这一领域更加关注。问题：高声誉期刊和会议中有关DDM的出版物数量已大大增加。研究人员很难获得需要进一步研究的DDM的全面视图。解决方案：我们进行了系统的文献综述，以绘制DDM领域的先前研究。我们的目标是通过确定DDM领域以及热门区域本身的差距来提供新研究的动力。结果：我们的分析得出了一些结论，回答了本文综述中提出的7个研究问题。此外，本文介绍了DDM研究领域的分类学。最后，这项系统的文献综述提供了自2000年至2015年以来DDM开发的统计量，这将有助于未来的研究人员全面概述DDM的当前状况。
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.
