MSMiner-a developing platform for OLAP

Authors:
Zhongzhi Shi;Youping Huang;Qing He;Lida Xu;Shaohui Liu;Liangxi Qin;Ziyan Jia;Jiayou Li;Huijing Huang;Lei Zhao
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China and Graduate School of the Chinese Academy of Sciences, Beijing 100039, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China and Graduate School of the Chinese Academy of Sciences, Beijing 100039, China and Department of Information Te ...;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China and Graduate School of the Chinese Academy of Sciences, Beijing 100039, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China and Graduate School of the Chinese Academy of Sciences, Beijing 100039, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China and Graduate School of the Chinese Academy of Sciences, Beijing 100039, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China
Venue:
Decision Support Systems
Year:
2007

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Abstract

Since the early 1970s, decision support systems (DSS) have evolved significantly. In this paper, the design and implementation of MSMiner, a developing platform for DSS, is introduced. The system is constructed on a data warehouse and integrated with a number of data mining algorithms. It is well suited for on-line analytical processing (OLAP). The characteristics of MSMiner include the ability to support multiple data sources and data mining strategies, additional organizational flexibility in regard to data and mining strategies, and the powerful expansibility of data mining tasks.