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Data mining and automatic OLAP schema generation
Usman, Muhammad1; Asghar, Sohail2; Fong, Simon3
Conference Name2010 Fifth International Conference on Digital Information Management (ICDIM)
Source Publication2010 5th International Conference on Digital Information Management, ICDIM 2010
Conference Date5-8 July 2010
Conference PlaceThunder Bay, ON, Canada
Author of SourceIEEE Computer Society

Data mining aims at extraction of previously unidentified information from large databases. It can be viewed as an automated application of algorithms to discover hidden patterns and to extract knowledge from data. Online Analytical Processing (OLAP) systems, on the other hand, allow exploring and querying huge datasets in interactive way. These OLAP systems are the predominant front-end tools used in data warehousing environments and the OLAP system's market has developed rapidly during the last few years. Several works in the past emphasized the integration of OLAP and data mining. More recently, data mining techniques along with OLAP have been applied in decision support applications to analyze large data sets in an efficient manner. However, in order to integrate data mining results with OLAP the data has to be modeled in a particular type of OLAP schema. An OLAP schema is a collection of database objects, including tables, views, indexes and synonyms. Schema generation process was considered a manual task but in the recent years research communities reported their work in automatic schema generation. In this paper, we reviewed literature on the schema generation techniques and highlighted the limitations of the existing works. The review reveals that automatic schema generation has never been integrated with data mining. Hence, we propose a model for data mining and automatic schema generation of three types namely star, snowflake, and galaxy. Hierarchical clustering technique of data mining was used and schema from the clustered data was generated. We have also developed a prototype of the proposed model and validated it via experiments of real-life data set. The proposed model is significant as it supports both integration and automation process. ©2010 IEEE.

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Document TypeConference paper
Affiliation1.Auckland University of Technology, Auckland, New Zealand;
2.Mohammad Ali Jinnah University, Islamabad, Pakistan;
3.University of Macau, Taipa, China
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GB/T 7714
Usman, Muhammad,Asghar, Sohail,Fong, Simon. Data mining and automatic OLAP schema generation[C]//IEEE Computer Society,2010:35-43.
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