Hierarchical Clustering Model for Automatic OLAP Schema Generation
Muhammad Usman, Sohail Asghar, Simon Fong
Journal of E-Technology, Vol. 2, No. 1, 2011.
DOI: 
Link: http://www.dline.info/jet/fulltext/v2n1/2.pdf
Abstract:
The concept is viewed as an automated application of algorithms to discover hidden patterns and to extract knowledge from data. For querying huge datasets in interactive way, the Online Analytical Processing (OLAP) systems, are used. These OLAP systems are the predominant front-end tools used in data warehousing environments and the OLAP systemâ€™s market has developed rapidly during the last few years. The integration of OLAP and data mining offer promising results. Currently many studies on 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. Scanning of earlier stuides shown that in the past no integration of automatic schema generation with data mining was carried out. In this work, we propose a model for data mining and automatic schema generation of three types namely star, snowflake, and galaxy and also extend the work further by validation with the help of experiments. We found that the model we have generated is more important as it supports both integration and automation process.