An intelligent middleware for linear correlation discovery

  • Authors:
  • Cecil Eng Huang Chua;Roger H. L. Chiang;Ee-Peng Lim

  • Affiliations:
  • J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30303, USA;College of Business Administration, University of Cincinnati, Cincinnati, OH 45221, USA;Center for Advanced Information Systems, School of Applied Science, Nanyang Technological University, Singapore 639798, Singapore

  • Venue:
  • Decision Support Systems
  • Year:
  • 2002

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Abstract

Although it is widely accepted that research from data mining, knowledge discovery, and data warehousing should be synthesized, little research addresses the integration of existing data management analysis software. We develop an intelligent middleware that facilitates linear correlation discovery, the discovery of associations between attributes and attribute groups. This middleware integrates data management and data analysis tools to improve traditional data analysis in three perspectives: (1) identify appropriate linear correlation functions to perform based on the semantics of a data set; (2) execute appropriate functions contained in the data analysis packages; and (3) derive useful knowledge from data analysis.