An intelligent middleware for linear correlation discovery

• Main Authors: CHUA, Cecil, CHIANG, Roger Hsiang-Li, LIM, Ee Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2002
• Subjects: Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/57; https://ink.library.smu.edu.sg/context/sis_research/article/1056/viewcontent/1_s2.0_S0167923601001270_main.pdf
• Institution: Singapore Management University

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 and 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.