Authors:
Sai Peck Lee
and
Lai Ee Hen
Affiliation:
Faculty of Computer Science & Information Technology, Universiti Malaya, Malaysia
Keyword(s):
Knowledge Discovery, Data Mining, Middleware, Data Mining Middleware.
Related
Ontology
Subjects/Areas/Topics:
Business Analytics
;
Communication and Software Technologies and Architectures
;
Data Engineering
;
Data Warehouses and Data Mining
;
e-Business
;
Embedded Communications Systems
;
Enterprise Information Systems
;
Software Architectures
;
Telecommunications
Abstract:
In today’s market place, information stored in a consumer database is the most valuable asset of an organization. It houses important hidden information that can be extracted to solve real-world problems in engineering, science, and business. The possibility to extract hidden information to solve real-world problems has led to increasing application of knowledge discovery in databases, and hence the emergence of a variety of data mining tools in the market. These tools offer different strengths and capabilities, helping decision makers to improve business decisions. In this paper, we provide a high-level overview of a proposed data mining middleware whose architecture provides great flexibility for a wide spectrum of data mining techniques to support decision makers in generating useful knowledge to help in decision making. We describe features that we consider important to be supported by the middleware such as providing a wide spectrum of data mining algorithms and reports through
plugins. We also briefly explain both the high-level architecture of the middleware and technologies that will be used to develop it.
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