ARCHITECTURE-CENTRIC DATA MINING MIDDLEWARE SUPPORTING MULTIPLE DATA SOURCES AND MINING TECHNIQUES

Sai Peck Lee, Lai Ee Hen

2007

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.

References

  1. Cheng Soon Ong, 2000. Knowledge Discovery In Databases: An Information Retrieval Perspective, Malaysian Journal of Computer Science. Vol. 13 No. 2. pp. 54-63
  2. Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P., 1996. From Data Mining to Knowledge Discovery: An Overview. Advances in Knowledge Discovery and Data Mining. MIT Press. 37-54.
  3. Ian Foster, Carl Kesselman, and Steven Tuecke, 2001. The Anatomy of the Grid: Enabling Scalable Virtual Organizations. 1-25.
  4. Jiawei Han and Micheline Kamber, 2006. Data Mining: Concepts and Techniques. Second Edition. Elsevier. p5 - 45
  5. Michael Goebel, and Le Gruenwald, 1999. A Survey Of Data Mining And Knowledge Discovery Software Tools, Sigkdd Explorations. ACM SIGKDD. Volume 1, Issue 1 - 20 - 33
  6. Peter M. Chen and David A, 1993. Storage Performance-Metrics and Benchmarks. Patterson. Volume 81. 1-33.
  7. Predictive Model Markup Language (PMML). 2005 Technology Reports. Cover Pages. http://xml.coverpages.org/pmml.html. Retrieved August 8, 2005
  8. Sanjiv Purba, 2006. Handbook of Data Management. Viva Books Private Limited.
Download


Paper Citation


in Harvard Style

Peck Lee S. and Ee Hen L. (2007). ARCHITECTURE-CENTRIC DATA MINING MIDDLEWARE SUPPORTING MULTIPLE DATA SOURCES AND MINING TECHNIQUES . In Proceedings of the Second International Conference on Software and Data Technologies - Volume 3: ICSOFT, ISBN 978-989-8111-07-4, pages 224-227. DOI: 10.5220/0001326102240227


in Bibtex Style

@conference{icsoft07,
author={Sai Peck Lee and Lai Ee Hen},
title={ARCHITECTURE-CENTRIC DATA MINING MIDDLEWARE SUPPORTING MULTIPLE DATA SOURCES AND MINING TECHNIQUES},
booktitle={Proceedings of the Second International Conference on Software and Data Technologies - Volume 3: ICSOFT,},
year={2007},
pages={224-227},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001326102240227},
isbn={978-989-8111-07-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Software and Data Technologies - Volume 3: ICSOFT,
TI - ARCHITECTURE-CENTRIC DATA MINING MIDDLEWARE SUPPORTING MULTIPLE DATA SOURCES AND MINING TECHNIQUES
SN - 978-989-8111-07-4
AU - Peck Lee S.
AU - Ee Hen L.
PY - 2007
SP - 224
EP - 227
DO - 10.5220/0001326102240227