Evaluation of Meta-Benchmarking Practices of Distributed Data Mining to Facilities Management

By

Ezendu I. Ariwa

Department of Accounting, Banking & Financial Systems

London Metropolitan University, UK

Mohamed M. Medhat

Sadat Academy for Management Sciences, Egypt

Introduction/Abstract

Distributed data mining [DDM] has great functionalities that can offer to nowadays applications. That is because the nature of most of these applications is data distribution. One of the potential applications for distributed data mining is the use of OIKI DDM model in Facilities Management (FM). In this paper we investigate the potential advantages of this approach.

Keywords: Information Systems [IS] – Facilities Management [FM] – Knowledge Discovery – OIKI DDM – Decision Support System [DSS] – Meta-Intelligent – Informatization – Financial Engineering [FE].

1- Introduction

There is no agreed definition on the term “Facilities Management” in the literature. However we could define it as follows: Facilities Management (FM) is an integrated approach to operating, maintaining, improving and adapting the infrastructure of an organization in order to build an environment that strongly supports the primary objectives of the organization. The FM uses information in order to accomplish its task. This information inherently distributed among a number of heterogeneous databases in different loosely coupled sites connected by a computer network [5].

Distributed data mining refers to

the mining of distributed data sets. The data sets

are stored in local databases, hosted by local com

puters, which are connected through a computer net

work. Data mining takes place at a local level and

at a global level where local data mining results are

combined to gain global findings [11].

In some applications, data are inherently dis

tributed, but it is necessary to gain global in

sights from the distributed data sets. For exam

ple, each site of a multinational company man

ages its own operational data locally, but the

data must be analyzed for global patterns to al

low company­wide activities such as planning,

marketing, and sales. One of the direct applications of distributed data mining is the use of it in FM in order to improve the decision making process.

References

[1] R. Agrawal, T. Imielinski and A. Swami. "Mining Association Rules Between Sets of Items in Large Databases", in Proc. of the ACM Int. Conf. on Management of Data, Washington, USA, May 1993.

[2] D. Chess, C. Harrison et A. Kershenbaum. Mobile Agents: Are They a Good Idea?. IBM Research Division, T.J. Watson Research Center, Yorktown Heights, New York, March 1995.

[3] Further Education Funding Council (1997) Effective Facilities Management: A Good PracticeGuide. Her Majesty's Stationery Office.
[4] E. Garcia-Lopez, Distributed Management Facilities Architecture, TINA-C baseline document TB_EGL.002_2.1_1996.
[5] Graham Goulbourne and Frans Coenen and Paul H. Leng, "KD in FM: Knowledge Discovery in Facilities Management Databases", Database and Expert Systems Applications., pp. 806-815,1998.

[6] R. Grossman, S. Bailey, S. Kasif, D. Mon, A. Ramu, and B. Malhi. Design of papyrus: A system for high performance, distributed data mining over clusters, meta-clusters and super-clusters. In Proceedings of Workshop on Distributed Data Mining, along with KDD98, Aug 1998.
[7] James E. White. Mobile Agents. In Jeffrey Bradshaw, editor, Software Agents. The MIT Press, 1996.
[8] H. Kargupta and P. Chan, editors. Advances in Distributed Data Mining. AAAI Press, 2000.
[9] Kendall, E. A., P.V. Murali Krishna, Chirag V. Pathak, C.B. Suresh, "Patterns of Intelligent and Mobile Agents," Agents '98, May, 1998.

[10] Ramu, A,T., (1998), "Incorporating Transportable Software Agents into a Wide Area High Performance Distributed Data Mining Systems", Masters Thesis, University of Illinois, Chicago, USA.
[11] M. Senousy, and M. Medhat. A proposed model for Distributed Data Mining using Mobile Agents. BIT 2001 “Constructing IS Futures,” Manchester. UK. 2001.

[12] Subramonian, R., & Parthasarathy, S. (1998). An architecture for distributed data mining. Fourth International Conference of Knowledge Discovery and Data Mining, New York, New York, Pages 44--59.
[13] Wu Z. D. and D. J. Hughes, "An Approach to Integrate Management Facilities for Campus Network Environments", Proceedings of the 1991 Signapore International Conference on Networks, pp. 131-136, September 1991.