Download Facilities Management

Bio-Informatization and Application of
Distributed Data Mining to Facilities
Management
Ezendu I. Ariwa
Department of Accounting, Banking & Financial Systems
London Metropolitan University
United Kingdom
Mohamed M. Medhat
Sadat Academy for Management Sciences
Egypt
Introduction/Abstract
Distributed data mining 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: Facilities Management – Knowledge Discovery – OIKI DDM –
Decision support system.
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
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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 computers, which are
connected through a computer network. 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 distributed, but it is necessary to
gain global insights from the distributed data sets. For example, each site of
a multinational company manages its own operational data locally, but the
data must be analyzed for global patterns to allow company-wide 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.
2- OIKI DDM Model
Senousy and Medhat have proposed Optimized Incremental Knowledge
Integration (OIKI) DDM model, it is a mobile agent based DDM model that
overcome the drawbacks of the traditional mobile agent based DDM models.
Instead of transferring the results from each data server to the client, the
client controls migration of the results among data servers to be integrated
locally and finally, the final results are transferred to the client [11].
The typical OIKI DDM process: the client multicasts MADMs and MAKIs
(Mobile Agents-Knowledge Integrators) to the required data servers. The data
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mining process is performed locally on each data server. The size of results
of the first two accomplished DM processes are compared. The smaller one is
migrated to the larger one. The knowledge integrator agent integrates the
results of these two data servers. This process is repeated until all integrated
results are resident in a specific data server and finally, the final results are
sent back to the client. Consequently, the OIKI DDM process passes through
three main stages: 1) Preparation Stage: The client multicasts MADMs and
MAKIs to data servers. 2) Data Mining Stage: Data mining process is
performed locally on each data server. 3) Knowledge Integration Stage: An
incremental knowledge integration technique is performed on the data
servers where the smaller results are migrated to the larger one to optimize
the cost of results migration among data servers.
The client generates DM request, then it determines the required data
servers needed in the DM request and sends MADMs and MAKIs to the
specified data severs. Each data server sends size of the generated results
and the timestamp upon accomplishing the data mining process back to the
client. The client arranges the data servers’ results information in a queue
according to the timestamp of each. The client makes a comparison between
two consecutive items in the queue according to the result size and sends a
control command to the smaller result to migrate to the larger one. The
knowledge integration process takes place at the data server that has the
larger result size. The loop continues till all the results are integrated and
are resident in one data server. Finally, the data server that contains the
final results sends it back to the client.
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3- Application of OIKI DDM model to FM
A typical application of OIKI DDM model to FM that MADMs and MAKIs visit
each FM used database: fleet DB, catering DB, print DB, etc in order to
deduce the hidden knowledge from these distributed databases in the same
operation steps illustrated above. Figure (1) illustrates this application.
R1
FM used
database 1
Rn
FM used
database 2
FM used
database n
…
MADM
MAKI
MADM
MAKI
MADM
MAKI
Final
Result
FM Decision Support
Figure (1): Applying OIKI DDM to Facilities Management
From the literature, we can deduce some potential advantages of our
approach. These potential advantages could be summarized as follows:
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1- Since distributed data mining could result in a global view of the
distributed data, the effect of each information unit of an organization
FM related data could be discovered. [1, 6, 8].
2- This approach is cheaper than the data warehousing approach since
the data warehouse creation is not necessary to accomplish the OIKI
DDM process [11].
3- The use of mobile agents in this context could adapt the system to
use e-FM using Intranet within the organization [4, 13].
4- The adoption of mobile agents could adapt the system to use extranet
in order to integrate among FM of the organization and its partners [2,
4, 7,10, 12].
5- The mobile agents can roam the Internet to deduce some of the
hidden knowledge in order to make the FM decision according to
global market conditions [3, 9, 13].
4- Conclusions and Future Work
The use of OIKI DDM model in Facilities Management would increase the
efficiency of the decision support system of that sector. The potential
advantages of the model have been discussed supported from the literature.
Our future work is to apply this model on some organizations in the UK and
Egypt.
References
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