Download Information Retrieval through Multi-Agent System

ISSN:2229-6093
Vishal Jain et al,Int.J.Comp.Tech.Appl,Vol 3 (1), 62-66
Information Retrieval through Multi-Agent System with Data Mining in
Cloud Computing
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Abstract
Problem solving solutions like Multi-Agent
System (MAS) capitalizes on its multiple
intelligent agents to receive precepts from
the environment, process the information
and produce the desired result for the
environment. The wide scope of capabilities
of an MAS permits the user to resolve
functional, methodic, algorithmic or
procedural query to explore and process the
data. MAS are also referred as autonomous
agents having the capability to resolve
problems that are not possible for a single
agent to handle.
The aim of this research paper is to
develop a practically implemented research
model for the information retrieval using
Multi-Agent System with Data Mining
technique in a Cloud Computing
environment. The paper will undertake a
review of the existing literature available on
this arena and develop an empirical model
showing real time data flow through MAS
with data mining after retrieval of
meaningful
information
from
data
warehouse present in a cloud computing
environment. In the end, paper will provide
recommendations for the organizations for
effective implementation and use.
Keywords: Information Retrieval, MultiAgent System, Data Mining, Cloud
Computing
Introduction
Cloud Computing is a general term that
refers to anything that “involves delivering
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hosted services over the Internet. Broadly it
is characterized into three categories,
namely: Software-as-a-Service (SaaS),
Infrastructure-as-a-Service
(IaaS)
and
Platform-as-a-Service
(PaaS)”
(„CloudComputing‟, 2007). Multi-Agent
System is a problem solving “system
composed of multiple interacting intelligent
agents” (Kent, Kobti, Snowdon and
Aggarwal, 2010).Data Mining means
“Discovery” of new knowledge that was not
known before. It is “the analysis step in the
Knowledge Discovery and Databases
process” (Nodine, Ngu, Cassandra and
Bohrer, 2003).Data Warehouse refers to a
data store which involves three stages,
namely: staging, integration and access for
reporting and analysis purposes.
Related Works
In the present age and knowledge economy,
discovering new knowledge and retrieving
information from a data center from a cloud
environment is a difficult aspect. The
concept of cloud computing does not
provide facilities for the knowledge
discovery and information retrieval.
Furthermore, it is required that the so-called
knowledge discovery should be in harmony
with the structure, schema and architecture
of that knowledge. The emerging knowledge
cloud is considered insufficient to retrieve
information effectively and thus, Chang,
Yang and Luo (2011) undertook a study to
propose
"an
ontology-based
agent
generation framework for information
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Vishal Jain et al,Int.J.Comp.Tech.Appl,Vol 3 (1), 62-66
retrieval in a flexible, transparent and easy
way on cloud environment” (p.1135). They
proposed a framework for information
retrieval in which the user will submit "a
flat-test based” request to retrieve
“information on a cloud environment”, the
request will be “deduced by a Reasoning
Agent automatically that is according to a
predefined ontology and a reasoning rule
and then translated to a Mobile Information
Retrieving
Agent
Description
File
(MIRADF) that is formed in a proposed
Mobile Agent Description Language
(MADF)" (Chang, Yang & Luo, 2011;
p.1136). MIRA-GA, a generating agent will
generate MIRA in accordance with
MIRADF.
Nodine, Ngu, Cassandra and Bohrer
(2003) studied InfoSleuth TM system that is a
unique discovers and retrieves information
in real time and open environments.
Primarily, this system is based on MAS.
InfoSleuthTM has a multi-broker function
which interacts with syntax and semantics in
MAS domain. A broker has to decide
through the advertised information regarding
the qualities of each agent, which agent is
best for a particular service. Thus,
InfoSleuthTM provides a brokering system
where brokers can share and receive
information regarding other brokers and
non-brokers to decide with which broker to
finalize the deal.
Lai, Chang, Hu, Huang and Chao
(2011) used data mining technique by
analyzing large-scale data and obtaining
statistical answers from it regarding
suggestions for TV programs. The study
weighed these figures through various
techniques of data mining regarding
preferences of viewers of TV programs in a
particular area. The authors found that this
process involved large amount of data,
computer power problems and scale as the
major bottlenecks in the effective
implementation of the techniques in finding
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the most viewed TV programs. They
suggested an architecture involving cloud
computing and "a map-reduced framework,
map-reduce version of k-means and the knearest neighbor (kNN) algorithm is
applied" (Lai, Chang, Hu, Huang and Chao
2011; p. 124).
Literature Review
The contemporary research available on the
concept of cloud computing, multi-agent
system, data mining and data warehouse
revolves around the identification of
bottlenecks in existing data
flow
frameworks
and
introducing
new
frameworks that overcome these problems.
The extensive data available regarding these
concepts highlight the wide scope of cloud
computing and multi-agent systems when
used with data mining techniques for
information retrieval.
Manvi and Venkataran (2004)
postulated that software agents, sub category
of MAS, are popularly used communications
specifically the mobile agent technology.
The authors found that MAS has flexible
operations, easy to use, adaptable to real
environments and wide reliance on the
concepts
of
software
engineering.
InfoSleuthTM and other relative systems had
issues regarding Agents-Communication
Language (ACL) and other knowledge
domains (Nodine & Chandrasekara, 1999).
These systems lacked the capabilities to
satisfy the emerging needs of the
environment with the expansion of the scope
of MAS application and increase in its
complexity. The existing MAS based
information discovery and retrieval systems
became insufficient in terms of information
security, information transfer and the usage
of information on multimedia.
Kent, Kobti, Snowdon and Aggarwal
(2010) studied unified data management and
decision support system (UDMDSS) in the
light of health care. They developed a
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Vishal Jain et al,Int.J.Comp.Tech.Appl,Vol 3 (1), 62-66
system that is founded on a modular
architecture that supports semantic data
models, queries, Bayesian statistical
analysis, data acquisition through mobile,
artificial intelligence for simulation and
modelling in health care and web-base
desktop in real time environments apart
from various other features. They focused
on the Canadian hospital and children safety
from car accidents.
Multi-Agent Systems are autonomous at
least partially. No one intelligent agent has a
complete view of the entire system due to
complexity of the system that limits the use
of knowledge completely by a single agent.
Furthermore, there is no defined control
system that forms it a monolithic system.
Data mining on the other hand,
automatically or semi-automatically studies
large number of data to find similar patterns
that are previously unknown. In most cases
the patterns found by data mining technique
are processed through decision support
systems to further analyze the data for
multiple purposes and uses. Cloud
computing is centralized that makes it less
costly, convenient, high utility, scalability,
agility, reliability, performance and security
of data. There are five layers in cloud
computing which are equally effective to
share information once the Internet has
established connection with two or more
computers, namely: client, application,
platform, infrastructure and server. There is
a public, community, hybrid and a private
cloud which have advantages as well as
disadvantages.
The
integrated
data
warehouses integrate and collect data from
various areas of the business at one point for
the users to analyze them and benefit from
the required information of choice.
Methodology
The research paper adopted a qualitative
research method based on the secondary
data collection activities. The preliminary
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use of knowledge available on the cloud
computing, data mining, data warehouses
and multi-agent system assisted in the
formation of a new research model with
practical significance and utility. The peerreviewed journal articles, books and
periodicals provided a concrete foundation
for the invention of a new model for
information retrieval. This justifies the
adoption of qualitative research method
using secondary data collection technique.
Findings and Summary
The study of the related works and
literature review that in order to retrieve
meaningful information from the data
warehouse through the help of a multi-agent
system and data mining techniques in a
cloud computing environment, the following
architecture is designed:
● The
Infrastructure-as-a-Service
(IaaS) provides a virtual environment
with storage and network without
having physical hardware. Therefore,
Infrastructure
cloud
computing
provides a data warehouse for
storage of data for further analysis.
● The user will submit a flat-text based
request on the IaaS for information
retrieval from its integrated data
warehouse that has gathered data
from numerous areas of business to
present to the user a wide variety to
choose from.
● The IaaS will forward this request to
the MAS to find the information as
requested.
● However, MAS does not have the
ability to find the information that
has large amounts of data from a
data warehouse. Therefore, it will
use the data mining algorithm to
analyze the large amount of data
from the data warehouse that is
residing in the IaaS.
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Vishal Jain et al,Int.J.Comp.Tech.Appl,Vol 3 (1), 62-66
● As a pre-processing stage, the MAS
will first develop a target data set
which will be large enough to
contain all the possible data patterns
and send it into the system. Then the
processing will begin where the data
will be analyzed through anomaly
detection, clustering, classification,
regression and summarization.
● The result of the process will be
shown on the screen to the user.
Figure 1: “Information Retrieval through Multi-Agent System with Data Mining in
Cloud Computing”
Conclusion and Recommendations
The information retrieval practical model
through the multi-agent system with data
mining in a cloud computing environment
has been proposed. It is however,
recommended that users should ensure that
the request made to the IaaS is within the
scope of integrated data warehouse and is
clear and simple. Thus, making the work for
the multi-agent system easier through
application of the data mining algorithms to
retrieve meaningful information from the
data warehouse. In this proposed research
model/architecture, the use of cloud
computing allows the users to retrieve
meaningful information from virtually
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integrated data warehouse that reduces the
costs of infrastructure and storage.
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