22-11-2012, 03:36 PM
Data Grid Mining of Mobile User Behaviors in Web Environments
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Abstract
Mobile E-Commerce provides location-based services to mobile users in web
environments. One of the best ways to personalize mobile services is based on location. In
this paper, we propose a new algorithm called the Distributed Pattern Miner (DPM), for
mining location-aware service request patterns from distributed databases on a Data Grid.
The location and service request patterns represent frequently requested services and the
corresponding location of mobile users in mobile web environments. These patterns are
used to predict the next location of mobile users and the service requests in the future. The
grid provides an effective computational and communicational support for distributed data
mining applications. We built a data grid system on a cluster of workstations using an open
source Globus Toolkit (GT). We have compared the performance of the existing
conventional distributed mining algorithm with the DPM in a grid environment, in terms of
computation time. The experimental result shows that the DPM gives a better performance
than other sequential and conventional distributed algorithms.
Introduction
The rapid development of wireless and World Wide Web (WWW) technologies enables mobile users
to request various kinds of services via mobile devices, while moving across different locations. A
mobile user may submit a service request, e.g. “find the nearby medical shops” at location A. Then he
can move to another location B, where he can submit a service request, “find the restaurants nearby”.
The mobile user can submit various kinds of service requests in different locations. In a mobile web
environment, the mobile users' service requests and location logs are accumulated as a large data set in
a distributed database [1], [2]. These databases need to be analyzed to generate location aware service
patterns using data mining techniques. In this regard, predicting the behavior of mobile users in terms
of location and service request must be required for efficient service provision, which is the main
objective of this paper.
Wireless technology is combined with internet enabled devices to access business related
services [3]. Location and service request prediction is a dynamic strategy in which the system
estimates the mobile user's behavior based on a user movement model.
Related Works
An efficient algorithm for mining an association rule is the Apriori, first proposed by Agrawal et al [6],
[7]. A number of further studies were done based on the Apriori algorithm. The paper [6] deals with an
algorithm for generating association rules on a large database. Some studies were made on data mining
techniques to predict the location of mobile users in a web environment [8]. In [9], a data mining
algorithm is executed on a centralized server to find the mobile user’s location aware service request
patterns. If the source log file is very large, a centralized algorithm will lead to problems in data
communication, process overhead and security. So, mining data sets in distributed sites is an efficient
method. Some studies have been done on distributed data mining for association rule mining [10]. The
existing conventional distributed data mining system does not have the capability to achieve a better
performance.
Distributed Data Mining on the Data Grid System
In this paper, we have developed a Data Grid system with distributed data bases, using an open source
Globus Toolkit 4 [21], and implemented a new distributed two dimensional associational rule mining
algorithm named as the Distributed Pattern Miner. The mining algorithm is developed to generate a
mobile user’s location-aware service request patterns from the database distributed in a data grid. Fig.1
shows the architecture of the data mining system using data grid services.