22-12-2012, 04:36 PM
Mining and Prediction of Mobile User Behavior in Location Based Service Environment
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Abstract
The services which are provided to the wireless mobile devices (such as PDAs, Cellular Phones, and Laptops) from anywhere, at any time using ISAP (Information Service and Application Provider) are enhanced by mining and prediction of mobile user behaviors.But such discovery may not be precise enough for predictions since the differentiated mobile behaviors among users and temporal periods are not considered simultaneously in the previous works. User relations and temporal property are used simultaneously in this work. Prediction strategy is used to predict the subsequent mobile behavior.
Here CTMSP-Mine (Cluster-based Temporal Mobile Sequential Pattern - Mine) algorithm is used to mine CTMSPs. In CTMSP-Mine requires user clusters, which are constructed by Cluster-Object-based Smart Cluster Affinity Search Technique (CO-Smart-CAST) and similarities between users are evaluated by Location-Based Service Alignment (LBS-Alignment) to construct the user groups. The temporal property is used by time segmenting the logs using time intervals. The specific time intervals to segment the huge data logs are found using Genetic Algorithm based method called GetNTSP (Get Number of Time Segmenting Points). The user cluster information resulting from CO-Smart-CAST and the time segmentation table are provided as input to CTMSP-Mine technique, which creates CTMSPs.
INTRODUCTION
The advancement of wireless communication techniquesand the popularity of mobile devices such as mobilephones, PDA, and GPS-enabled cellular phones, havecontributed to a new business model. Mobile users can request services through their mobile devices via InformationService and Application Provider (ISAP) from anywhereat any time. This business model is known asMobile Commerce (MC) that provides Location-BasedServices (LBS) through mobile phones.
MC is expectedto be as popular as e-commerce in the future and it isbased on the cellular network composed of several basestations. The communication coverage of each base stationis called a cell as a location area. The average distance between two base stations is hundreds of meters and thenumber of base stationsare usually more than 10,000 in acity. When users move within the mobile network, theirlocations and service requests are stored in a centralizedmobile transaction database.
Analysis
Existing System
In a mobile network consisting cells with a base
station for each, users of wireless mobile devices move from
one location to another in a random manner. The mobile
users are served by ISPs and ISAP to access the World Wide
Web, to get necessary information in their daily life. When
user’s movement and their service requests are predicted in
advance, it helps to provide customized and efficient service
to the users. Efficiency is increased to help mobile users
experience the usage of web applications and web pages as
if they access from a PC. The Existing system for prediction
uses the moving paths of users or the time a user requests for
a service. This system does not consider groups of users in
mining, but it considers only individual users. This did not
provide efficient Prediction of mobile user behavior and it
consumes more time to predict and also it lacks in accuracy.
Therefore a new system is proposed to solve the problems in
prediction.
Overview of the Project
The services which are provided to the wireless mobile devices (such as PDAs, Cellular Phones, and Laptops) from anywhere, at any time using ISAP (Information Service and Application Provider) are enhanced by mining and prediction of mobile user behaviors. This business model of mobile services is referred as Mobile Commerce.
Mining and prediction of mobile movements and associated transactions is the core of the project.The project focuses on discovering mobile patterns from the whole logs. But such discovery may not be precise enough for predictions since the differentiated mobile behaviors among users and temporal periods are not considered simultaneously in the previous works. User relations and temporal property are used simultaneously in this work to provide more accuracy, and scalability.
Prediction strategy is used to predict the subsequent mobile behavior. Here CTMSP-Mine (Cluster-based Temporal Mobile Sequential Pattern - Mine) algorithm is used to mine CTMSPs. In CTMSP-Mine requires user clusters, which are constructed by Cluster-Object-based Smart Cluster Affinity Search Technique (CO-Smart-CAST) and similarities between users are evaluated by Location-Based Service Alignment (LBS-Alignment) to construct the user groups.
Future Enhancements
When the required service is not provided in a location, the user details are registered, so that the next time when the user enters, the service is provided as required by the user. For example, when the mobile user enters a specific location and surfs for the information about the nearest Library. When the user is not serviced with the required information, the user’s details are logged and registered. Later, when the user enters the same location, he is identified by his registered details. The service is provided efficiently as required by the user.
Activating prioritization, so that it is possible to provide priorities for selected users among the complex user behavior. Huge number of users utilize the mobile services every day. Some users access specific services frequently. Such users are prioritized over other mobile users. Those prioritized services help to satisfy the needs of mobile users completely.