Abstract
Problem statement: Mobility prediction is the important issue in Personal Communication Systems (PCS). Mobile users moving logs are stored in data grid located in different locations. Distributed data mining algorithm is applied on this moving logs to generate the mobility pattern of mobile users. As new moving logs are added to the data grid, existing mobility pattern becomes invalid and it should be updated. One of the existing work to derive the new mobility pattern is re-executing the algorithm from scratch results in excessive computation. Approach: We had designed new incremental algorithm by maintaining infrequent mobility patterns, which avoids unnecessary scan of full database. Incremental data mining algorithm taken lesser time to compute new mobility patterns. The discovered location patterns can be used to provide various location based services to the mobile user by the application server in mobile computing environment. Data grid provided geographically distributed database for computational grid which implements incremental data mining algorithm. We built data grid system on a cluster of workstation using open source globus toolkit 4.0 and Message Passing Interface extended with Grid Services (MPICH-G2). Results: The experiments were conducted on original data sets and data were added incrementally and the computation time was recorded for each data sets. The performance improvement for increment size of 100 K was about 55% for 0.20% support count and it is increased to 60% for 0.25% support count. The performance is increased about 65% for the support count 0.30%. Conclusion: We analyzed our results with various sizes of data sets and the proof shows the time taken to generate mobility pattern by incremental mining algorithm is less than re-computing approach. In future the execution time can further be reduced by balancing the workload of grid nodes.
Highlights
Data grid is designed to allow large moving logs to be stored in repositories
The transactions in the database are considered as original database and datasets are added incrementally for three runs of algorithm to show the performance of Incremental Mining algorithm (IM) over Re-Computing algorithm (RC)
We have proposed incremental parallel and distributed algorithm implemented on Knowledge grid to predict the location of mobile user in a mobile web computing system
Summary
Data grid is designed to allow large moving logs to be stored in repositories. In business area it is necessary to develop environment for analysis, inference and discovery over the data grid. The evolution of the data grid is represented by knowledge grid offering high level services for distributed mining and extraction of knowledge from data repositories available on data grid[1]. The Knowledge Grid (KG) is a parallel and distributed architecture that integrates data mining techniques and grid technologies[4]. The knowledge grid is exploited to perform distributed data mining on very large data sets available over grids to find hidden valuable information, process models to make decisions and results to make business decisions[5,8]. In present study knowledge grid is developed to predict the location of mobile user in mobile environment. By using the predicted location, the system effectively allocate resources to mobile users in the neighbor location and it is possible to answer the queries that refer to the future position of mobile users
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