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Articles by K. Thangavel
Total Records ( 2 ) for K. Thangavel
  S. Thenmozhi , A. Tamilarasi and K. Thangavel
  In mobile grid environment, the main challenging issues are scheduling, adaptation, security and mobility. The job scheduling problem becomes more complicated due to the limitations of node mobility. In order to minimize the resource utilization, gaining the maximum profit to be cost effective and satisfying the user constraints, an efficient job scheduling technique is required for mobile grid environment. In this study, researchers propose a fuzzy based task scheduling algorithm for resource allocation depending upon the workload and the resource availability of the grid members. In this scheduling, the computation sensitive task is assigned for grid members with least workload and the communication sensitive task is assigned for grid members with high resource availability. Using the workload and resource availability as input variables, fuzzy decision rule table is created. After defuzzification, the output gives us a perfect matching for scheduling the tasks according to the load and availability. Thus, the algorithm proves to be more effective in task scheduling of mobile grids. From the simulation results, researchers show that the proposed scheduling technique attained maximum throughput and less delay when compared with the existing technique.
  K. Thangavel and R. Rathipriya
  In this study, a biclustering algorithm based query model is proposed that is able to extract biclusters of web objects (i.e., users and pages) from web usage datasets. This Query Based Biclustering (QBB) algorithm is applied to the web usage data to recruit biclusters with respect to query which contain a certain users of similar browsing pattern across a subset of pages of a web site. By this way, one can target the right group of user for the focalized marketing strategy. In this application, the main goal is to identify group of web users or customers with similar behavior so that one can predict the customer’s interest and make proper recommendations to improve their performance. To evaluate the efficiency of the proposed algorithm, the experiment is conducted on the CTI dataset. Results show that the proposed QBB algorithm is efficient in extracting the maximum similar bicluster based on the query.
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