Earticle

Interoperability between legacy systems is essential for various processes to be effectively operated in terms of cooperation in business. A process, however, may include various queries, and thus data interoperability based on queries might not be properly executed due to problems of heterogeneousness. This paper proposes a cooperation system for the business process based on XMDR in Grid. The proposed system solves the problem of heterogeneousness that may take place regarding interoperability of queries in a XMDRbased business process. Heterogeneousness in an operation of a business process may involve metadata collision, schema collision, or data collision. This can be handled by operating a business process by making use of XMDR-based Global Query and Local Query.

This paper presents a parallel hybrid evolutionary algorithm executed in a grid environment. The algorithm executes local searches using Simulated Annealing within a Genetic Algorithm to solve the Job Shop Scheduling Problem. Experimental results of the algorithm obtained in the “Tarantula MiniGrid” are shown. Tarantula was implemented by linking two clusters from different geographic locations in Mexico (Morelos-Veracruz). The technique used to link the two clusters and conFigureure the Tarantula MiniGrid is described. The effects of latency in communication between the two clusters are discussed. It is shown that the evolutionary algorithm presented is more efficient working in Grid environments because it can carry out major exploration and exploitation of the solution space.

Grid computing using wireless networks is receiving increasing attention and is expected to become a critical part of future grid computing. However, the inherent challenges of mobile environments such as mobility management, disconnected operation, device heterogeneity, service discovery, and resource sharing are significant issues in mobile grid computing. To achieve the best performance in a mobile grid computing environment, the mobile devices with the lowest probability of mobility should be selected for use first. We therefore focus on the idle state of mobile devices and use IP-paging scheme to identify the idle mobile devices. For this, we propose a user-defined checkpoint technique for task migration, which is based on the information of when a mobile device will leave the network or stop due to low battery. Our checkpoint technique performs checkpoints by two conditions : when a mobile device leaves its current cell and when it turns off due to low battery capacity. By performance evaluation, proposed task migration scheme decreases total execution time and total job completion time.

Radio Frequency Identification is increasingly popular technology with many applications. The majority of applications of RFID are in the field of supply chain management. In this paper, we proposed the development of wireless RFID Glove for various applications in real life. Proposed wireless RFID glove is composed of RFID reader of 13.56 MHz, RF wireless module, and power supply battery. Proposed Gloves were applied to two applications. The first application is the interactive leaning and the second application is meal aid system for blind people. The experimental results confirmed good performances.

Task scheduling in Grid environment is a challenging problem because of NP-complete nature of scheduling issue and dynamic characteristic of the environment. Not constrained by local scheduling policy of Grid site, a dynamic service evaluation method based on cloud model is presented. Then we obtain performance metrics of dynamic service. According to dynamic service evaluation, an adaptive and dynamic service clustering method is derived from PSO (Particle Swarm Optimization)-based clustering algorithm. It gathers the services with similar or same QoS (Quality of Service) into one cluster. A dynamic meta-task scheduling algorithm is proposed in light of service clustering. We conduct extensive experiments on the implemented prototype. The results show that our algorithm outperforms prior well-established algorithms in terms of time complexity and user QoS guarantee.

Social network analysis is a new research field in data mining. The clustering in social network analysis is different from traditional clustering. It requires grouping objects into classes based on their links as well as their attributes. While traditional clustering algorithms group objects only based on objects’ similarity, and it can't be applied to social network analysis. So on the basis of BSP (business system planning) clustering algorithm, a social network clustering analysis algorithm is proposed. The proposed algorithm, different from traditional BSP clustering algorithms, can group objects in a social network into different classes based on their links and identify relation among classes dynamically & require less amount of memory.