The popularity of grid services has widened their application to numerous domains and increased the utilization of computational resources. In order to create more incentives for the resources owners to lease their resources and prevent users from wasting the resources, the introduction of a market-oriented grid is inevitable. However, the issues for the negotiation between service provider and consumer over the supply and demand of resources can be complex, with highly interdependent issues. In this research, a simulated automated negotiation mechanism including a co-evolutionary mechanism and a modified game theory approach is proposed, to assist them in reaching an agreement over the conflicting issues. In the proposed architecture, the co-evolution process is able to reduce the multiple dimensional search space into a two-dimension search space and identify the appropriate negotiation strategies for the negotiating agents to form a payoff matrix which can be used for the game theory related stage of their interaction. The multiple stage negotiation process is introduced to improve the negotiation result. In this paper, an application which requires a large amount of computational resources to process the data generated from mobile devises is used to demonstrate that the proposed system is able to resolve the conflicts and obtain a valid solution.
The innovative diffusion theory indicates that the key to success of businesses is the innovative ideas of the early adopters. Furthermore, the early adopters' documents on the Internet were extremely rare; the traditional associative analyses in text mining tend to ignore these useful ideas of the early adopters. In this study, a framework was proposed, which uses low-term frequency (TF), low-term frequency with inverse document frequency and low TF with the inverse clusters frequency, to acquire rare connections between low-frequency terms, to extract early adopters' incipient and innovative ideas. This new proposed framework amplifies the rare chance to find potential terms which are valuable for businesses' future. Finally, some observed data obtained from the passengers on airplanes or trains were used to extract the innovative ideas from early adopters. By putting the data into a business scenario, a case study was presented and the feasibility of the framework can therefore be checked by experts. A comparison has been made between the proposed framework and chance discovery. The experimental result evidences that the results in the new framework are more effective than the outcomes of chance discovery method to sift out incipient ideas.
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