ACQUIRE: agent-based complex query and information retrieval engine
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Heuristics for agent routing and itinerary optimization on dynamic networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Performance optimization analysis using P-agent
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
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Mobile agents have the potential to substantially improve the speed and efficiency with which distributed and heterogeneous data is retrieved. By moving the computation to the data, retrieval times can be reduced by the elimination of unnecessary data transfer. One way to improve a mobile agent system's retrieval efficiency is to incorporate various query optimization techniques (Das et. al., 2002). These methods involve re-writing of the query execution graph so each mobile agent retrieves its requested data in an optimized order, thus minimizing total data transfer size. While these query re-writing methods can be highly effective in reducing both retrieval times and data transfer sizes, they are generally "static", in that the mobile agents retrieve data in a particular order based on an itinerary that is fixed at the time the plan is generated. We have developed a system by which the advantages of mobile agents are leveraged to optimize data retrieval by dynamically optimizing the retrieval strategy as it is carried out. This strategy equips each spawned agent with the full query execution graph and necessary code to execute the retrieval plan at any data site in the network. The spawned agents communicate and collaborate with each other to dynamically decide where to migrate, send data, and perform necessary computations. These decisions depend on retrieval factors such as network speed, data size, and the computational capabilities of the data servers involved in the retrieval. The feasibility of the approach has been demonstrated within a local area network environment using Earth Science data and we present some experimental results in this context.