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28 March 2005 Large-scale distributed foraging, gathering, and matching for information retrieval: assisting the geospatial intelligence analyst
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Abstract
With the proliferation of online resources, there is an increasing need to effectively and efficiently retrieve data and knowledge from distributed geospatial databases. One of the key challenges of this problem is the fact that geospatial databases are usually large and dynamic. In this paper, we address this problem by developing a large scale distributed intelligent foraging, gathering and matching (I-FGM) framework for massive and dynamic information spaces. We assess the effectiveness of our approach by comparing a prototype I-FGM against two simple controls systems (randomized selection and partially intelligent systems). We designed and employed a medium-sized testbed to get an accurate measure of retrieval precision and recall for each system. The results obtained show that I-FGM retrieves relevant information more quickly than the two other control approaches.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Eugene Santos Jr., Eunice E. Santos, Hien Nguyen, Long Pan, and John Korah "Large-scale distributed foraging, gathering, and matching for information retrieval: assisting the geospatial intelligence analyst", Proc. SPIE 5803, Intelligent Computing: Theory and Applications III, (28 March 2005); https://doi.org/10.1117/12.606395
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