Large scale distributed foraging, gathering, and matching for information retrieval: Assisting the geospatial intelligence analyst

Eugene Santos, Eunice E. Santos, Hien Nguyen, Long Pan, John Korah

Research output: Contribution to journalConference article

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.

Original languageEnglish (US)
Article number10
Pages (from-to)66-77
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5803
DOIs
StatePublished - Nov 9 2005
Externally publishedYes
EventIntelligent Computing: Theory and Applications III - Orlando, FL, United States
Duration: Mar 28 2005Mar 29 2005

Keywords

  • Distributed information retrieval
  • Dynamic information space
  • Geospatial information retrieval
  • Multi-agent systems
  • Parallel information retrieval
  • Retrieval performance

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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