Share Email Print

Proceedings Paper

Large-scale distributed foraging, gathering, and matching for information retrieval: assisting the geospatial intelligence analyst
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 28 March 2005
PDF: 12 pages
Proc. SPIE 5803, Intelligent Computing: Theory and Applications III, (28 March 2005); doi: 10.1117/12.606395
Show Author Affiliations
Eugene Santos Jr., Univ. of Connecticut (United States)
Eunice E. Santos, Virginia Polytechnic Institute and State Univ. (United States)
Hien Nguyen, Univ. of Connecticut (United States)
Long Pan, Virginia Polytechnic Institute and State Univ. (United States)
John Korah, Virginia Polytechnic Institute and State Univ. (United States)

Published in SPIE Proceedings Vol. 5803:
Intelligent Computing: Theory and Applications III
Kevin L. Priddy, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?