Large Scale Distributed Foraging, Gathering, and Matching for Information Retrieval: Assisting the Geospatial Intelligence Analyst

Abstract

There is a growing need for quick and effective data retrieval from dispersed geospatial databases due to the abundance of
internet resources. The enormous and ever-changing nature of geospatial datasets is one of the main obstacles to solving this
issue. To solve this issue, we create an I-FGM framework for huge and ever-changing information spaces that is distributed and
designed for intelligent foraging, collecting, and matching on a wide scale. To determine how well our method works, we pit a
prototype I-FGM against two control systems: one using randomized selection and the other using semi intelligent algorithms.
To ensure that we were measuring each system’s retrieval accuracy and recall accurately, we constructed and used a medium-sized
testbed. The results demonstrate that compared to the other two control methods, I-FGM collects pertinent data at a faster rate.