Intelligent Foraging, Gathering and Matching (I-FGM) combines a unique multi-agent architecture with a novel partial
processing paradigm to provide a solution for real-time information retrieval in large and dynamic databases. I-FGM
provides a unified framework for combining the results from various heterogeneous databases and seeks to provide
easily verifiable performance guarantees. In our previous work, I-FGM had been implemented and validated with
experiments on dynamic text data. However, the heterogeneity of search spaces requires our system having the ability to
effectively handle various types of data. Besides texts, images are the most significant and fundamental data for
information retrieval. In this paper, we extend the I-FGM system to incorporate images in its search spaces using a
region-based Wavelet Image Retrieval algorithm called WALRUS. Similar to what we did for text retrieval, we
modified the WALRUS algorithm to partially and incrementally extract the regions from an image and measure the
similarity value of this image. Based on the obtained partial results, we refine our computational resources by updating
the priority values of image documents. Experiments have been conducted on I-FGM system with image retrieval. The
results show that I-FGM outperforms its control systems. Also, in this paper we present theoretical analysis of the
systems with a focus on performance. Based on probability theory, we provide models and predictions of the average
performance of the I-FGM system and its two control systems, as well as the systems without partial processing.
Intelligent foraging, gathering and matching (I-FGM) has been shown to be an effective tool for intelligence analysts
who have to deal with large and dynamic search spaces. I-FGM introduced a unique resource allocation strategy based
on a partial information processing paradigm which, along with a modular system architecture, makes it a truly novel
and comprehensive solution to information retrieval in such search spaces. This paper provides further validation of its
performance by studying its behavior while working with highly dynamic databases. Results from earlier experiments
were analyzed and important changes have been made in the system parameters to deal with dynamism in the search
space. These changes also help in our goal of providing relevant search results quickly and with minimum wastage of
computational resources. Experiments have been conducted on I-FGM in a realistic and dynamic simulation
environment, and its results are compared with two other control systems. I-FGM clearly outperforms the control
systems.
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.
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