Journal article
A large-scale distributed framework for information retrieval in large dynamic search spaces
Applied intelligence (Dordrecht, Netherlands), v 35(3), pp 375-398
01 Dec 2011
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
One of the main problems facing human analysts dealing with large amounts of dynamic data is that important information may not be assessed in time to aid the decision making process. We present a novel distributed processing framework called Intelligent Foraging, Gathering and Matching (I-FGM) that addresses this problem by concentrating on resource allocation and adapting to computational needs in real-time. It serves as an umbrella framework in which the various tools and techniques available in information retrieval can be used effectively and efficiently. We implement a prototype of I-FGM and validate it through both empirical studies and theoretical performance analysis.
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Details
- Title
- A large-scale distributed framework for information retrieval in large dynamic search spaces
- Creators
- Eugene Santos - Dartmouth CollegeEunice E. Santos - The University of Texas at El PasoHien Nguyen - University of Wisconsin–WhitewaterLong Pan - Virginia TechJohn Korah - The University of Texas at El Paso
- Publication Details
- Applied intelligence (Dordrecht, Netherlands), v 35(3), pp 375-398
- Publisher
- Springer Nature
- Number of pages
- 24
- Grant note
- HM1582-04-1-2027 / National Geospatial Intelligence Agency
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Engineering
- Web of Science ID
- WOS:000296009800004
- Scopus ID
- 2-s2.0-77951121602
- Other Identifier
- 991022207048504721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence