A large-scale distributed framework for information retrieval in large dynamic search spaces

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)375-398
Number of pages24
JournalApplied Intelligence
Volume35
Issue number3
DOIs
StatePublished - Dec 2011
Externally publishedYes

Keywords

  • Content analysis and indexing
  • Distributed processing
  • Dynamic anytime processing
  • Information search and retrieval
  • Multi-agent architecture

ASJC Scopus subject areas

  • Artificial Intelligence

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