AgentDiscover: A Multi-Agent System for Knowledge Discovery from Databases

  title={AgentDiscover: A Multi-Agent System for Knowledge Discovery from Databases},
  author={Horia Emil Popa and Daniel Pop and V. Negru and D. Zaharie},
  journal={Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2007)},
  • Horia Emil Popa, Daniel Pop, +1 author D. Zaharie
  • Published 2007
  • Computer Science
  • Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2007)
AgentDiscover is a multi-agent based intelligent recommendation system, supporting real-time control and feedback, for building and execution workflows for knowledge discovery from databases (KDD). The aim of the proposed system is to deal with the complexity of KDD processes and to offer a tool that supports both researchers exploring KDD methods and non-expert users looking for quick results in this field. A prototype was developed in JADE and the approach was tested for a medical dataset. 
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