Domain-Driven, Actionable Knowledge Discovery
IEEE Intelligent SystemsPublished 1 July 2007
Longbing Cao, Chengqi Zhang, Qiang Yang, David Bell, Michail Vlachos, Bahar Taneri
Citations83
SJR quartileQ1
SJR score1.33
SNIP2.01
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TL;DR
Data mining increasingly faces complex challenges in the real-life world of business problems and needs and both researchers and practitioners are realizing the importance of domain knowledge to close this gap and develop actionable knowledge for real user needs.
Abstract
Researchers are developing domain-driven data mining techniques that target actionable knowledge discovery (KDD) in complex domain problems. The domain-driven technique aims to utililize and mine many aspects of intelligence, such as in-depth data, domain expertise, real-time human involvement, process, environment, and social intelligence. It also metasynthesizes its intelligence sources for actionable knowledge discovery. The method works to expose next-generation methodologies for actionable knowledge discovery, identifying ways in which KDD can better contribute to critical domain problems in theory and practice. It undercovers domain-driven techniques to help KDD, strengthen business intelligence in complex enterprise applications. It also reveals applications that effectively deploy domain-driven data mining method,to solve complex practical problems.
Keywords
Computer Science
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