Domain-Driven Action Able Knowledge Discovery

Traditional data mining research mainly deals with development of data centre algorithms and models. The algorithms and patterns are technically sound enough but very few were executable in real world. A pattern mined does not satisfy the business expectations. Business people are not informed about how and what to do to take over the technical deliverables. The gap between academia and business has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity. To narrow down the gap, cater for real world factors relevant to data mining, and make data mining workable in supporting decision-making actions in the real world, we propose the methodology of Domain Driven Data Mining (D3M for short). D3M aims to construct next-generation methodologies, techniques and tools for a possible paradigm shift from data-centered hidden pattern mining to domain-driven actionable knowledge delivery. Knowledge mined is actionable in real world. Based on our related work, this paper presents an overview of driving forces, theoretical frameworks, methodologies, components, and open issues of D3M.