Domain-Driven Data Mining: Challenges and Prospects
Traditional data mining research mainly focus]es on developing, demonstrating, and pushing the use of specific algorithms and models. The process of data mining stops at pattern identification. Consequently, a widely seen fact is that 1) many algorithms have been designed of which very few are repeatable and executable in the real world, 2) often many patterns are mined but a major proportion of them are either commonsense or of no particular interest to business, and 3) end users generally cannot easily understand and take them over for business use. In summary, we see that the findings are not actionable, and lack soft power in solving real-world complex problems. Thorough efforts are essential for promoting the actionability of knowledge discovery in real-world smart decision making. To this end, domain-driven data mining (D-3 M) has been proposed to tackle the above issues, and promote the paradigm shift from "data-centered knowledge discovery" to "domain-driven, actionable knowledge delivery." In D-3 M, ubiquitous intelligence is incorporated into the mining process and models, and a corresponding problem-solving system is formed as the space for knowledge discovery and delivery. Based on our related work, this paper presents an overview of driving forces, theoretical frameworks, architectures, techniques, case studies, and open issues of D-3 M. We understand D-3 M discloses many critical issues with no thorough and mature solutions available for now, which indicates the challenges and prospects for this new topic.
data mining domain-driven data mining (d-3 m) actionable knowledge discovery and delivery