‘Domain-driven data mining’ was proposed in 2004 for ‘actionable knowledge discovery’ in complex domains and problems, when discovering ‘actionable intelligence’ was not a trivial task. The significant developments of data science, new-generation AI and deep neural learning make domain-driven actionable intelligent discovery possible with progress made such as in representing and learning various complexities and intelligences in complex systems, data and behaviors. This talk will briefly review the aims, progresses and gaps of conventional data mining/knowledge discovery and machine learning, domain-driven actionable knowledge discovery, and challenges and opportunities in domain-driven actionable intelligence discovery. Related strategic issues in data science thinking, new-generation AI and deep learning will also be discussed. Illustrations, case studies, and theoretical and practical challenges in industry and government data science and AI will be discussed together with the above review.
Longbing Cao is a professor and an Australian Research Council Future Fellow (Professorial level) at the University of Technology Sydney (UTS). He received the Eureka prize for excellence in data science, a most prestigious scientific award in Australia. His broad research interest covers data science, artificial intelligence, machine learning, behavior informatics, knowledge discovery, and complex intelligent systems, etc. He created several Australian and global initiatives in data science. In 2004, he proposed the concept ‘domain driven data mining’ and has led to implement many large enterprise data science projects for actionable knowledge discovery for governments and businesses, involving over 10 domains including capital markets, banking, insurance, telecommunication, transport, education, smart cities, online business, and public sectors (e.g., financial service, taxation, social welfare, IP, regulation, immigration). More about his work is available at www.datasciences.org.