Actionable knowledge discovery and delivery

Abstract

This chapter illustrates the issues in mining complex data and problems for knowledge that will support decision-making actions and shows how complex problems are analyzed through consideration of the concepts and thinking in metasynthetic computing. Typically, mining complex data to deliver actionable knowledge is becoming increasingly challenging. These challenges arise from the following issues: 1.The limitations of existing KDD methodologies and systems, such as purely data-driven techniques or the poor involvement of business and domain coupling with data.2.The characteristics and challenges of complex data in the real world, which involve many different kinds of complexities as discussed in Chaps. 1, 2, 3, and 4.3.Arguably, methodologies and systems available in the current KDD literature rarely present a systematic and comprehensive guide from system sciences and multidisciplinary aspects. These instead play an important role in the study of open complex systems.4.The majority of the existing work focuses on mining simple and manipulated data and problems, which are abstracted from the complexities of real-life problems, and we therefore face critical challenges in addressing real problems and their complexities. Real-life problems present as complex systems, and taking a systematic and comprehensive view is thus very important.