Intelligence metasynthesis and knowledge processing in intelligent systems

Intelligence and Knowledge play more and more important roles in building complex intelligent systems, for instance, intrusion detection systems, and operational analysis systems. Knowledge processing in complex intelligent systems faces new challenges from the increased number of applications and environment, such as the requirements of representing domain and human knowledge in intelligent systems, and discovering actionable knowledge on a large scale in distributed web applications. In this paper, we discuss the main challenges of, and promising approaches to, intelligence metasynthesis and knowledge processing in open complex intelligent systems. We believe (1) ubiquitous intelligence, including data intelligence, domain intelligence, human intelligence, network intelligence and social intelligence, is necessary for OCIS, which needs to be meta-synthesized; and (2) knowledge processing should pay more attention to developing innovative and workable methodologies, techniques, tools and systems for representing, modelling, transforming, discovering and servicing the uncertain, large-scale, deep, distributed, domain-oriented, human-involved, and actionable knowledge highly expected in constructing open complex intelligent systems. To this end, the meta-synthesis of ubiquitous intelligence is an appropriate way in designing complex intelligent systems. To support intelligence meta-synthesis, m-interaction can play as the working mechanism to form rn-spaces as problem-solving systems. In building such m-spaces, advancement in knowledge processing is necessary. © J.UCS

A Method for Interpretively Synthesizing Qualitative Research Findings

In the qualitative research world, one can use a method called meta-synthesis to interpretively assess a compiled body of literature on a specific topic, though it has seen little application in business research let alone in management information systems scholarship. However, because methods for qualitative inquiry have gained more popularity in the information systems discipline, this method holds great promise in supporting efforts toward theoretical generalization for qualitative researchers. Accordingly, in this paper, we present a methodological tutorial on the nature and practice of analytically synthesizing a body of qualitative research for developing and explicating theory