Formal structures for data mining, knowledge discovery and communication in a knowledge management environment
Current Business Intelligence (BI) initiatives customize DM-KDD techniques into business analytics, which cannot be used in applications other than business. A review of current methodology at the strategic level of the KM/KDD domains indicates that there exists no general formal framework which can be adopted in new applications, or new application areas. There are no established procedures for the domain expert to express their prior knowledge, understanding and aims in a way which can be linked to KDD/DMM processes and subsequent deployment of discovered knowledge. It is suggested that the sequential life-cycle project-management approach of CRISP-DM needs to be complemented by a dynamic interactive view of a conceptual data/information/knowledge hierarchy in the KM context. It is also suggested that a graphical/visual knowledge representation framework needs to be developed as the basis of a knowledge and discovery and communication framework (KDCF). A review of the limitations in DM methodology at the technical/technological level leads to the conclusion that there is no coherent DM methodology to guide the choice of models and their evaluation, that the DM discipline is fractionated, and that the fundamental search and sampling paradigms have been insufficiently utilized in DM development. It is proposed that development of linked data and model ontologies, together with a DM-epistemology, and associated with full exploitation of search and sampling could lead to improved cohesion and efficacy of the DM discipline.
kdd crisp-dm kdcf ontology data-mining (dm) data-warehousing (dw) business-intelligence (bi) epistemology knowledge-representation e-r diagrams uml bayesian nets o-ss-e