Expanding the Knowledge Base for More Effective Data Mining

Traditionally, data mining, as part of the knowledge discovery process, relies solely on the information contained in the database to generate patterns. Recently, there has been some recognition in the field that expanding the knowledge passed to the pattern generation phase by including other domain knowledge, may have beneficial effects of the interestingness and actionability of the resulting patterns. In this paper, we present a new knowledge discovery method that uses additional decision rules and the analytic hierarchy process (AHP) to conceptualize and structure the domain, thus capturing a broader notion of domain knowledge upon which data mining can be applied. Based on design science guidelines, we design, develop and implement our method within the domain of a brain trauma intensive care unit

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse