| Abstrakt: |
Business process reengineering (BPR) can help organisations to identify and improve their business processes. A major problem is the high volume of business process datasets with characteristics such as high dimensionality, noise, uncertainty in process datasets and complicated interactions among process variables. Data mining (DM) techniques facilitate the identification and analysis of business processes, and improve their performance by extracting the hidden knowledge in business process datasets. In this paper, we present the application of DM to BPR, based on two novel approaches. By a literature review, the first approach proposes DMbBPR model, mainly focuses on the applications of data mining to each BPR phase. The second approach presents a novel combinational model based on the knowledge management cycle and CRISP-DM process in the framework of process monitoring architecture. To achieve better results, both approaches should be considered simultaneously in order to effectively identify, analyse, and improve business processes. |
