Making decision process knowledge explicit using the product data model

In this paper, we present a new knowledge acquisition and formalization method: the decision mining approach. Basically, we aim to produce a model of the workflow of mental actions performed by decision makers during the decision process. We show that through the use of a Product Data Model (PDM) we can make explicit the knowledge employed in decision making. We use the PDM to provide insights into the data view of a business decision process. To support our claim we introduce our complete, functional decision mining approach. We present a "decision-aware system" that introduces the user in a simulation scenario environment containing all data needed for the decision. We log the interaction with the system (focusing on data manipulation and aggregation) and output a user action log file. The log file is then mined through the presented mining algorithm and a Product Data Model (PDM) is created. The advantage of our approach is that, when needed to investigate a large number of subjects, it is much faster, less expensive and produces more objective results than classical knowledge acquisition methods (such as interview and questionnaires). The feasibility and usability of our approach is shown by a prototype, a case study and experiments.