From action to response to effect: Mining statistical relations in work processes. Issue 109 (November 2022)

• Record Type: Journal Article
• Authors: Koorn, Jelmer J.; Lu, Xixi; Leopold, Henrik; Reijers, Hajo A.
• Is Part Of: Information systems. Issue 109(2022)
• Publication Date: 2022-11
• Subjects: Process discovery -- Statistical process mining -- Effect measurement; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2022.102035
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: Process mining techniques are valuable to gain insights into and help improve (work) processes. Many of these techniques focus on the sequential order in which activities are performed. Few of these techniques consider the statistical relations within processes. In particular, existing techniques do not allow insights into how responses to an event (action) result in desired or undesired outcomes (effects). We propose and formalize the ARE miner, a novel technique that allows us to analyze and understand these action-response-effect patterns. We take a statistical approach to uncover potential dependency relations in these patterns. The goal of this research is to generate processes that are: (1) appropriately represented, and (2) effectively filtered to show meaningful relations. We evaluate the ARE miner in two ways. First, we use an artificial data set to demonstrate the effectiveness of the ARE miner compared to two traditional process-oriented approaches. Second, we apply the ARE miner to a real-world data set from a Dutch healthcare institution. We show that the ARE miner generates comprehensible representations that lead to informative insights into statistical relations between actions, responses, and effects. Highlights: We present the ARE miner as a novel process discovery technique. The ARE miner provides insights into action–response–effect patterns. We build on the notion of statistical significance to accomplish this. The results are visualized in a dedicated graphical representation. The representations allow to effectively analyze (un)desired effects.