TY  - JOUR
A1  - Koorn, Jelmer Jan
A1  - Lu, Xixi
A1  - Leopold, Henrik
A1  - Reijers, Hajo A.
T1  - From action to response to effect
T2  - Information systems : IS ; an international journal ; data bases
N2  - 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.
KW  - Process discovery
KW  - Statistical process mining
KW  - Effect measurement
Y1  - 2022
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/62476
SN  - 0306-4379
SN  - 0094-453X
VL  - 109
PB  - Elsevier
CY  - Amsterdam
ER  -