EVALUATING PROCESS DISCOVERY FROM LOOP STRUCTURES

Abstract

Modern organizations increasingly rely on sophisticated
information systems to manage their business processes, generating detailed
event logs that record key activities. Process mining, and specifically process
discovery, utilizes these event logs to construct models that represent the
underlying processes. Effective process discovery is crucial for organizations
to gain insights into their operations, identify inefficiencies, and drive
continuous improvement. This study evaluates the capability of four process
discovery algorithms – Alpha Miner, Heuristics Miner, ILP Miner, and
Inductive Miner – in handling complex workflow patterns, particularly those
involving intricate loop and nested control-flow structures. By generating
synthetic event logs and applying the PM4Py library, we assess the
algorithms' performance using metrics such as fitness, precision,
generalization, and simplicity. Our results highlight the strengths and
limitations of each algorithm, providing valuable insights for researchers and
practitioners in the field.