Monotonicity-Guided Bottom-Up Petri Net Discovery: The SPECpp Framework
2026-08-10 • Databases
DatabasesArtificial Intelligence
AI summaryⓘ
The authors address the challenge of discovering processes from event data using Petri nets, which can represent complex behaviors like concurrency in a compact way. Unlike traditional methods that start with fixed patterns, their approach lets these patterns form naturally, capturing more detailed behaviors. They created a tool called SPECpp that efficiently finds good process models despite the huge number of possibilities, and tested it on both made-up and real data. This helps better understand and analyze processes in various systems.
process discoveryprocess miningPetri netsconcurrencymonotonic propertiesbottom-up discoveryInductive Minerfree-choice constructsevent dataprocess models
Authors
Leah Tacke genannt Unterberg, Lisa L. Mannel, Wil M. P. van der Aalst
Abstract
Process discovery is one of the central challenges in process mining. Petri nets are particularly attractive because simple local constructs can express complex behavior, including concurrency. While their global behavior may be difficult to analyze, individual places can be efficiently characterized using monotonic properties, enabling bottom-up discovery. Unlike top-down approaches such as the Inductive Miner, which rely on predefined constructs for sequences, choices, loops, and concurrency, our approach allows such structures to emerge organically and can exploit the full expressive power of Petri nets, including free-choice constructs and long-term dependencies. The main challenge is the exponential number of candidate places and their combinations. We present the SPECpp framework which implements strategies to obtain high-quality models under time and resource constraints. SPECpp supports rapid experimentation and is used to evaluate these strategies using both synthetic and real-life event data.