A Node Embedding Framework for Model-Centric Conformance Checking in Process Mining

Mostafa Abbasi; Homayoun Najjaran

cs.AIOct 13, 2026

A Node Embedding Framework for Model-Centric Conformance Checking in Process Mining

Authors: Mostafa Abbasi, Homayoun Najjaran

Organizations: Faculty of Engineering and Computer Science, University of Victoria, Victoria, BC, Canada

Abstract

Evaluating the quality of process models discovered from event logs is a fundamental challenge in process mining. Traditional evaluation relies on conformance metrics such as fitness and precision, which are highly sensitive to noise and deviations in event logs and therefore often fail to give a consistent assessment of discovery algorithms. We introduce a model-centric evaluation framework that combines node embeddings with Procrustes alignment to enable cross-graph comparability, since embeddings derived from different graphs reside in distinct vector spaces. The proposed PetriNetGraph method transforms Petri net process models into graph representations compatible with embedding algorithms. Comparing the node embeddings of ground-truth and discovered models through a parameterized cosine distance yields a unified and bounded metric for evaluating process discovery across datasets and methods. We evaluate the framework under static and context-aware scenarios to examine how prior knowledge of activity existence influences the assessment of structural similarity. The framework also integrates a compliance checking mechanism that localizes deviations at the activity level and an entropy-regularized node distance aggregation function that quantifies global deviations across the model. Experiments on nine publicly available event logs show that the approach provides a reliable embedding-based conformance indicator that reflects the behavior of fitness and precision. Experiments on synthetic logs with controlled noise levels confirm that it is substantially more robust to log deviations than traditional conformance metrics, PetriNet2Vec, and its dynamic-window and Procrustes-aligned variants, while remaining sensitive to process model degradation. All implementation details and datasets are publicly available.

Figures & tables

Figure 2

Figure 3: Static process model conformance checking results vs. cosine distance

Figure 4: 3D components by PCA Heuristics Miner on Road Traffic Fines dataset

Figure 5: Context-aware process model conformance checking results vs. cosine distance

Panel of Figure 6 — Figure 6: Discovered process model for Road Traffic Fines dataset

Dataset	Method	Simplicity
BPIC_2020_Request_for_Payment	ILP Miner	0.245
	Inductive Miner	0.556
Road Traffic	ILP Miner	0.245
	Inductive Miner	0.636
BPIC_2012	Heuristics Miner	0.654
	Split Miner	0.818

Table 1: Simplicity metrics for process models discovered using different methods across datasets. Higher values indicate more readable models. Note: Simplicity metric as defined in [ 36 ] .

Figure 7: 3D Components by PCA ILP miner on 2020 Domestic Declaration

Panel of Figure 8 — Figure 8: Distance and conformance metrics evaluated on fixed models across varying noise levels in the event logs.

Metric	CV $@$ ( $\varepsilon\downarrow$ in $\mathcal{L}_{\varepsilon}$ )	CV $@$ ( $\varepsilon\uparrow$ in $\mathcal{L}_{\varepsilon}$ )	CV $@$ ( $\varepsilon\uparrow$ in $\mathcal{M}_{\varepsilon}$ )
Fitness	0.000000	0.237964	0.000000
Precision	0.124164	0.121690	0.126832
PetriNet2Vec [ 9 ]	0.120372	0.227511	0.105085
PetriNet2Vec+PA	0.109452	0.162488	0.256595
PetriNet2Vec+DW	0.095438	0.096008	0.524864
Our Work	0.077518	0.074094	0.678094

Table 2: Comparison of coefficient of variation (CV) values under different noise conditions in

\mathcal{L}_{\varepsilon}

and

\mathcal{M}_{\varepsilon}

. (PA: Procrustes Alignment, DW: Dynamic Window)

Figure 9: Evaluation of distance and conformance metrics using noise-free event logs with process models discovered under varying noise levels. ( $\mathcal{M}_{\varepsilon}$ )

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Dataset / Method	$\lambda$	$D_{\text{Weighted}}$	# Matched	# Unmatched	Total residual	Per-anchor
bpic_2012
GNN Miner	0	0.00766	12	14	6.206	0.517
	0.5	0.11635	12	14	6.206	0.517
	1	0.12079	12	14	6.206	0.517
	10	0.12468	12	14	6.206	0.517
	inf	0.12510	12	14	6.206	0.517

Appendix

Table 3: Context-aware model distance of node embeddings, grouped by dataset and method, with varying

\lambda

Dataset / Method	$\lambda$	$D_{\text{Weighted}}$	# Matched	# Unmatched	Total residual	Per-anchor
bpic_2012
GNN Miner	0	0.00942	12	14	5.431	0.453
	0.5	0.12431	12	14	5.431	0.453
	1	0.12893	12	14	5.431	0.453
	10	0.13306	12	14	5.431	0.453
	inf	0.13351	12	14	5.431	0.453

Appendix

Table 4: Static model distance of node embeddings, grouped by dataset and method, with varying

\lambda

Figure 10: Context-aware process model conformance checking results vs. cosine distance

Figure 11: Static process model conformance checking results vs. cosine distance

Scenario	$d$	# Matched	# Unmatched	Total residual	Per-anchor residual
context_aware	12	12	41	4.843588	0.403632
context_aware	32	12	41	4.048280	0.337357
context_aware	64	12	41	4.909243	0.409104
context_aware	128	12	41	4.534481	0.377873
static	12	11	42	4.653875	0.423080
static	32	11	42	4.713240	0.428476

Appendix

Table 5: Parameter sensitivity analysis on BPIC_2020_Permit_log using ILP Miner, reported for different embedding dimensions

d

Table 18

	Context-aware		Static
min_count	Trace pairs	Truth pairs	Trace pairs	Truth pairs
20	1,365,674	1,944,968	2,527,037	2,427,754
30	1,368,732	1,943,188	2,530,069	2,427,626
50	1,364,554	1,946,840	2,524,866	2,428,312
100	1,356,886	1,940,308	2,527,260	2,430,278
150	1,363,682	1,939,820	2,528,345	2,427,590

Appendix

Table 8: Average number of Word2Vec embedding pairs across min_count values (Sepsis).

Figure 12: Wall-clock runtime breakdown across the nine benchmark datasets for the two evaluation scenarios

Figure 13: Runtime scaling behavior of the proposed framework

Process Mining

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Concurrency-Aware Process Model Forecasting with Causal Nets

Process model forecasting (PMF) aims to predict the process model that will characterize a future period, thereby providing a process-level view of how behavior is expected to evolve. Existing PMF methods, however, forecast directly-follows graphs, which cannot explicitly represent concurrency. We extend PMF to causal nets by forecasting time series of relation and binding counts and using these forecasts to reconstruct future process models with AND/XOR semantics. To evaluate the resulting models, we introduce a protocol that accounts for partial traces and constructs the workflow nets required for conformance checking. Experiments on four event logs show that the forecasted models achieve conformance levels close to those of models re-mined from observations in the corresponding future windows. They also outperform static discovery baselines, which retain high precision on the structurally stable log but exhibit substantial precision losses on the other three logs. Filtering infrequent bindings improves most conformance metrics, although it also removes much of the concurrent behavior captured by the models.

Yongbo Yu, Jari Peeperkorn, Johannes De Smedt +1

Research Center for Information Systems Engineering (LIRIS), KU Leuven, Leuven, Belgium

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Aug 10, 2026

Monotonicity-Guided Bottom-Up Petri Net Discovery: The SPECpp Framework

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.

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Process And Data Science (PADS) RWTH Aachen University D-52074 Aachen, Germany

Process Mining

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Jul 6, 2026

Framework for Grouping Local Process Models

Local Process Models (LPMs) are an underexplored concept in process mining. LPMs describe patterns in event data considering sequence, choice, concurrency, and loop. In recent years, process mining has proved successful in the analysis and improvement of operational processes. More often than not, surprising findings are found when one does not consider the full process, making LPMs and their discovery highly valuable. However, similar to other pattern mining approaches, LPM discovery algorithms face the problems of model explosion and model repetition, i.e., the algorithms may create hundreds if not thousands of LPMs, and subsets of them are close in structure or behavior. Practically, no analyst would be able to comb through thousands of LPMs leading to using a sample of LPMs that are easily accessible. The current sentiment is that the top-scoring LPMs form the optimal sample to be presented. However, different applications should demand a different optimal sample. With this work, we show that if the goal of the mined LPMs is to understand a process, using the top-scoring LPMs as an optimal sample is a poor choice because of high repetition. We propose a framework for grouping LPMs and creating an optimal sample by taking one representative LPM for each group. We measure similarity between models via established process model similarity measures or by comparing the context in which an LPM appears. The context is formed using data attributes available in the underlying event logs. We demonstrate the usefulness of grouping on multiple event logs by comparing repetition and coverage between samples comprised of the top-scoring models and the representatives of discovered groups.

Viki Peeva, Wil M. P. van der Aalst

Chair of Process and Data Science (PADS), RWTH Aachen University, Ahronstrasse 55, Aachen, 52074, North Rhine-Westphalia, Germany.

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