Извлечение читаемых моделей из логов событий тема диссертации и автореферата по ВАК РФ 00.00.00, кандидат наук Бегичева Антонина Константиновна

• Бегичева Антонина Константиновна
• кандидат наук
• 2026

Оглавление диссертации кандидат наук Бегичева Антонина Константиновна

Contents

Introduction

1 Related Work

1.1 Abstraction Techniques in Process Mining

1.2 Hierarchical Process Modeling Formalisms

1.3 Process Discovery

1.4 Conformance Checking

1.5 Model Readability and Complexity Metrics

2 Preliminaries

2.1 Background

2.2 Event Logs

2.3 Petri Nets

2.4 Hierarchical Workflow Nets

2.5 Conclusions of Chapter

3 Hierarchical Conformance Checking

3.1 Motivating Example: Airline Compensation Process

3.2 Log and Model Transformation

3.3 Hierarchical Conformance Checking

3.4 Conclusions of Chapter

4 Discovery of High-Level Process Models

4.1 Problem Formulation: Discovering Hierarchical Process Models , , , ,

4.2 Hierarchical Discovery for Acyclic Processes

4.3 Handling Cyclic Behavior

4.4 The Integrated Algorithm for Hierarchical Discovery

4.5 Conclusions of Chapter

5 Experimental Evaluation

5.1 Implementation and Environment

5.1.1 Hierarchical Conformance Checking: Plug-in

5.1.2 Hierarchical Process Discovery Algorithm Implementation , ,

5.2 Evaluation of Hierarchical Conformance Checking

5.2.1 Robustness to Xoise and Deviations

5.2.2 Stability Analysis

5.3 Evaluation of the Hierarchical Discovery Algorithm

5.3.1 Discovering HWF-Xets from Artificial Event Logs

5.3.2 Discovering HWF-Xets from Real-Life Event Logs

5.4 Conclusions of Chapter

Conclusions and Future Work

References

А Русская версия диссертации

List of Figures

1 The "spaghetti" process model discovered from the Dutch municipalities' building permit event log

2 The model obtained by applying one of the abstraction methods to

the model shown in the Fig,

3 The Petri net model of the ticket refund request process

4 The positioning of the three main types of process mining

5 The workflow net for handling compensation requests

6 The HWF-net with two refined transitions

7 The WF-net equivalent to the HWF-net in Fig,

8 The abstract process model N1 for handling compensation requests ,

9 The low-level model N2 refined from the model N1 in Fig

10 The log L2, generated by the model N2 in Fig

11 The abstract event log L1

N1

13 The example of inconsistency between a cycle and high-level activities

14 The patterns of interleaving and iteration of sub-processes

15 The transition system for traces ff^d cr2

16 The high-level WF-net corresponding to Fig,

17 The low-level net with cycles within and between sub-processes corresponding to the high-level net from Fig,

18 The causality graphs for the cyclic components (eves) in L

19 Input resource selection interface (ProM plug-in)

20 The initial high-level model displayed in the plug-in

21 The partition assignment interface

22 The output transformed model

23 The result of the compliance verification and the localization of deviations in the original model

24 The result of the compliance verification and localization of deviations

in the model modified by the algorithm

25 The classical WF-net generated by refining the WF-net in Fig, 5 , , ,

26 The high-level WF-net discovered from the event log generated by the WF-net in Fig

27 The classical WF-net discovered from the BPI Challenge 2015 event log

28 The high-level WF-net discovered from the BPI Challenge 2015 event

log

29 The distribution of complexity metrics between the classical model of the BPI Challenge 2015 process and the abstract model and its subproeesses

30 The classical WF-net discovered from the BPI Challenge 2017 event log

31 The high-level WF-net discovered from the BPI Challenge 2017 event

log

32 The distribution of complexity metrics between the classical model of the BPI Challenge 2017 process and the abstract model and its subproeesses

List of Tables

1 Event log sample

2 Relations between transitions in Example

3 Fitness for event logs with specific type of noise

4 Fitness for event logs with noise in a specific place of the process routing

5 Fitness for models with minor structural changes

6 Fitness for models with different degrees of conformance

7 Comparing complexity metrics for detailed and abstract models for

the artificial process

8 Comparing conformance metrics for classical and high-level WF-nets discovered from BPI Challenge event logs

Введение диссертации (часть автореферата) на тему «Извлечение читаемых моделей из логов событий»

Introduction

The information systems of modern organizations generate a large amount of data for each business process. Organizations typically store more detailed data in order to derive valuable insights from it using data analytics. Most information systems automatically produce event logs that provide a complete report on sequences of actions performed within the system over a specified time period. These logs contain traces that represent a sequence of events related to a single instance of a process.

Key attributes typically captured in event logs include the activity name, time-stamp, case identifier, and supplementary contextual information. An illustration of an event log is shown in Table 1,

Process mining is a technology that extracts data from event logs to provide various methods for investigating and monitoring real processes. The two most significant tasks of Process Mining are: (1) process discovery, which creates a process model based on the behavior recorded in the event logs, and (2) conformance checking, which identifies and quantifies discrepancies between the actual and simulated behavior of the process.

Most of the existing process synthesis methods are used to directly transform information from event logs into a process model with the same level of detail. This is inconvenient for experts, who are usually the main users of the resulting models. Event logs generated by systems often contain very detailed information, down to the smallest actions of system components, which makes it difficult to carry out an analysis of the process based on the model. It would be more useful to first obtain an abstract model that contains only the main events and a general scheme of

Table 1: Event log sample

Case ID Timestamp Activity name Employee

Trace 1

1 27-12-2024:09,45 register request Sarah

1 04-01-2025:13.47 check ticket Bob

1 05-01-2025:15.22 examine causally Sarah

1 10-01-2025:12.15 decide Bob, Sarah

1 11-01-2025:17.45 pay compensation Alice

Trace 2

2 29-12-2024:17.48 register request Sarah 2 02-01-2025:14.13 examine thoroughly Sarah 2 04-01-2025:11.23 check ticket Bob

2 08-01-2025:10.59 decide Bob, Sarah

2 11-01-2025:13.47 reject request Alice

the system. This will not only improve understanding of the overall system, but also highlight interesting aspects for more thorough analysis of smaller subprocesses. Obtaining a model that is easier to understand for humans from automatically generated event logs is therefore an urgent task that has not been fully resolved yet.

Hierarchical process models allow us to maintain a high-level view of the process by encapsulating the behavior of individual sub-processes in a high-level transition while preserving details of each sub-process and allowing further transitions to its model. At the highest level, there is an abstract model where each individual transition corresponds to a sub-process built from a lower-level event. The detailed history of process behavior is recorded as a classic event log, making hierarchical process modeling a useful tool for creating understandable process models.

Figure 1: The "spaghetti" process model discovered from the Dutch municipalities' building permit event log

The example in Fig, 1 and Fig, 2 contains two models derived from data on the Dutch municipalities' building permit event logs from the BPI Challenge 2015

dataset. Both models were synthesized from the same event logs that recorded sequences of actions during multiple instances of the appeal process. The model shown in Fig, 2 was created by applying an abstraction algorithm to the original model. It is easy to see that the general structure of the workflow process is visible in a more abstract model.

Figure 2: The model obtained by applying one of the abstraction methods to the model shown in the Fig, 1

The objective of this dissertation is to synthesize a readable process model based on a detailed event log such that the abstract representation is consistent with the original process, with precision to substitution, and is more readable for experts,

Petri nets were chosen as the description language for workflow modeling. Figure 3 shows a classic example of a Petri net that simulates the processing of a ticket refund request from the book |1|, The behavior of this network also partly corresponds to the events in Table 1,

reinitiate request

Figure 3: The Petri net model of the ticket refund request process

Main Contributions of the Dissertation

• An algorithm for checking the conformance between high-level WF-net models and low-level event logs. The algorithm handles interleaving and concurrency in sub-processes,

•

processes,

that also correctly handles cyclic behavior, conformance between the model and the event log,

and real-world event logs confirms that the obtained hierarchical models achieve fitness and precision comparable to classical discovery algorithms, while being significantly more readable and compact.

Presentation of contributions

1, Spring/Summer Young Researchers' Colloquium on Software Engineering

(SYRCoSE-2014, May 2014, Saint-Petersburg State Polytechnic University, Saint-Petersburg, Russia), Talk: Checking conformance of high-level business process models to event logs

2, Science of the Future — Science of the Youth II All-Russia Youth Scientific Forum (September 2016, Kazan, Russia), Talk: Checking conformance of highlevel business process models to event logs

3, I Scientific Conference of the Faculty of Computer Science 2023 (June 2023,

Voronovo, Russia) Talk: An overview of existing methods for synthesizing readable models

4, II Scientific Conference of the Faculty of Computer Science 2024

(October 2024, Voronovo, Russia) Talk: Discovering hierarchical process models: an approach based on events partitioning

Publication of contributions

Second-tier publications:

1. Begieheva A. K,, Lomazova I. A. Does your event log fit the high-level process model? //Modeling and Analysis of Information Systems, - 2015, - V, 22, -№. 3. - p. 392-403.

2. Begieheva A. K,, Lomazova I. A. Discovering high-level process models from event logs //Modeling and Analysis of Information Systems. - 2017. - V. 24. - №. 2. - p. 125-140.

3. Begieheva A. K,, Lomazova I. A., Nesterov R. A. Discovering hierarchical process models: an approach based on events partitioning //Modeling and Analysis of Information Systems. - 2024. - V. 31. - №. 3. - p. 294-315.

Other publications:

1. Begieheva A. K,, Lomazova I. A. Checking conformance of high-level business process models to event logs //Proceedings of the Spring/Summer Young Researchers' Colloquium on Software Engineering. - Institute for System Programming of the RAS, 2014. - №. 8.

2. A.K. Бегичева, Решение проблемы проверки соответствия между абстрактной моделью процесса и детальным журналом событий, Сборник

научно-исследовательских работ по итогам конкурса НИРС I II IN ВШЭ -2015. М.: Издательский дом IIIIY ВШЭ, 2016. Р. 112-124.

3. А,К, Бегичева, Решение проблемы проверки соответствия между абстрактной моделью процесса и детальным журналом событий // В кн. : Сборник тезисов участников форума "Наука будущего — наука молодых". Т. 1 М.: Ипкопсалт К, 2016. С. 224-227.

Outline

The chapters are organized as follows. Chapter 1 presents a structured literature review covering five key areas: (1) abstraction techniques for logs and models, (2) hierarchical modeling formalisms, (3) process discovery algorithms, (4) conformance checking methods, and (5) metrics for model readability and complexity. The analysis highlights specific limitations in discovering readable hierarchical models and performing conformance checking across abstraction levels, thus defining the research gap addressed in this dissertation.

In Chapter 2, the formal definitions are introduced, including event logs, Petri nets, workflow nets and hierarchical workflow nets.

Chapter 3 presents an algorithm for checking conformance between high-level process models and low-level event logs. The correctness of the algorithm was proven for cases of perfect fitness.

Chapter 4 introduces algorithms for synthesizing hierarchical process models from low-level event logs. It begins with a solution for acyclic processes and extends to the handling of cycles and concurrency. This chapter describes the steps for event partitioning, cycle detection, and model construction, supported by formal proofs of correctness.

Chapter 5 demonstrates the practical usability of the proposed algorithms through experiments with artificial and real-life event logs. The robustness of the conformance checking algorithm was also evaluated through experiments using noisy and imperfect data. The evaluation of the process discovery algorithm was based on fitness, precision and model readability. The results confirmed that hierarchical models

are more compact and easy to understand, and their fitness and precision rates are comparable to those for classical algorithms.

Заключение диссертации по теме «Другие cпециальности», Бегичева Антонина Константиновна

Conclusions and Future Work

This dissertation addresses the challenge of discovering readable process models and analyzing them using process mining techniques. We present algorithms for both conformance checking and process discovery for hierarchical process models and event logs. For conformance checking, we have developed an algorithm that can transform both high-level process models and low-level event logs, allowing standard algorithms to evaluate model fitness despite differences in granularity and concurrency effects. To support process discovery, we have proposed algorithms that synthesize hierarchical process models directly from event logs based on a predefined event partitioning method. The hierarchical discovery approach successfully handles complex control flows, including concurrent and cyclic behaviors, and produce structured models that accurately represent the underlying process.

The main contributions of this dissertation are as follows:

• A novel algorithm for hierarchical conformance checking that transforms both models and logs to handle interleaving, with a formal proof of correctness (Theorem 1) and demonstrated robustness to noise,

•

acyclic processes, and (b) an extended, integrated algorithm that correctly handles cyclic and concurrent behavior, with formal guarantees of perfect fitness (Theorems 2 and 3),

strates that hierarchical models preserve high conformance metrics while achieving significant improvements in readability.

By allowing comparison between high-level models, which are preferable for experts, and actual low-level execution data, the conformance checking technique facilitates validation and refinement of strategic process views.

The discovery algorithms produce models that are more readable and understandable for domain experts than traditional flat "spaghetti" models derived from detailed logs. The hierarchical structure allows for analysis at different zoom levels, focusing on the overall flow or drilling down into specific sub-processes as needed. Reliance on event partitioning provides flexibility, allowing the abstraction to be tailored based on domain knowledge or specific analytical goals.

The results of this dissertation provide a foundation for further research on hierarchical process mining and conformance checking. An interesting aspect is developing techniques to automatically suggest meaningful event partitions from log data using clustering, event attributes, or machine learning techniques, reducing the reliance on manual input. To date, there are no common metrics for identifying readability, Therefore, it would be interesting to conduct user studies to quantify perceived readability and usability improvements offered by the newly discovered hierarchical model in comparison to classical models used for various analysis tasks and to compare them with complexity metrics. Although the conformance checking method has shown robustness against noise, it is necessary to further investigate and improve the robustness of the hierarchical algorithm against noisy event logs.

Список литературы диссертационного исследования кандидат наук Бегичева Антонина Константиновна, 2026 год

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