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Imposing Rules in Process Discovery: an Inductive Mining Approach

Ali Norouzifar, Marcus Dees, Wil van der Aalst

TL;DR

This paper proposes a discovery technique incorporating process documentation and domain experts' knowledge in a novel inductive mining approach that takes a set of user-defined or discovered rules as input and utilizes them to discover enhanced process models.

Abstract

Process discovery aims to discover descriptive process models from event logs. These discovered process models depict the actual execution of a process and serve as a foundational element for conformance checking, performance analyses, and many other applications. While most of the current process discovery algorithms primarily rely on a single event log for model discovery, additional sources of information, such as process documentation and domain experts' knowledge, remain untapped. This valuable information is often overlooked in traditional process discovery approaches. In this paper, we propose a discovery technique incorporating such knowledge in a novel inductive mining approach. This method takes a set of user-defined or discovered rules as input and utilizes them to discover enhanced process models. Our proposed framework has been implemented and tested using several publicly available real-life event logs. Furthermore, to showcase the framework's effectiveness in a practical setting, we conducted a case study in collaboration with UWV, the Dutch employee insurance agency.

Imposing Rules in Process Discovery: an Inductive Mining Approach

TL;DR

This paper proposes a discovery technique incorporating process documentation and domain experts' knowledge in a novel inductive mining approach that takes a set of user-defined or discovered rules as input and utilizes them to discover enhanced process models.

Abstract

Process discovery aims to discover descriptive process models from event logs. These discovered process models depict the actual execution of a process and serve as a foundational element for conformance checking, performance analyses, and many other applications. While most of the current process discovery algorithms primarily rely on a single event log for model discovery, additional sources of information, such as process documentation and domain experts' knowledge, remain untapped. This valuable information is often overlooked in traditional process discovery approaches. In this paper, we propose a discovery technique incorporating such knowledge in a novel inductive mining approach. This method takes a set of user-defined or discovered rules as input and utilizes them to discover enhanced process models. Our proposed framework has been implemented and tested using several publicly available real-life event logs. Furthermore, to showcase the framework's effectiveness in a practical setting, we conducted a case study in collaboration with UWV, the Dutch employee insurance agency.
Paper Structure (12 sections, 9 figures, 4 tables, 1 algorithm)

This paper contains 12 sections, 9 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Motivating Examples
  4. Preliminaries
  5. Inductive Miner with Rules (IMr)
  6. The Set of Rules
  7. Candidate Cuts Pruning
  8. Evaluation
  9. Real-life Event Logs
  10. Case Study UWV
  11. Open Challenges
  12. Conclusion

Figures (9)

  • Figure 1: Comparing the information flow in IM frameworks with our framework.
  • Figure 2: Motivating examples, using IMf to discover process models for BPIC 2017, BPIC 2018, UWV event log.
  • Figure 3: One iteration of IMr, the framework proposed in this paper, identifies candidate cuts adhering to specified rules and selects the cut with minimum cost, incorporating cost functions from DBLP:conf/sac/NorouzifarA23.
  • Figure 4: Comparison between process models discovered using IMr, IMf, and IMbi.As can be seen, IMr (red shapes) models perform better than IMbi (green shapes) and IMf (blue shapes) models.
  • Figure 5: Comparison between best models discovered by IMr, IMf, and IMbi (for Sepsis and Hospital event logs it was not feasible to discover a model in an hour using IMbi).
  • ...and 4 more figures

Theorems & Definitions (4)

  • definition thmcounterdefinition: Event log
  • definition thmcounterdefinition: Process tree
  • definition thmcounterdefinition: Binary Cut
  • definition thmcounterdefinition: Constraint violation