The Connection between Process Complexity of Event Sequences and Models discovered by Process Mining

  title={The Connection between Process Complexity of Event Sequences and Models discovered by Process Mining},
  author={Adriano Augusto and Jan Mendling and Maxim Vidgof and Bastian Wurm},
  journal={Inf. Sci.},

A Five-Level Framework for Research on Process Mining

Over the last 20 years, intensive research has been conducted into various process mining techniques, which support the automatic discovery of business process models from event log data, the checking of conformance between specified and observed behavior, the identification of various variants of a business process, non-compliant behavior, performance-relevant insights, and so forth.

Fig4PM: A Library for Calculating Event Log Measures (Extended Abstract)

Fig4PM is an attempt toward building a standard, comprehensive, and reusable library for calculating event log measures, and to build a standard public Python library to facilitate feature generation in process mining applications.

Exploring the Impact of Process Diversity on Business Process Performance (Extended Abstract)

This PhD project aims to show that diversity is an portant indicator when it comes to business process improve ment, and uses existing diversity measures from different search fields to apply to discover reference model s from event logs, which provide a balance between process performance and resilience.

Method to Address Complexity in Organizations Based on a Comprehensive Overview

This paper proposes a method for complexity management that serves to provide key insights and decision support in the form of extensive guidelines for addressing complexity and can assist organizations in their complexity management initiatives.



Automated Discovery of Process Models from Event Logs: Review and Benchmark

The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.

Split miner: automated discovery of accurate and simple business process models from event logs

Split Miner is the first automated process discovery method that is guaranteed to produce deadlock-free process models with concurrency, while not being restricted to producing block-structured process models.

Evaluating Conformance Measures in Process Mining using Conformance Propositions (Extended version)

This paper forms 21 conformance propositions and uses these propositions to evaluate current and existing conformance measures to trigger a discussion by clearly formulating the challenges and requirements (rather than proposing new measures).

Measuring Fitness and Precision of Automatically Discovered Process Models: A Principled and Scalable Approach

An empirical evaluation shows that representative exemplars of this family of measures yield intuitive results on a synthetic dataset of model-log pairs, while outperforming existing measures of fitness and precision in terms of execution times on real-life event logs.

Extraction, correlation, and abstraction of event data for process mining

Techniques proposed in the literature to support the creation of event logs from raw data are reviewed and classified and includes techniques for identification and extraction of the required event data from diverse sources as well as their correlation and abstraction.

Process Mining: Data Science in Action

This is the second edition of Wil van der Aalsts seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several

Scalable process discovery and conformance checking

This paper introduces a framework for process discovery that ensures these properties while passing over the log only once and introduces three algorithms using the framework and introduces a model–model and model–log comparison framework that applies a divide-and-conquer strategy to measure recall, fitness, and precision.

Process mining: a two-step approach to balance between underfitting and overfitting

The two-step process mining approach, implemented in the context of ProM, overcomes many of the limitations of traditional approaches and enables the user to control the balance between “overfitting” and “underfitting’.