The adoption of process mining technology is steadily increasing in practice. However, individual analysts confronted with questions about a process execution lack adequate support for various tasks. Therefore, this doctoral project aims to explore the specific support needs of individuals and ofer methods and operational assistance for the analysis phase in process mining projects. Employing an empirical approach and analyzing behavior observations, interviews, and questionnaire results, we seek to identify the challenges of analysts and develop practice-oriented guidance. The envisioned methods, guidelines, and operational support tools will aid analysts throughout the entire analysis process, from formulating questions to evaluating their answers.
Research in the field of process mining (PM) has so far mainly focused on technical aspects [
Within this doctoral project, we aim to close this gap by (1) contributing to a better understanding of the analysis process and (2) developing methodological guidance and software-based support for novice analysts.
The motivation behind the aforementioned goals stems from the recognition that performing a
PM analysis poses challenges for analysts, especially for those who are less experienced (novices).
Existing research (e.g., [
RQ0: What are the challenges perceived by individual process analysts during a PM project?
To answer this question, we followed grounded theory, a qualitative research approach that suggests how to retrieve so far unknown concepts and theories from empirical data [10]. We conducted semi-structured interviews and identified 23 challenges that are faced during PM projects [11]. The existence of challenges, such as formulating PM questions, navigating an analysis with limited domain knowledge and prior experience, or concluding an analysis and ifnding an answer [ 11], confirms the need for a better understanding of the individual level of PM. This understanding is required to identify the causes of challenges and develop tailored support for analysts.
In this project, we focus on the analysis phase, including its influencing factors and its output,
but excluding PM project tasks detached from the analysis phase, such as data preparation.
Guided by the outcomes of the interview study, combined with findings from existing literature
(esp. [
First, we could infer that the available tools and PM techniques, the properties of the event
log to be analyzed, and the PM question influence the analysis phase and are often given from
the start and adopted over several iterations. While the quality of event logs already received
recognizable attention in the research community [14] and tools are constantly enhanced [
Second, we learned that the analysis behavior is highly analyst-specific, emergent, and manual,
as analysts apply sequences of operations, i.e., data manipulations, creation and interpretation
of representations, formulation and testing of hypotheses, etc. [15, 16], to answer (business)
questions. Additionally, many analysis steps depend on prior experience and domain
knowledge [12]. While ideas have been proposed to automate selected analysis tasks (e.g., [
Third, analysts need to translate their observations into insights and ultimately answer the questions of the stakeholders. In practice, these insights often trigger process transformations that increase the eficiency or quality of the process. Based on our knowledge, attempts to streamline the qualitative evaluation of PM results exist [17], but support in assessing the quality of PM insights objectively beyond the model quality is currently lacking.
In order to suggest support for analysts comprehensively, this project will consider all three areas with the following RQs.
RQ1: How can analysts be supported in deriving PM questions for their analysis? RQ2: How can analysts be supported in adopting efective analysis behavior during the mining and analysis phase to answer PM questions? RQ3: How can analysts be supported in evaluating the answers derived during the analysis?
By answering the aforementioned RQs, our goal is to develop a comprehensive framework to support PM analysts. Based on our current research status, we envision the framework to consist of, at least, the following methods and tools.
To address RQ1, we will develop a PM question bank containing questions retrieved from literature and practice, enriched by a taxonomy providing a question classification system. With the combination of these two components, we aim to support analysts in the identification of questions based on their analysis goals. To allow access to the taxonomy and the question bank, we plan to implement an interface enabling analysts to retrieve questions by selecting categories they are interested in. They can adopt questions from the question bank either directly, or use them as inspiration to formulate their own questions. Additionally, analysts will be enabled to categorize questions and ensure a common understanding of the question and the underlying PM concepts (e.g., bottlenecks, rework) based on the definitions of the categories of the taxonomy.
Method To collect a representative set of questions, we already performed a systematic literature review of case studies and Business Process Intelligence Challenge1 reports. The results of an online data collection complement the set. To develop the taxonomy, we will thoroughly follow the guidelines proposed by [18]. We will evaluate our taxonomy including its interface with experts and in a case study together with an industry partner to demonstrate its usefulness for the support of analysts.
To support analysts during the analysis (cf. RQ2), we first need to enhance the existing
understanding of what analysts do, how they do it, and also why they do it. Based on such an
enhanced understanding of the analysis process, we plan to propose a collection of efective
analysis patterns. In a first version, we will integrate these patterns into the taxonomy interface
(cf. RQ1), linked to questions they can be applied for. For a second version, proposing these
patterns in the form of traceable and re-playable analysis building blocks (similar to the idea
introduced in [
Our plan to address RQ3 is preliminary. We envision developing evaluation guidelines that help analysts objectively assess their PM answers following the design science research methodology. We will start by defining the problem space. To this end, a case study can help to identify the context and concrete gaps in the evaluation of diferent PM results. Afterward, we can propose adequate support measures, such as a method or rules that should be followed to ensure high-quality PM results that help process stakeholders achieve their goals. Compared to the authors in [17] who suggest guidelines for the qualitative evaluation of PM results together with a stakeholder group, we will focus on a quantitative assessment applicable to an individual analyst. Based on our knowledge, this will be one of the first works explicitly linking PM questions and answers and therefore providing analysts with guidance regarding objective assessment measures. Similar to the results of RQ1, we plan to evaluate the proposed guidelines both with experts as well as in a real-world setting (e.g., a case study with an industry partner).
I thank my PhD supervisor Barbara Weber, for her valuable guidance, and Francesca Zerbato, for her consistent support throughout this research.
This work is supported by the ProMiSE project funded by the SNSF under Grant No.: 200021_197032. [6] A. Seeliger, A. Sánchez Guinea, T. Nolle, M. Mühlhäuser, Processexplorer: intelligent process mining guidance, in: BPM, Springer, 2019, pp. 216–231. [7] K. Kubrak, F. Milani, A. Nolte, A visual approach to support process analysts in working with process improvement opportunities, Bus. Process Manag. J. 29 (2023) 101–132. [8] N. Martin, D. A. Fischer, G. D. Kerpedzhiev, K. Goel, S. J. Leemans, M. Röglinger, W. M. van der Aalst, M. Dumas, M. La Rosa, M. T. Wynn, Opportunities and challenges for process mining in organizations: results of a delphi study, Bus. Inf. Syst. Eng. 63 (2021) 511–527. [9] F. Zerbato, P. Sofer, B. Weber, Initial insights into exploratory process mining practices, in: BPM, Springer, 2021, pp. 145–161. [10] A. Strauss, J. M. Corbin, Grounded theory in practice, Sage, 1997. [11] L. Zimmermann, F. Zerbato, B. Weber, Process mining challenges perceived by analysts:
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