ProMiSE: Process Mining Support for End-users

Type
fundamental research project
Start Date
November 2020
End Date
October 2024
URI
Acronym
ProMiSE
Status
ongoing
Keywords
Process mining guidance
Analysis of interaction traces
User behavior analysis
Process of Process Mining
Process Mining
Description
The rapid progress of digital transformation requires organizations to find intelligent ways of exploiting the vast amounts of data stored in their information systems to maintain a competitive edge. In this scenario, organizations are increasingly resorting to process mining to discover process models from event data and drive process improvement. Process mining blends principles of data mining and machine learning to extract meaningful process-related information from event data to understand and streamline processes. Over the last decade, process mining has grown into an established discipline, gaining remarkable momentum in both academia and industry. However, despite the unquestioned success of process mining, research has witnessed a lack of guidance for conducting many process mining tasks, such as creating and understanding event logs or explaining the obtained results. Indeed, some process mining tasks, such as data familiarization and process discovery, are inherently exploratory, meaning that neither the analysis goal nor the set of actions that analysts are expected to perform is precisely stated. The analysis process is unstructured and characterized by knowledge-intensive tasks executed repeatedly, as analysts often rely on their technical skills and experience to explore data. This, in turn, challenges the development of guidance for non-expert users, including novice analysts. So far, process mining research has focused mainly on technical aspects to improve algorithms and applications, while user behavior and the needs of process analysts received limited attention. However, gaining a comprehensive understanding of the analysis process, and especially of its exploratory phases, is crucial for supporting novice analysts effectively during the analysis. Thus, the central goal of this project is to gain a comprehensive understanding of how analysts do process mining in practice, i.e., the ``process of process mining'', to develop methodological guidance and operational support to assist novice analysts during the analysis effectively. To gain insights into the process of process mining, we will combine user interaction data with process mining applications (implicit feedback) and subjective insights gathered through retrospective interviews (explicit feedback). Interaction traces will be recorded with the help of a specialized tool, called ProMiSter, during observational studies on exploratory process mining tasks, conducted with process mining experts and novice analysts. For the analysis, we will combine quantitative and qualitative state-of-the-art approaches from data mining, cluster analysis, and grounded theory. The main outcome of the analysis will be a comprehensive understanding of user behavior for exploratory process mining, including frequent patterns of effective and noneffective behavior, analysis profiles, common analysis strategies, and typical challenges. Analysis patterns, profiles, and strategies will, in turn, provide strong empirical evidence for the development of methodological guidance and operational support aimed to assist novice analysts during exploratory process mining, potentially overcoming some of the identified challenges. Building upon observed effective analysis processes, we will derive a set of process mining guidelines providing practical guidance during the analysis. Besides, building upon effective and noneffective patterns of behavior, we will integrate into ProMiSter operational support in the form of recommendations and warnings based on the kind of behavior detected. By building upon the analysis of user behavior, this project will contribute to significantly advance our understanding of how analysts conduct exploratory process mining tasks, potentially providing useful empirical evidence for the improvement of process mining approaches and applications designed to support non-expert users. Besides, the obtained insights and the developed tool and guidelines will be useful to support the training and education of novice analysts and process mining students.
Leader contributor(s)
Partner(s)
Andrea Burattin, Technical University of Denmark DTU Compute Technical University of Denmark
Tijs Slaats, Department of Computer Science University of Copenhagen
Pnina Soffer, Department of Information Systems University of Haifa
Funder

SNF – Third Party

Range
HSG + other universities + partners
Range (De)
HSG + andere Unis + Partner
Division(s)

ICS - Institute of Co...

SCS - School of Compu...

Eprints ID
248291
Funding code
197032

Filters

Settings

Now showing 1 - 2 of 2
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Leveraging Digital Trace Data to Investigate and Support Human-Centered Work Processes
    (Springer, 2024)
    Barbara Weber  
    ;
    Amine Abbad-Andaloussi  
    ;
    Marco Franceschetti  
    ;
    Ronny Seiger  
    ;
    Hagen Völzer  
    The ongoing digitization of processes in all domains of everyday life driven by IT systems shows great potential for process automation, analysis, and optimization. In the last decade process mining has advanced to an important and mature discipline of computer science research and has been widely adopted in industry. More recently,-acknowledging the huge potential of digital trace data to study processes-process science has been introduced as an interdisciplinary field studying how processes unfold over time. This paper discusses the potential that arises when using digital trace data not only in the context of highly automated processes but also to investigate humancentered (work) processes and elaborates on associated challenges. Examples range from the semi-automated storage and production processes in a smart factory to healthcare processes to process analysts performing process mining tasks and software engineers reading software artifacts like source code and process models.
    Type:conference paper
    Volume:2028
    URL:https://link.springer.com/chapter/10.1007/978-3-031-64182-4_1
    DOI:10.1007/978-3-031-64182-4_1
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/120549
    Scopus© Citations 5
  • Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    PEM4PPM: A Cognitive Perspective on the Process of Process Mining
    (2023-09)
    Elizaveta Sorokina
    ;
    Pnina Soffer
    ;
    Irit Hadar
    ;
    Uri Leron
    ;
    Francesca Zerbato  
    During the last decades, process mining (PM) has matured and rapidly increased in its adoption. Making sense of data is a main part of the work of PM analysts, which involves cognitive processes. Recent work has leveraged behavioral data to explain these processes. Still, the process of process mining (PPM) is yet to be well understood and a theoretical foundation for explaining how these processes unfold is missing. This paper attempts to fill this gap by understanding how PPM data can be analyzed in a theory-guided manner and what insights can be gained from this analysis. To investigate these aspects, we analyzed verbal data and interaction traces obtained from analysis sessions with 29 participants performing a PM task. The analysis was based on the Predictive Processing (PP) theory and the derived Prediction Error Minimization (PEM) process, anchored in cognitive science. The results include (1) a theoretical adaptation of the PEM theory to the PPM context, (2) four strategies utilized by PM analysts, identified, and validated based on the adapted theory, and (3) an understanding of the differences in performance between analysts using different strategies and independence of the expertise level and the strategy choice.
    Type:conference contribution
    Journal:Lecture Notes in Computer Science
    Volume:14159
    DOI:10.1007/978-3-031-41620-0_27
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/118617
    Scopus© Citations 17