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Auflistung nach Schlagwort "Process mining"

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  • Zeitschriftenartikel
    Entwicklung einer VR-Umgebung zur Exploration von Process-Mining
    (HMD Praxis der Wirtschaftsinformatik: Vol. 59, No. 1, 2022) Wetzel, Manuel; Koschmider, Agnes
    VR-Umgebungen werden bereits in zahlreichen Anwendungsszenarien erfolgreich zur Visualisierung von Daten mit dem Ziel beispielsweise der Lernprozessunterstützung eingesetzt. Dieser Beitrag stellt eine VR-basierte Umgebung für Process-Mining mit dem Ziel der Prozessanalyseunterstützung vor. Die VR-basierte Umgebung ermöglicht, Prozessdaten aus Quellsystemen dynamisch anzubinden und zu laden und diese als ein dreidimensionales Prozessmodell zu visualisieren und zu analysieren. Die VR-Umgebung wurde systematisch basierend auf einer Anforderungsanalyse konzipiert, die aus kommerziellen (2D) Process-Mining Werkzeugen und verwandten Arbeiten aus der Literatur abgeleitet wurde. Bei der Implementierung der Umgebung wurde Wert auf Erweiterbarkeit und Offenheit der Umgebung gelegt. Im Gegensatz zu einer zweidimensionalen Visualisierung ermöglicht die VR-Umgebung für Process-Mining eine verbesserte Exploration der Zusammenhänge zwischen Prozessen und Daten (z. B. Prozesskennzahlen wie Ressourcenauslastung, Durchlaufzeit, Prozessabweichungen). VR environments are already being used successfully in numerous application scenarios for the visualization of data with the aim of e.g., supporting learning processes. This paper presents a VR-based environment for process mining with the aim of supporting process analysis. The VR-based environment makes it possible to dynamically link and load process data from source systems and to visualize and analyze them as a three-dimensional process model. The VR environment was systematically designed on a requirements analysis derived from commercial (2D) process mining tools and related work from the literature. When implementing the environment, emphasis was placed on extensibility and openness of the environment. In contrast to a two-dimensional visualization, the VR environment for process mining supports an improved exploration of the relationships between processes and data (e.g., KPIs such as resource utilization, lead time, process deviations).
  • Textdokument
    Event sequence analysis and visualization
    (EMISA 2024, 2024) Yeshchenko, Anton; Mendling, Jan
    Event sequence analysis is an important field of computer science due to its relevance to a diverse spectrum of application domains such as manufacturing, logistics, healthcare, financial services, education, to name but a few. Despite this broad relevance across these domains, it is striking to observe that techniques for event sequence data analysis have been developed rather independently in different fields of computer science. The most prominent research fields investigating the analysis of event sequence data are process mining and information visualization. Process mining has emerged as a subfield of research on workflow management systems. Its focus is the development of new techniques for automatic process discovery from event sequence data with the ambition to provide a meaningful and understandable summary of the behavior to the business process analyst. Information visualization is a field of computer graphics, which originated as a subfield of human–computer interaction. Its focus is on devising new techniques for visualizing event sequence data in a meaningful way such that analysts can effectively explore them. Typical representations frequently used in this field are timelines that plot conceptually related sequences of events over a time axis. As similar as the ambitions of these research areas may sound, it is surprising that there is hardly any exchange of ideas. Cross-references are scarce and mutual awareness and understanding is limited.1 All this makes research on event sequence analysis a fragmented field with scattered contributions. So far, the contributions from these two fields have neither been compared nor have they been mapped to an integrated framework. At this stage, it is not clear to which extent both fields have developed complementary concepts and insights. Such intransparency is problematic since it bears the risk of opportunities of integration are missed and concepts established in one field are independently reinvented in the other one. In this current research talk at EMISA 2024 based on a recent article, we develop such a framework that we call Event Sequence Visualization framework (ESeVis) and that gives due credit to the traditions of both fields. Our mapping study provides an integrated perspective on both fields and potential synergies for future research. In this way, our work contributes towards overcoming the fragmentation of research on event sequence data analysis.
  • Zeitschriftenartikel
    Extracting Best-Practice Using Mixed-Methods
    (Business & Information Systems Engineering: Vol. 63, No. 6, 2021) Poppe, Erik; Pika, Anastasiia; Wynn, Moe Thandar; Eden, Rebekah; Andrews, Robert; Hofstede, Arthur H. M.
    Problem Definition: Queensland’s Compulsory Third-Party (CTP) Insurance Scheme provides a mechanism for persons injured as a result of a motor vehicle accident to receive compensation. Managing CTP claims involves multiple stakeholders with potentially conflicting interests. It is therefore pertinent to investigate whether ‘best practice’ for claims processing can be identified and measured so all claimants receive fair and equitable treatment. The project set out to test the applicability of a mixed-method approach to identify ‘best-practice’ using qualitative, process mining, and data mining techniques in an insurance claims processing domain. Relevance: Existing approaches typically identify ‘best practice’ from literature or surveys of practitioners. The study provides insights into an alternative, mixed-method approach to deriving best practice from historical data and domain knowledge. Methodology: The study is a reflective analysis of insights gained from a practical application of a mixed-method approach to determine ‘best practice’. Results: The mixed-method approach has a number of benefits over traditional approaches in uncovering best practice process behavior from historical data in the real-world context (i.e., can identify process behavior differences between high and low performing cases). The study also highlights a number of challenges with regards to the quality and detail of data that needs to be available to perform the analysis. Managerial Implications: The ‘lessons learned’ from this study will directly benefit others seeking to implement a data-driven approach to understand a ‘best-practice’ process in their own organization.
  • Zeitschriftenartikel
    Improving Process Mining Maturity – From Intentions to Actions
    (Business & Information Systems Engineering: Vol. 66, No. 5, 2024) Brock, Jonathan; Brennig, Katharina; Löhr, Bernd; Bartelheimer, Christian; Enzberg, Sebastian; Dumitrescu, Roman
    Process mining is advancing as a powerful tool for revealing valuable insights about process dynamics. Nevertheless, the imperative to employ process mining to enhance process transparency is a prevailing concern for organizations. Despite the widespread desire to integrate process mining as a pivotal catalyst for fostering a more agile and flexible Business Process Management (BPM) environment, many organizations face challenges in achieving widespread implementation and adoption due to deficiencies in various dimensions of process mining readiness. The current Information Systems (IS) knowledge base lacks a comprehensive framework to aid organizations in augmenting their process mining readiness and bridging this intention-action gap. The paper presents a Process Mining Maturity Model (P3M), refined through multiple iterations, which outlines five factors and 23 elements that organizations must address to increase their process mining readiness. The maturity model advances the understanding of how to close the intention-action gap of process mining initiatives in multiple dimensions. Furthermore, insights from a comprehensive analysis of data gathered in eleven qualitative interviews are drawn, elucidating 30 possible actions that organizations can implement to establish a more responsive and dynamic BPM environment by means of process mining.
  • Zeitschriftenartikel
    Multi-Perspective Clustering of Process Execution Traces
    (Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 14, Nr. 2, 2019) Jablonski, Stefan; Röglinger, Maximilian; Schönig, Stefan; Wyrtki, Katrin Maria
    Process mining techniques enable extracting process models from process event logs. Problems can arise if process mining is applied to event logs of flexible processes that are extremely heterogeneous. Here, trace clustering can be used to reduce the complexity of logs. Common techniques use isolated criteria such as activity profiles for clustering. Especially in flexible environments, however, additional data attributes stored in event logs are a source of unused knowledge for trace clustering. In this paper, we present a multi-perspective trace clustering approach that improves the homogeneity of trace subsets. Our approach provides an integrated definition of similarity between traces by defining a distance measure that combines information about executed activities, performing resources, and data values. The evaluation with real-life event logs, one from a hospital and one with traffic fine data, shows that the homogeneity of the resulting clusters can be significantly improved compared to existing techniques.
  • Zeitschriftenartikel
    No Longer Out of Sight, No Longer Out of Mind? How Organizations Engage with Process Mining-Induced Transparency to Achieve Increased Process Awareness
    (Business & Information Systems Engineering: Vol. 63, No. 5, 2021) Eggers, Julia; Hein, Andreas; Böhm, Markus; Krcmar, Helmut
    In recent years, process mining has emerged as the leading big data technology for business process analysis. By extracting knowledge from event logs in information systems, process mining provides unprecedented transparency of business processes while being independent of the source system. However, despite its practical relevance, there is still a limited understanding of how organizations act upon the pervasive transparency created by process mining and how they leverage it to benefit from increased process awareness. Addressing this gap, this study conducts a multiple case study to explore how four organizations achieved increased process awareness by using process mining. Drawing on data from 24 semi-structured interviews and archival sources, this study reveals seven sociotechnical mechanisms based on process mining that enable organizations to create either standardized or shared awareness of sub-processes, end-to-end processes, and the firm’s process landscape. Thereby, this study contributes to research on business process management by revealing how process mining facilitates mechanisms that serve as a new, data-driven way of creating process awareness. In addition, the findings indicate that these mechanisms are influenced by the governance approach chosen to conduct process mining, i.e., a top-down or bottom-up driven implementation approach. Last, this study also points to the importance of balancing the social complications of increased process transparency and awareness. These results serve as a valuable starting point for practitioners to reflect on measures to increase organizational process awareness through process mining.
  • Zeitschriftenartikel
    Opportunities and Challenges for Process Mining in Organizations: Results of a Delphi Study
    (Business & Information Systems Engineering: Vol. 63, No. 5, 2021) Martin, Niels; Fischer, Dominik A.; Kerpedzhiev, Georgi D.; Goel, Kanika; Leemans, Sander J. J.; Röglinger, Maximilian; van der Aalst, Wil M. P.; Dumas, Marlon; La Rosa, Marcello; Wynn, Moe T.
    Process mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of process mining in organizations has been far more limited. In particular, there is limited understanding of the opportunities and challenges of using process mining in organizations. Such an understanding has the potential to guide research by highlighting barriers for process mining adoption and, thus, can contribute to successful process mining initiatives in practice. In this respect, the paper provides a holistic view of opportunities and challenges for process mining in organizations identified in a Delphi study with 40 international experts from academia and industry. Besides proposing a set of 30 opportunities and 32 challenges, the paper conveys insights into the comparative relevance of individual items, as well as differences in the perceived relevance between academics and practitioners. Therefore, the study contributes to the future development of process mining, both as a research field and regarding its application in organizations.
  • Zeitschriftenartikel
    Privacy-Preserving Process Mining
    (Business & Information Systems Engineering: Vol. 61, No. 5, 2019) Mannhardt, Felix; Koschmider, Agnes; Baracaldo, Nathalie; Weidlich, Matthias; Michael, Judith
    Privacy regulations for data can be regarded as a major driver for data sovereignty measures. A specific example for this is the case of event data that is recorded by information systems during the processing of entities in domains such as e-commerce or health care. Since such data, typically available in the form of event log files, contains personalized information on the specific processed entities, it can expose sensitive information that may be traced back to individuals. In recent years, a plethora of methods have been developed to analyse event logs under the umbrella of process mining. However, the impact of privacy regulations on the technical design as well as the organizational application of process mining has been largely neglected. This paper set out to develop a protection model for event data privacy which applies the well-established notion of differential privacy. Starting from common assumptions about the event logs used in process mining, this paper presents potential privacy leakages and means to protect against them. The paper also shows at which stages of privacy leakages a protection model for event logs should be used. Relying on this understanding, the notion of differential privacy for process discovery methods is instantiated, i.e., algorithms that aim at the construction of a process model from an event log. The general feasibility of our approach is demonstrated by its application to two publicly available real-life events logs.
  • Zeitschriftenartikel
    Process Mining for Six Sigma
    (Business & Information Systems Engineering: Vol. 63, No. 3, 2021) Graafmans, Teun; Turetken, Oktay; Poppelaars, Hans; Fahland, Dirk
    Process mining offers a set of techniques for gaining data-based insights into business processes from event logs. The literature acknowledges the potential benefits of using process mining techniques in Six Sigma-based process improvement initiatives. However, a guideline that is explicitly dedicated on how process mining can be systematically used in Six Sigma initiatives is lacking. To address this gap, the Process Mining for Six Sigma (PMSS) guideline has been developed to support organizations in systematically using process mining techniques aligned with the DMAIC (Define-Measure-Analyze-Improve-Control) model of Six Sigma. Following a design science research methodology, PMSS and its tool support have been developed iteratively in close collaboration with experts in Six Sigma and process mining, and evaluated by means of focus groups, demonstrations and interviews with industry experts. The results of the evaluations indicate that PMSS is useful as a guideline to support Six Sigma-based process improvement activities. It offers a structured guideline for practitioners by extending the DMAIC-based standard operating procedure. PMSS can help increasing the efficiency and effectiveness of Six Sigma-based process improving efforts. This work extends the body of knowledge in the fields of process mining and Six Sigma, and helps closing the gap between them. Hence, it contributes to the broad field of quality management.
  • Zeitschriftenartikel
    Repairing Alignments of Process Models
    (Business & Information Systems Engineering: Vol. 62, No. 4, 2020) Zelst, Sebastiaan J.; Buijs, Joos C. A. M.; Vázquez-Barreiros, Borja; Lama, Manuel; Mucientes, Manuel
    Process mining represents a collection of data driven techniques that support the analysis, understanding and improvement of business processes. A core branch of process mining is conformance checking, i.e., assessing to what extent a business process model conforms to observed business process execution data. Alignments are the de facto standard instrument to compute such conformance statistics. However, computing alignments is a combinatorial problem and hence extremely costly. At the same time, many process models share a similar structure and/or a great deal of behavior. For collections of such models, computing alignments from scratch is inefficient, since large parts of the alignments are likely to be the same. This paper presents a technique that exploits process model similarity and repairs existing alignments by updating those parts that do not fit a given process model. The technique effectively reduces the size of the combinatorial alignment problem, and hence decreases computation time significantly. Moreover, the potential loss of optimality is limited and stays within acceptable bounds.