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Auflistung nach Schlagwort "Process Mining"

1 - 10 von 26
  • Zeitschriftenartikel
    Analyse von Studienverläufen mit Process-Mining-Techniken
    (HMD Praxis der Wirtschaftsinformatik: Vol. 55, No. 4, 2018) Buck-Emden, Rüdiger; Dahmann, Franz-Dominik
    Studenten an Hochschulen und Universitäten haben bei der Gestaltung ihrer Studienverläufe meistens viele Freiheitsgrade. Begrenzt werden diese Freiheiten durch das Curriculum, das bestimmte Rahmenbedingungen für den Studienverlauf einer Fachrichtung festlegt und Empfehlungen bzw. Vorgaben macht, welche Veranstaltungen in welchem Semester besucht werden sollen. In der Praxis weichen viele Studenten von den Empfehlungen des Curriculums ab. Dies führt zu einer Vielzahl individueller Studienverläufe, von denen jeder einzelne mehr oder weniger erfolgreich sein kann (z. B. in Hinblick auf das Erreichen des angestrebten Abschlusses, auf die erzielte Abschlussnote oder auf die benötigte Studiendauer). Für eine an erfolgreichen Studienverläufen orientierte Weiterentwicklung von Curricula und zugehörigen Studienberatungen fehlen den Verantwortlichen an Hochschulen und Universitäten nicht selten detaillierte Erkenntnisse über das konkrete Studienverhalten und über erfolgreiche bzw. weniger erfolgreiche Studienverlaufsmuster. Durch Process-Mining-Techniken wie Bubble-Chart-Analysen, Fuzzy Mining und Inductive Visual Mining können die Verantwortlichen Transparenz bei der Auswertung von Studienverläufen gewinnen und darauf aufbauend gezielte Maßnahmen einleiten. Students at universities usually enjoy a high level of freedom to shape their course of studies. Only the curriculum is the limit, which defines rules and suggests courses to be taken in certain semesters. In practice, many students deviate from these suggestions, leading to a plethora of individual schedules which may be more or less successful (with regards e. g. to achieved degree, final rates, and time to graduation). University officials in charge of evolving and enhancing curricula as well as student advisory services often do not have detailed knowledge regarding student’s specific behavior as well as successful and less successful study schedules. Here process mining techniques like bubble chart analytics, fuzzy mining, and inductive visual mining can fill the gap and provide transparency as foundation for dedicated measures.
  • Textdokument
    Anwendung von Machine Learning bei der datengetriebenen Prozessanalyse - Eine State-of-the-Art Literaturanalyse
    (INFORMATIK 2022, 2022) Welz,Laslo; Beckmann,Helmut
    Die Disziplin Process Mining im Anwendungsbereich der datengetriebenen Prozessanalyse ermöglich die Abbildung realer Geschäftsprozesse durch die Extrahierung von Daten aus Eventlogs von Informationssystemen. Konventionelles Process Mining kann mit Ansätzen aus dem Bereich Machine Learning ergänzt werden, um die Prozessanalysen zu verbessern. Anhand einer Literaturanalyse untersucht diese Forschungsarbeit die Anwendung von Machine Learning bei Process Mining. Die Ergebnisse aus einer Stichprobe von 34 Publikationen zeigen, dass in den beiden Process Mining Bereichen „Discovery“ und „Enhancement“ die meisten Machine Learning-Methoden angewendet werden. Insbesondere ist die Anwendung von Entscheidungsbäumen und Neuronalen Netzen weit verbreitet.
  • Textdokument
    Application Fields and Research Gaps of Process Mining in Manufacturing Companies
    (INFORMATIK 2020, 2021) Dreher, Simon; Reimann, Peter; Gröger, Christoph
    To survive in global competition with increasing cost pressure, manufacturing companies must continuously optimize their manufacturing-related processes. Thereby, process mining constitutes an important data-driven approach to gain a profound understanding of the actual processes and to identify optimization potentials by applying data mining and machine learning techniques on event data. However, there is little knowledge about the feasibility and usefulness of process mining specifically in manufacturing companies. Hence, this paper provides an overview of potential applications of process mining for the analysis of manufacturing-related processes. We conduct a systematic literature review, classify relevant articles according to the Supply-Chain-Operations-Reference-Model (SCOR-model), identify research gaps, such as domain-specific challenges regarding unstructured, cascaded and non-linear processes or heterogeneous data sources, and give practitioners inspiration which manufacturing-related processes can be analyzed by process mining techniques.
  • Konferenzbeitrag
    Applying Predictive Process Monitoring to Predict User Behavior with Click Data Behavior with Click Data - Short Paper
    (AKWI Jahrestagung 2024, 2024) Feiler, Jana; Walter, Tobias
    Predictive Process Monitoring (PPM) is used to predict the future behavior of running process instances using machine learning techniques. While traditional PPM focuses and works well on highly structured business processes, the planned research explores the application of and challenges related to PPM on rather unstructured processes, such as those discovered through user click traces on websites. Therefore, this resarch’s overall objective is to develop and evaluate appropriate AI-supported PPM approaches for unstructured processes based on click traces provided by Germany’s public broadcaster ARD with its audio and video streaming services ARD Mediathek & ARD Audiothek.
  • Zeitschriftenartikel
    Business process management for Industry 4.0 – Three application cases in the DFKI-Smart-Lego-Factory
    (it - Information Technology: Vol. 60, No. 3, 2018) Rehse, Jana-Rebecca; Dadashnia, Sharam; Fettke, Peter
    The advent of Industry 4.0 is expected to dramatically change the manufacturing industry as we know it today. Highly standardized, rigid manufacturing processes need to become self-organizing and decentralized. This flexibility leads to new challenges to the management of smart factories in general and production planning and control in particular. In this contribution, we illustrate how established techniques from Business Process Management (BPM) hold great potential to conquer challenges in Industry 4.0. Therefore, we show three application cases based on the DFKI-Smart-Lego-Factory, a fully automated “smart factory” built out of LEGO ® bricks, which demonstrates the potentials of BPM methodology for Industry 4.0 in an innovative, yet easily accessible way. For each application case (model-based management, process mining, prediction of manufacturing processes) in a smart factory, we describe the specific challenges of Industry 4.0, how BPM can be used to address these challenges, and, their realization within the DFKI-Smart-Lego-Factory.
  • Textdokument
    Describing Behavior Sequences of Fattening Pigs Using Process Mining
    (EMISA 2024, 2024) Lepsien, Arvid; Melfsen, Andreas; Bosselmann, Jan; Koschmider, Agnes; Hartung, Eberhard
    Process mining is a well-established technique for gaining insights into event data. It allows significant insights into event data in terms of identifying process anomalies, giving hints between as-is and to-be process states or making predictions based on data. Although process mining has been successfully applied in many application domains like healthcare, finance, and manufacturing, additional domains might also benefit from process mining like life and natural sciences. However, these domains mainly do not rely on structured business data that is expected as input for process mining algorithms. Rather, data from these domains first has to be efficiently pre-processed. This paper suggests process mining as an approach to identify behavioral patterns of fattening pigs from video data. The goal of this approach is to demonstrate that process mining might be a valuable tool for understanding the behavior of pigs by considering and analyzing their behavior sequences. Furthermore, additional insights can be gained in terms of temporal and spatial analysis about the division of the pig pen in functional areas. In this way, new implications might be found about pig behavior compared to existing state-of-the art approaches in the field.
  • Konferenzbeitrag
    Desktop Activity Mining - A new level of detail in mining business processes
    (Workshops der INFORMATIK 2018 - Architekturen, Prozesse, Sicherheit und Nachhaltigkeit, 2018) Linn, Christian; Zimmermann, Phileas; Werth, Dirk
    New analysis and automation technologies are significantly changing the way how business process management is performed. Especially Robotic ProcessAutomation (RPA) is rapidly gaining importance as a method to automate office processes. An efficient automation of office processes however requires detailed information about all user activities related to the process. While process mining techniques can in principle be used to discover processes in a data-driven way, the existing approaches are not able to gather information in a level of detail required for automation purposes. That is why in particular the configuration of RPA systems is a labor and knowledge-intensive task that is based on a human expert, modeling all process variations in detail. In this paper, we present Desktop Activity Mining as a new approach to mine detailed process activity data. The concept is to record the detailed desktop activities of all users performing an office process and consolidate the process variations with process mining techniques to discover an integrated process model. As a proof of concept, we realized a prototypical implementation. Our findings suggest that Desktop Activity Mining holds the potential to optimize not only process automation but also to derive a new level of detail in mining and analyzing business processes.
  • Konferenzbeitrag
    Enhancing Digital Twins for Production through Process Mining Techniques: A Literature Review
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Schumacher, Marcel; Buschermöhle, Ralf; Haak, Liane; Höfinghoff, Max; Seipolt, Arne; Korn, Goy-Hinrich
    A digital twin (DT) plays a vital role in the advancement of manufacturers towards Industry 4.0. However, the creation and maintenance of DTs can be time-consuming. One approach to streamline this process is the utilization of process mining (PM) methods and techniques, which can automatically generate valuable information for DTs. Therefore, this paper aims to examine different approaches that augment DTs with PM and explore their effects. The review categorizes these approaches into three groups: theoretical approaches, approaches with laboratory case studies, and approaches with real-world case studies conducted by manufacturers. The review reveals that the use of PM can enhance the flexibility and sustainability of DTs. However, this improvement comes at the cost of requiring high-quality data and more data preparation efforts.
  • Zeitschriftenartikel
    Die ethischen Implikationen von Prozessdaten: Eine systematische Literaturanalyse von Datenethik und Process Mining
    (HMD Praxis der Wirtschaftsinformatik: Vol. 61, No. 1, 2024) Gabdoulline, Gulnara; Kern, Christopher Julian; Krönung, Julia
    Process Mining (PM) stellt eine wachsende Disziplin dar, die aufgrund ihres Potenzials zur Verbesserung von Geschäftsprozessen immer mehr Aufmerksamkeit von Forschern und Anwendern auf sich zieht. Wie jede neue Technologie gibt es jedoch auch im Kontext von PM-Bedenken hinsichtlich der ethischen Anwendung. Gerade bezogen auf Erhebung, Verarbeitung und Nutzung von Daten kann es hierbei zu Problemen kommen. Dieser Artikel zielt daher darauf ab, anhand einer Literaturanalyse ethische Implikationen im Process Mining herauszuarbeiten. Dabei wurden 39 Artikel aus sechs Zeitschriften im Bereich PM und 24 Artikel aus vier Zeitschriften im Bereich Datenethik analysiert. Die Ergebnisse zeigen das wachsende Interesse an der Datenethik und PM, aber es befasst sich nur ein geringer Anteil der analysierten PM-Artikel mit datenethischen Grundsätzen. Weitere Forschung ist in Bereichen bestimmter datenethischer Grundsätze, wie Datenqualität und der informierten Zustimmung, erforderlich. Insgesamt bietet diese Studie einen Ausgangspunkt für weitere Forschungen zur ethischen Nutzung von Daten bei der Anwendung von PM und verdeutlicht, dass diesem Bereich mehr Aufmerksamkeit gewidmet werden sollte. Process Mining (PM) is of increasing attention for researchers and practitioners alike, especially in regards of the potential to improve business processes in their efficiency. However, like any new data-driven technology, PM raises ethical concerns about the collection, processing, and use of data. The purpose of this paper is to demonstrate ethical implications in process mining by conducting a literature review. To do so, 39 articles from six journals in the field of PM and 24 articles from four journals in the field of data ethics were analyzed. The results show that despite there being growing interest in data ethics and PM, only a small percentage of the PM articles analyzed address data ethical principles. This highlights the need for clear ethical guidelines for use in PM. Further research is needed in areas of specific data ethics principles such as data quality and informed consent. As a result, this review provides a foundation for further research on the ethical use of data in the application of PM.
  • Konferenzbeitrag
    Folding Marked Generalized Stochastic Petri Nets for Time Prediction in Business Processes
    (Workshops der INFORMATIK 2018 - Architekturen, Prozesse, Sicherheit und Nachhaltigkeit, 2018) Fahrenkrog-Petersen, Stephan A.; Weidlich, Matthias
    Generalized Stochastic Petri Nets (GSPNs) can be used for performance analysis of business processes. Recently, it was shown that foldings of a GSPN, i.e., a set of model reduction rules, help to avoid over-fitting of the model with respect to the performance characteristics of a process. Yet, these foldings ignore the marking of a GSPN and, thus, are applicable solely for steady-state analysis. In this paper, we discuss how foldings may be lifted to marked nets and provide an assessment of stateful foldings for sequential GSPNs.