Now showing 1 - 10 of 22
  • Publication
    The biggest business process management problems to solve before we die
    (Elsevier, 2023-01)
    Beerepoot, Iris
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    Ciccio, Claudio Di
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    Reijers, Hajo A.
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    Rinderle-Ma, Stefanie
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    Bandara, Wasana
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    Burattin, Andrea
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    Calvanese, Diego
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    Chen, Tianwa
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    Cohen, Izack
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    Depaire, Benoît
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    Federico, Gemma Di
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    Dumas, Marlon
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    van Dun, Christopher
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    Fehrer, Tobias
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    Fischer, Dominik A.
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    Gal, Avigdor
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    Indulska, Marta
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    Isahagian, Vatche
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    Klinkmüller, Christopher
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    Kratsch, Wolfgang
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    Leopold, Henrik
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    Looy, Amy Van
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    Lopez, Hugo
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    Lukumbuzya, Sanja
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    Mendling, Jan
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    Meyers, Lara
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    Moder, Linda
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    Montali, Marco
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    Muthusamy, Vinod
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    Reichert, Manfred
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    Rizk, Yara
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    Rosemann, Michael
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    Röglinger, Maximilian
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    Sadiq, Shazia
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    Slaats, Tijs
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    Simkus, Mantas
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    Someh, Ida Asadi
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    Weber, Ingo
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    Weske, Mathias
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    It may be tempting for researchers to stick to incremental extensions of their current work to plan future research activities. Yet there is also merit in realizing the grand challenges in one’s field. This paper presents an overview of the nine major research problems for the Business Process Management discipline. These challenges have been collected by an open call to the community, discussed and refined in a workshop setting, and described here in detail, including a motivation why these problems are worth investigating. This overview may serve the purpose of inspiring both novice and advanced scholars who are interested in the radical new ideas for the analysis, design, and management of work processes using information technology.
  • Publication
    An Interactive Method for Detection of Process Activity Executions from IoT Data
    The increasing number of IoT devices equipped with sensors and actuators pervading every domain of everyday life allows for improved automated monitoring and analysis of processes executed in IoT-enabled environments. While sophisticated analysis methods exist to detect specific types of activities from low-level IoT data, a general approach for detecting activity executions that are part of more complex business processes does not exist. Moreover, dedicated information systems to orchestrate or monitor process executions are not available in typical IoT environments. As a consequence, the large corpus of existing process analysis and mining techniques to check and improve process executions cannot be applied. In this work, we develop an interactive method guiding the analysis of low-level IoT data with the goal of detecting higher-level process activity executions. The method is derived following the exploratory data analysis of an IoT data set from a smart factory. We propose analysis steps, sensor-actuator-activity patterns, and the novel concept of activity signatures that are applicable in many IoT domains. The method shows to be valuable for the early stages of IoT data analyses to build a ground truth based on domain knowledge and decisions of the process analyst, which can be used for automated activity detection in later stages.
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  • Publication
    Integrating process management and event processing in smart factories: A systems architecture and use cases
    (Elsevier, 2022-05) ;
    Malburg, Lukas
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    Bergmann, Ralph
    The developments of new concepts for an increased digitization of manufacturing industries in the context of Industry 4.0 have brought about novel system architectures and frameworks for smart production systems. These range from generic frameworks for Industry 4.0 to domain-specific architectures for Industrial Internet of Things (IIoT). While most of the approaches include a service-based architecture for selective integration with enterprise systems, a close two-way integration of the production control systems and IIoT sensors and actuators with Process-Aware Information Systems (PAIS) on the management level for automation and mining of production processes is rarely discussed. This fusion of Business Process Management (BPM) with IIoT can be mutually beneficial for both research areas, but is still in its infancy. We propose a systems architecture for IIoT that shows how to integrate the low-level hardware components–sensors and actuators–of a smart factory with BPM systems. We discuss the software components and their interactions to address challenges of device encapsulation, integration of sensor events, and interaction with existing BPM systems. This integration is demonstrated within several use cases regarding process modeling, automation and mining for a smart factory model, showing benefits of using BPM technologies to analyze, control, and adapt discrete production processes in IIoT.
  • Publication
    HoloFlows: modelling of processes for the Internet of Things in mixed reality
    (Springer, 2021) ;
    Kühn, Romina
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    Korzetz, Mandy
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    Aßmann, Uwe
    Our everyday lives are increasingly pervaded by digital assistants and smart devices forming the Internet of Things (IoT). While user interfaces to directly monitor and control individual IoT devices are becoming more sophisticated and end-user friendly, applications to connect standalone IoT devices and create more complex IoT processes for automating and assisting users with repetitive tasks still require a high level of technical expertise and programming knowledge. Related approaches for process modelling in IoT mostly suggest extensions to complex modelling languages, require high levels of abstraction and technical knowledge, and rely on unintuitive tools. We present a novel approach for end-user oriented--no-code--IoT process modelling using Mixed Reality (MR) technology: HoloFlows. Users are able to explore the IoT environment and model processes among sensors and actuators as first class citizens by simply "drawing" virtual wires among physical IoT devices. MR technology hereby facilitates the understanding of the physical contexts and relations among the IoT devices and provides a new and more intuitive way of modelling IoT processes. The results of a user study comparing HoloFlows with classical modelling approaches show an increased user experience and decrease of required modelling knowledge and technical expertise to create IoT processes.
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    Scopus© Citations 29
  • Publication
    Immersives verteiltes Robotic Co-working
    (Springer, 2020-09-02) ;
    Aßmann, Uwe
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    Grzelak, Dominik
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    Belov, Mikhail
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    Riedel, Paul
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    Podlubne, Ariel
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    Zhao, Wanqi
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    Kerber, Jens
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    Mohr, Jonas
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    Espinosa, Fabio
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    Schwartz, Tim
    Scopus© Citations 1
  • Publication
    Method to Identify Process Activities by Visualizing Sensor Events
    (Springer, 2022-09)
    Weyers, Flemming
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    With the onset of the Internet of Things (IoT) everyday objects suddenly become data sources equipped with sensors measuring the object’s properties and surroundings. However, the lack of process-awareness in IoT environments (e.g., smart factories) prevents the adoption of more sophisticated process analysis and optimization. One hurdle is the differing abstraction level of low-level sensor data and process-level activities. We propose a method to identify activities step-by-step from raw IoT data using visualizations. By relying on minimal process knowledge, we discover process activities from sensor events. These activities are represented by specific sequences of sensor events–Activity Signatures–that serve as a basis for finding similar activities. We demonstrate the method’s validity with a proof of concept in a smart factory.
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  • Publication
    End-User Development of Internet of Things Processes in Augmented Reality (Extended Abstract)
    With the increasing number of IoT devices pervading our everyday surroundings there also emerges the desire of interlinking devices and creating processes among them for automation purposes. This “programming” of IoT environments currently requires a high level of technical expertise and abstraction capabilities, which hinders end-users from adapting these new technologies. We present HoloFlows–an approach for end-user oriented IoT process development in augmented reality– consisting of a simple modeling language and an intuitive user interface with natural interactions. HoloFlows showed an increased efficiency and user experience as well as steep learning curve for non-experts in a user study and maybe one way of programming the smart home and IoT of the future.
  • Publication
    Granularity in Process Mining: Can We Fix It?
    ( 2021-09) ; ;
    Di Federico, Gemma
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    Burattin, Andrea
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    Process mining techniques rely on the availability of event logs, where events have a certain granularity that is deemed appropriate for representing business activities. In this paper, we discuss why choosing a proper granularity level during preprocessing can be challenging and reflect on the implications that such a “fixed” view over the process bears for the analysis. Then, inspired by use cases in the context of user behavior analysis, we envision possible solutions that allow exploring and mining multiple granularity levels of process activities.
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  • Publication
    An Object-centric Approach to Handling Concurrency in IoT-aware Processes
    ( 2023-09)
    Florian Gallik
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    Yusuf Kirikkayis
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    Manfred Reichert
    The increasing adoption of IoT in the context of Business Process Management (BPM) makes it necessary to efficiently coordinate concurrent processes and activities that involve physical resources. Traditional approaches to handling concurrency in BPM systems are not suitable for automating IoT-aware processes due to novel challenges raised by the IoT. We propose to handle concurrency in IoT based on objectcentric processes implemented in the PHILharmonicFlows framework. The framework facilitates the data-driven modeling and coordination of object lifecycles and interactions, which are suitable to address concurrency in IoT-aware processes. The approach is demonstrated in a scenario from smart manufacturing. The results show that PHILharmonicFlows offers a flexible and comprehensible solution for coordinating concurrent activities in IoT settings with constrained physical resources.
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  • Publication
    An Event-Centric Metamodel for IoT-Driven Process Monitoring and Conformance Checking
    Process monitoring and conformance checking analyze process events describing process executions. However, such events are not always available or in a form suitable for these analysis tasks, for example for manual processes and (semi-)automated processes whose executions are not controlled by a Process-Aware Information System. To bridge this gap, we propose to leverage Internet of Things (IoT) technologies for sensing low-level events and abstracting them into high-level process events to enable process monitoring and conformance checking. We propose an event-centric metamodel for monitoring and conformance checking systems that is agnostic with respect to process characteristics such as level of automation, system support, and modeling paradigm. We demonstrate the applicability of the metamodel by instantiating it for processes represented by different modeling paradigms.
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