Ronny Seiger
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Seiger
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Ronny
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ronny.seiger@unisg.ch
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  • Publication
    An Object-centric Approach to Handling Concurrency in IoT-aware Processes
    ( 2023-09)
    Florian Gallik
    ;
    Yusuf Kirikkayis
    ;
    Ronny Seiger orcid-logo
    ;
    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.
    Type: conference contribution
    Journal: 21st International Conference on Business Process Management (BPM) Workshops
    DOI: 10.1007/978-3-031-50974-2_9
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/117945
  • Publication
    ProAmbitIon: Online Process Conformance Checking with Ambiguities Driven by the Internet of Things
    (CEUR-WS.org, 2023-06)
    Marco Franceschetti orcid-logo
    ;
    Ronny Seiger orcid-logo
    ;
    Mauricio Jacobo González González
    ;
    Enrique Garcia-Ceja
    ;
    Luis Armando Rodríguez Flores
    ;
    Luciano García-Bañuelos
    ;
    Barbara Weber orcid-logo
    ;
    Jaime Font
    ;
    Lorena Arcega
    ;
    José-Fabián Reyes-Román
    ;
    Giovanni Giachetti
    The ongoing digitization of processes in everyday life shows great potential for process automation, analysis, and optimization. However, digital traces of processes in the physical world, especially those involving human interactions, are often incomplete. This limits the possibilities for an automated process monitoring and analysis. ProAmbitIon proposes to use the Internet of Things (IoT) to bridge the gap between physical world process executions and their digital traces. In this project we leverage software-controlled sensors and actuators to enable a fine-grained monitoring and contextualization of process activities. Digital traces of executed processes can be created from and enriched with IoT data, and used for conformance checking to detect deviations-even at runtime and without relying on a Business Process Management System (BPMS). In developing new approaches for IoT-driven process conformance checking, we also address the issue of potential ambiguities originating from 1) informal process descriptions and 2) the lack of process-related data in IoT data. The project is conducted using real-world scenarios from smart healthcare and smart manufacturing.
    Type: conference contribution
    Journal: CAiSE 2023
    Volume: Vol-3413
    URL: https://ceur-ws.org/Vol-3413/paper8.pdf
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/117450
  • Publication
    Data-driven Generation of Services for IoT-based Online Activity Detection
    ( 2023-11)
    Ronny Seiger orcid-logo
    ;
    Marco Franceschetti orcid-logo
    ;
    Barbara Weber orcid-logo
    Business process management (BPM) technologies are increasingly adopted in the Internet of Things (IoT) to analyze processes executed in the physical world. Process mining is a mature discipline for analyzing business process executions from digital traces recorded by information systems. In typical IoT environments there is no central information system available to create homogeneous execution traces. Instead, many distributed devices including sensors and actuators produce low-level IoT data related to their operations, interactions and surroundings. We leverage this data to monitor the execution of activities and to create events suitable for process mining. We propose a framework to generate activity detection services from IoT data and a software architecture to execute these services. Our proof-of-concept implementation is based on an extensible complex event processing platform enabling the online detection of activities from IoT data. We use a running example from smart manufacturing to showcase the framework.
    Type: conference contribution
    Journal: 21st International Conference on Service-Oriented Computing (ICSOC)
    DOI: https://doi.org/10.1007/978-3-031-48424-7_14
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/118255
  • Publication
    Integrating IoT-Driven Events into Business Processes
    (Springer Nature Switzerland AG, 2023-06)
    Yusuf Kirikkayis
    ;
    Florian Gallik
    ;
    Ronny Seiger orcid-logo
    ;
    Manfred Reichert
    ;
    Cristina Cabanillas
    ;
    Francisca Pérez
    Extending Business Process Management (BPM) with Internet of Things (IoT) enhances process automation, improves process monitoring, and enables decision making based on data from the physical world. In this context, the transformation of low-level IoT data to process-level IoT-driven events constitutes an important step. However, the modeling of these transformations is challenging due to the complexity of IoT environments. Contemporary approaches do not provide sufficient support to model these transformations. Process models either become too complex with increasing numbers of devices and transformations, or this logic is externalized and viewed as a black box. This paper presents an integrated approach to model IoT-driven events in processes based on a BPMN 2.0 extension and a tool that adopts concepts from DMN. A scenario from smart production is used to demonstrate the application and improved integration of IoT-driven events in processes.
    Type: conference contribution
    Journal: Intelligent Information Systems
    URL: https://doi.org/10.1007/978-3-031-34674-3_11
    DOI: 10.1007/978-3-031-34674-3_11
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/117414
    Scopus© Citations 3
  • Publication
    From Internet of Things Data to Business Processes: Challenges and a Framework
    ( 2024-05)
    Jürgen Mangler
    ;
    Ronny Seiger orcid-logo
    ;
    Benzin, Janik-Vasily
    ;
    Grüger, Joscha
    ;
    Kirikkayis, Yusuf
    ;
    Gallik, Florian
    ;
    Malburg, Lukas
    ;
    Ehrendorfer, Matthias
    ;
    Bertrand, Yannis
    ;
    Marco Franceschetti orcid-logo
    ;
    Barbara Weber orcid-logo
    ;
    Rinderle-Ma, Stefanie
    ;
    Bergmann, Ralph
    ;
    Asensio, Serral
    ;
    Reichert, Manfred
    The IoT and Business Process Management (BPM) communities co-exist in many shared application domains, such as manufacturing and healthcare. The IoT community has a strong focus on hardware, connectivity and data; the BPM community focuses mainly on finding, controlling, and enhancing the structured interactions among the IoT devices in processes. While the field of Process Mining deals with the extraction of process models and process analytics from process event logs, the data produced by IoT sensors often is at a lower granularity than these process-level events. The fundamental questions about extracting and abstracting process-related data from streams of IoT sensor values are: (1) Which sensor values can be clustered together as part of process events?, (2) Which sensor values signify the start and end of such events?, (3) Which sensor values are related but not essential? This work proposes a framework to semi-automatically perform a set of structured steps to convert low-level IoT sensor data into higher-level process events that are suitable for process mining. The framework is meant to provide a generic sequence of abstract steps to guide the event extraction, abstraction, and correlation, with variation points for plugging in specific analysis techniques and algorithms for each step. To assess the completeness of the framework, we present a set of challenges, how they can be tackled through the framework, and an example on how to instantiate the framework in a real-world demonstration from the field of smart manufacturing. Based on this framework, future research can be conducted in a structured manner through refining and improving individual steps.
    Type: forthcoming
    URL: https://arxiv.org/abs/2405.08528
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/120149
  • Publication
    Integrating Process Management and Event Processing in Smart Factories: A Systems Architecture and Use Cases (Journal First Presentation)
    ( 2023-06-14)
    Ronny Seiger orcid-logo
    ;
    Lukas Malburg
    ;
    Barbara Weber orcid-logo
    ;
    Ralph Bergmann
    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.
    Type: presentation
    Journal: Conference on Advanced Information Systems Engineering (CAiSE)
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/117500