Now showing 1 - 5 of 5
  • 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
    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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  • Publication
    A Characterisation of Ambiguity in BPM
    ( 2023) ; ;
    Hugo A. Lopez
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    Andrea Burattin
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    Luciano Garcia Banuelos
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    Business Process Management is concerned with processrelated artefacts such as informal specifications, formal models, and event logs. Often, these process-related artefacts may be affected by ambiguity, which may lead to misunderstandings, modelling errors, non-conformance, and incorrect interpretations. To date, a comprehensive and systematic analysis of ambiguity in process-related artefacts is still missing. Here, following a systematic development process with strict adherence to established guidelines, we propose a taxonomy of ambiguity, identifying a set of concrete ambiguity types related to these process-related artefacts. The proposed taxonomy and ambiguity types help to detect the presence of ambiguity in process-related artefacts, paving the road for improved processes. We validate the taxonomy with external process experts.
  • Publication
    ProAmbitIon: Online Process Conformance Checking with Ambiguities Driven by the Internet of Things
    (CEUR-WS.org, 2023-06) ; ;
    Mauricio Jacobo González González
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    Enrique Garcia-Ceja
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    Luis Armando Rodríguez Flores
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    Luciano García-Bañuelos
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    Jaime Font
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    Lorena Arcega
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    José-Fabián Reyes-Román
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    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.
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  • Publication
    Data-driven Generation of Services for IoT-based Online Activity Detection
    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.
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