Aaron Kurz

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Kurz
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Aaron
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aaron.kurz@unisg.ch

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    Item type:Publication,
    Generating Stream Processing Applications for IoT-based Process Event Abstraction: A Framework and Architecture
    (Gesellschaft für Informatik, 2026-03)
    Ronny Seiger  
    ;
    Aaron Kurz  
    ;
    Marco Franceschetti  
    Events play an important role in information systems engineering to facilitate the analysis of relevant happenings in a system or business process via process mining. The Internet of Things (IoT) provides new ways of collecting execution-related events in real world-physical-systems using sensors and actuators. However, these new sources emit data at a too fine-grained level, which prevents process mining from deriving meaningful insights. We present a generic event abstraction framework to lift low-level data to higher level events. Starting with annotated IoT data, we generate stream processing applications that encode change patterns derived from the low-level data. These applications are then used for detecting events and activities at runtime within our proposed software architecture. We evaluate the approach for process executions in smart manufacturing and smart healthcare.
    Type:conference contribution
    Journal:International Workshop on Enterprise Modeling and Information Systems Architecture (EMISA)
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/125214
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    Item type:Publication,
    A Domain-specific Language and Architecture for Detecting Process Activities from Sensor Streams in IoT (Journal First Presentation)
    (2026-09)
    Ronny Seiger  
    ;
    Daniel Locher
    ;
    Marco Kaufmann
    ;
    Aaron Kurz  
    Modern Internet of Things (IoT) systems are equipped with a large quantity of sensors providing real-time data about the current operations of their components, which is crucial for the systems’ internal control systems and processes. However, these data are often too fine-grained to derive useful insights into the execution of the larger processes an IoT system might be part of. Process mining has developed advanced approaches for the analysis of business processes that may also be used in the context of IoT. Bringing process mining to IoT requires an event abstraction step to lift the low-level sensor data to the business process level. In this work, we aim to enable domain experts to perform this step using a newly developed domain-specific language (DSL) called Radiant. Radiant supports the specification of patterns within the sensor data that indicate the execution of higher level process activities. These patterns are translated to complex event processing (CEP) applications to be used for detecting activity executions at runtime. We propose a corresponding software architecture that enables online event abstraction from IoT sensor streams using the CEP applications. We evaluate these applications to monitor activity executions in smart manufacturing and smart healthcare. These evaluations are useful to inform the domain expert about the quality of activity detections based on the specified patterns and potential for improvement via additional or modified patterns and sensors.
    Type:presentation
    Journal:International Conference on Enterprise Design, Operations, and Computing (EDOC)
    URL:https://conf.researchr.org/details/cbi-edoc-2026/cbi-edoc-2026-journal-first/1/A-Domain-specific-Language-and-Architecture-for-Detecting-Process-Activities-from-Sen
    URI:https://alexandria.unisg.ch/handle/20.500.14171/136623