Ronny Seiger
19 results
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Item type:Publication, Generating Stream Processing Applications for IoT-based Process Event Abstraction: A Framework and ArchitectureEvents 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 contributionJournal:International Workshop on Enterprise Modeling and Information Systems Architecture (EMISA) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Distributed Computing with Language Models at the Edge: A Framework and Prototype(ACM, 2025)Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A semi-automated approach to detecting process-level activities from sensor data(Elsevier, 2025-04); ; Silva Trujillo, Alejandra GuadalupeInternet of Things (IoT) technologies can be leveraged to monitor and check the conformance of business process executions in the absence of a Business Process Management (BPM) System. This poses the challenge of detecting process activities from lowlevel sensor data by means of event abstraction. Existing methods rely on fully supervised approaches, combining sensor data and process event logs to train classification models. However, frequently these logs are not available, invalidating the applicability of such methods. In this paper, we propose a semi-automated approach to detecting process activities from sensor data based on frequently repeated subsequences and automata. We evaluate the approach with a proof-of-concept implementation and data from a small-scale smart factory. Our evaluation demonstrates the applicability of the proposed approach and its effectiveness in detecting process activities from sensor data to support domain experts with data analysis through automated suggestions.Type:conference contributionJournal:Procedia Computer ScienceScopus© Citations 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Toward IoT-based Process Analytics: Extending Event Knowledge Graphs with AmbiguityDigital traces of processes executed in the physical world without Information Systems (IS) support are composed of high-level events derived from low-level physical world events (e.g., from IoT). Due to this, frequently these high-level events are ambiguous, i.e., they yield multiple interpretations. As current IS lack ambiguity-awareness, ambiguity in digital traces can compromise process analytics. Motivated by this and current trends toward multi-dimensional analytics, we introduce an ambiguity-aware object-centric representation of digital traces by extending Event Knowledge Graphs with ambiguity. We integrate its construction into a framework to enable analytics for ambiguous IoT-based digital traces. A prototype implementation shows the applicability of the construction from ambiguous process event streams in online settings.Type:conference contributionJournal:Enterprise, Business-Process and Information Systems ModelingScopus© Citations 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, ProAmbitIon, reloaded: A two-year retrospectionThe rapid digital transformation of business processes holds significant promise for enhancing process automation, analysis, and optimization. However, digital traces of real-world processes-particularly those involving human activities-are frequently incomplete, thereby constraining the capabilities for automated process analysis. With the ProAmbitIon project, we address this challenge by leveraging the Internet of Things (IoT) to bridge the gap between real-world process executions and their digital representations. First, by augmenting the process environment with sensors, we enable a fine-grained monitoring and contextualization of process activities. Next, by generating and enriching digital traces from and with IoT data, we enable online conformance checking without depending on traditional information systems. With the development of new approaches for IoT-driven process conformance checking, we also address the issue of ambiguities originating from process-related artifacts. The project is validated through real-world scenarios from healthcare and manufacturing. We report on the results and insights from the first two years of the project, and outline current work and next steps.Type:conference contributionJournal:Research Projects Exhibition @ CAiSE 2025 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Case-Based Activity Detection from Segmented Internet of Things DataThe use of Internet of Things (IoT) technologies drives the automation of business processes. However, such environments often lack process awareness and corresponding systems to monitor process executions. Due to its too fine-grained nature and variations, the direct use of data from IoT devices for monitoring is problematic, requiring an event abstraction step to lift the data to the business process level. This work investigates the application of Temporal Case-Based Reasoning (TCBR) as a novel experience-based approach to detect process activity executions in IoT data. The proposed TCBR approach uses activity signatures-representations of process and IoT data for an activity prototypeas a case base to classify unknown IoT time series data from a smart factory. A data flow architecture is presented that supports analysts in selecting a suitable activity prototype and evaluating its quality for activity detection. The results enable both, the development of high-quality activity detection services and the identification of improvement opportunities in IoT monitoring systems. The approach is evaluated using data produced by a smart factory. The results indicate that the TCBR methods used are very suitable for detecting activities in this IoT use case.Type:conference contributionJournal:Case-Based Reasoning Research and DevelopmentVolume:15662Scopus© Citations 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Role-based Modeling of Business Processes with RBPMN(German Informatics Society, 2025-02); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Activity and Sequence Detection Evaluation Metrics: A Comprehensive Tool for Event Log ComparisonNowadays, event logs are not only created by traditional information systems, but also new data sources such as the IoT are considered to derive and construct event logs. This makes it necessary to evaluate the quality of these detected event logs and their underlying detection methods by comparison with given ground truth logs. We present AquDeM, enabling the comparison of XES-based event logs to evaluate activity and sequence detection methods. AquDeM features 1) a Python library that allows for programmatic comparison of event logs featuring a comprehensive set of metrics, and 2) a web app for visual event log comparison.Type:conference contributionJournal:BPM 2024 Best Dissertation Award, Doctoral Consortium, and Demonstration & Resources Forum - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Object-centric Approach to Handling Concurrency in IoT-aware Processes(2023-09); Manfred ReichertThe 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 contributionJournal:21st International Conference on Business Process Management (BPM) WorkshopsScopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Event-Centric Metamodel for IoT-Driven Process Monitoring and Conformance Checking(2023-09); ; 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.Type:conference contributionJournal:21st International Conference on Business Process Management (BPM) WorkshopsScopus© Citations 7