Ronny Seiger
8 results
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Item type:Publication, Digital Twins of Business Processes: A Research Manifesto(Elsevier, 2024)Scopus© Citations 17 - Some of the metrics are blocked by yourconsent settings
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, 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, 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, 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, ProAmbitIon: Online Process Conformance Checking with Ambiguities Driven by the Internet of Things(CEUR-WS.org, 2023-06); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Data-driven Generation of Services for IoT-based Online Activity Detection(2023-11); ; 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 contributionJournal:21st International Conference on Service-Oriented Computing (ICSOC)Scopus© Citations 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, From Internet of Things Data to Business Processes: Challenges and a Framework(2024-05);