ProAmbitIon: Online Process Conformance Checking with Ambiguities Driven by the Internet of Things

Type
fundamental research project
Start Date
November 2022
End Date
October 2025
URI
Acronym
ProAmbitIon
Status
ongoing
Keywords
Process Mining
Conformance Checking
Explainability
Internet of Things
Ambiguous Process Models
Description
The ongoing digitization of processes in all domains of everyday life driven by IT systems shows great potential for process automation, analysis and optimization. However, the digital traces of processes executed in the real world--especially those with human involvement--are often incomplete, too coarse-grained, or captured for individual process steps but not linked to the overall process context. This limits the possibilities for an automated checking of process conformance between the normative process descriptions and their actual execution.In this project--ProAmbitIon--we propose to use the Internet of Things (IoT) as enabler to close the gap between the physical world process execution and its digital representation. New software-controlled sensors and actuators emerging with the IoT promise to enable a fine-grained monitoring of activity executions and the correlation with their underlying processes. The resulting digital representations (traces) of the executed physical world processes lay the foundation for an automated online conformance checking as a process mining activity to detect deviations and non-conformance at runtime. Moreover, when considering human-centered processes, the process knowledge and descriptions are often provided in unstructured informal documents that allow multiple valid (ambiguous) interpretations and executions. Current conformance checking techniques are not capable of handling these ambiguous descriptions in online settings. They rely on a clearly specified formal process model--an assumption that we will relax within ProAmbitIon.We aim at developing new approaches for IoT-driven process conformance checking that are able to cope with ambiguities originating from informal process descriptions and lack of process-related data in detected activities. This includes a user-friendly approach to enrich process descriptions with IoT-related, pattern-based monitoring points to be provided via domain experts. Based on these monitoring points, new mechanisms for abstraction and correlation of IoT data with the execution of processes will be developed. This process execution data has to be be able to represent ambiguities that emerged during event abstraction and correlation. This data serves as input for novel conformance checking algorithms that are able to handle and resolve ambiguities in offline and online analyses. To make the results of conformance checking understandable by end-users and to interactively resolve remaining ambiguities, new concepts for providing interpretable feedback about process conformance will be developed.The project will be conducted following principles of design science developing new artifacts including constructs, frameworks, models, methods and instantiations. Requirements and evaluations will be based on real-world scenarios from healthcare and manufacturing, and will be additionally grounded in literature. The project combines the research competencies and strengths of both principal investigators in Switzerland and Mexico on the topics of process mining, conformance checking, software engineering and IoT, and is supported by renowned researchers and domain experts.With ProAmbitIon we will reduce the gap between processes executed in the physical world and their digital representations. The IoT hereby serves as a new source for process execution data, which will impact process mining in several ways. First, it relaxes the assumption that a central IT system is available to monitor and control the execution. Here the pattern-based monitoring points specified for process elements and mechanisms for event abstraction and correlation will simplify the generation of process event logs and streams based on a new framework for IoT-based event processing. Second, it is no longer necessary to have clearly specified process models for conformance checking as we also allow informal descriptions and the scope of conformance checking will be expanded to online settings where process executions are analyzed at runtime. Our conformance checking approaches will provide user-friendly interactive feedback in case the underlying processes have not been completely followed, resulting in new notions of partial conformance and interpretable feedback. This opens up many new application domains where human involvement and expertise play an important role and cannot be addressed with current process mining techniques.
Leader contributor(s)
Member contributor(s)
Partner(s)
Luciano García-Bañuelos, Department of Computer Science School of Engineering and Sciences Tecnológico de Monterrey
Funder

SNF – Third Party

Range
HSG + other universities + partners
Range (De)
HSG + andere Unis + Partner
Division(s)

ICS - Institute of Co...

SCS - School of Compu...

Eprints ID
248292
Funding code
208497

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    ProAmbitIon: Online Process Conformance Checking with Ambiguities Driven by the Internet of Things
    (CEUR-WS.org, 2023-06)
    Marco Franceschetti  
    ;
    Ronny Seiger  
    ;
    Mauricio Jacobo González González
    ;
    Enrique Garcia-Ceja
    ;
    Luis Armando Rodríguez Flores
    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
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    ProAmbitIon, reloaded: A two-year retrospection
    (ceur-ws.org, 2025-06)
    Marco Franceschetti  
    ;
    Ronny Seiger  
    ;
    García-Bañuelos, Luciano
    ;
    Barbara Weber  
    The 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 contribution
    Journal:Research Projects Exhibition @ CAiSE 2025
    URL:https://ceur-ws.org/Vol-4050/paper03.pdf
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122870
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    Sustainability in and through IoT-enhanced Business Processes
    (ceur-ws.org, 2024-09)
    Albert, Manoli
    ;
    Antoni Mestre Gascon
    ;
    Ronny Seiger  
    ;
    Torres, Victoria
    ;
    Valderas, Pedro
    In today's interconnected world, businesses integrate Internet of Things (IoT) devices to enhance efficiency, gather real-time data, and make informed decisions. These devices autonomously execute tasks and collect data, revolutionizing business processes (IoT-enhanced BPs). They optimize operations, improve productivity, and streamline resource utilization across various industries, such as manufacturing, retail, and logistics. However, businesses must also focus on sustainability beyond environmental concerns, encompassing economic, social, human, and technical aspects. Measuring the sustainability of IoT-enhanced BPs across these dimensions is crucial for long-term viability. While sustainability in business processes has been integrated over the past two decades, existing research has not sufficiently considered the role that IoT devices play in this context. To this end, this work aims to analyze the impact of IoT devices on sustainability issues, emphasizing the need for ongoing research in the BPM field to achieve sustainable IoT-enhanced BPs.
    Type:working paper
    Journal:BPM 2024 Best Dissertation Award, Doctoral Consortium, and Demonstration & Resources Forum
    URL:https://ceur-ws.org/Vol-3758/paper-28.pdf
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/120781
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    From Internet of Things Data to Business Processes: Challenges and a Framework
    (2024-05)
    Jürgen Mangler
    ;
    Ronny Seiger  
    ;
    Benzin, Janik-Vasily
    ;
    Grüger, Joscha
    ;
    Kirikkayis, Yusuf
    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
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    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
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    Online detection of process activity executions from IoT sensors using generated event processing services
    (Elsevier BV, 2025)
    Ronny Seiger  
    ;
    Kurz Aaron Friedrich
    ;
    Marco Franceschetti  
    Data streams from Internet of Things (IoT) devices containing sensors and actuators provide new insights into their interactions, context, and process executions in the physical world. These new data sources may open up novel opportunities to apply Business Process Management (BPM) technologies to analyze process and activity executions using established process mining techniques. However, the rather low abstraction level of data emitted from the IoT devices is often not suitable to directly apply process mining, which requires additional steps of event abstraction. Related approaches train expensive supervised machine learning models on historical sensor data to realize this event abstraction enabling only a post-mortem classification of activity executions. In this work we propose a framework to automatically generate activity detection services from IoT data with minimal human involvement to implement the event abstraction. Along with the framework, we present a software architecture focused on a flexible and extensible complex event processing (CEP) platform that achieves high-performance activity detection from IoT data streams at runtime-enabling online process analytics. Evaluations of our proof-of-concept implementation to monitor processes executed in smart manufacturing and smart healthcare show acceptable results when detecting activities that are affected by no to only small variations in the underlying IoT data. We identify several ways to improve the robustness of the activity detections regarding variations in IoT data as starting points for future work.
    Type:journal article
    Journal:Future Generation Computer Systems
    Volume:174
    URL:https://www.sciencedirect.com/science/article/pii/S0167739X25002821
    DOI:10.1016/j.future.2025.107987
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/123073
    Scopus© Citations 3
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    A Characterisation of Ambiguity in BPM
    (2023)
    Marco Franceschetti  
    ;
    Ronny Seiger  
    ;
    Hugo A. Lopez
    ;
    Andrea Burattin
    ;
    Luciano Garcia Banuelos
    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.
    Type:conference contribution
    DOI:10.1007/978-3-031-47262-6_15
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/118143
    Scopus© Citations 16
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    Exploring the Cognitive Effects of Ambiguity in Process Models
    (2024)
    Marco Franceschetti  
    ;
    Amine Abbad-Andaloussi  
    ;
    Clemens Schreiber
    ;
    Hugo-andrés López
    ;
    Barbara Weber  
    Ambiguity in business process models might lead to multiple alternative process interpretations by the readers. This plurality of interpretations causes undesirable situations such as misunderstandings, unclear responsibilities, and unexpected behaviors. However, to date, little attention has been given to how ambiguity affects the model readers. Here, we report on an eye-tracking study aimed at investigating the impact of different ambiguities (i.e., pragmatic, semantic, syntactic, and lexical) on readers' cognitive load, comprehension, and visual associations when reading process models. The results of this study show that these ambiguities yield a significant impact on cognitive load, comprehension, and visual associations. These results raise further attention toward the negative effects of ambiguity from a cognitive and behavioral perspective, and stimulate the development of novel tools supporting ambiguity detection in process models.
    Type:conference contribution
    DOI:10.1007/978-3-031-70396-6_28
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/120828
    Scopus© Citations 10
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    An Interactive Method for Detection of Process Activity Executions from IoT Data
    (2023-02)
    Seiger, Ronny  
    ;
    Franceschetti, Marco  
    ;
    Weber, Barbara  
    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.
    Type:journal article
    Journal:Future Internet
    Volume:15
    Issue:2
    DOI:10.3390/fi15020077
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/107773
    Scopus© Citations 38