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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
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(s)
Range
HSG + other universities + partners
Range (De)
HSG + andere Unis + Partner
Division(s)
Eprints ID
248292
Reference Number
208497
10 results
Now showing
1 - 10 of 10
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PublicationProAmbitIon: Online Process Conformance Checking with Ambiguities Driven by the Internet of Things(CEUR-WS.org, 2023-06)
;Mauricio Jacobo González González ;Enrique Garcia-Ceja ;Luis Armando Rodríguez Flores ;Luciano García-Bañuelos ;Jaime Font ;Lorena Arcega ;José-Fabián Reyes-RománGiovanni GiachettiThe 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 contributionJournal: CAiSE 2023Volume: Vol-3413 -
PublicationFrom Internet of Things Data to Business Processes: Challenges and a Framework( 2024-05)
;Jürgen Mangler ;Benzin, Janik-Vasily ;Grüger, Joscha ;Kirikkayis, Yusuf ;Gallik, Florian ;Malburg, Lukas ;Ehrendorfer, Matthias ;Bertrand, Yannis ;Rinderle-Ma, Stefanie ;Bergmann, Ralph ;Asensio, SerralReichert, ManfredThe 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 -
PublicationActivity 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
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PublicationSustainability in and through IoT-enhanced Business Processes(ceur-ws.org, 2024-09)
;Albert, Manoli ;Antoni Mestre Gascon ;Torres, VictoriaValderas, PedroIn 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 paperJournal: BPM 2024 Best Dissertation Award, Doctoral Consortium, and Demonstration & Resources Forum -
PublicationData-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)
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PublicationAn 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) Workshops
Scopus© Citations 2 -
PublicationA Characterisation of Ambiguity in BPM( 2023)
;Hugo A. Lopez ;Andrea Burattin ;Luciano Garcia BanuelosBusiness 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 -
PublicationExploring the Cognitive Effects of Ambiguity in Process Models( 2024)
;Clemens Schreiber ;Hugo-andrés LópezAmbiguity 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 -
PublicationAn Interactive Method for Detection of Process Activity Executions from IoT Data( 2023-02)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 articleJournal: Future InternetVolume: 15Issue: 2
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PublicationType: conference paperVolume: 2028