2023-04-132023-04-13https://www.alexandria.unisg.ch/handle/20.500.14171/57584The 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.Process MiningConformance CheckingExplainabilityInternet of ThingsAmbiguous Process Modelsfundamental research project