Marco Franceschetti
Last Name
Franceschetti
First name
Marco
Email
marco.franceschetti@unisg.ch
ORCID
Phone
+41 71 224 2806
26 results
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Item type:Publication, Online detection of process activity executions from IoT sensors using generated event processing servicesData 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 articleJournal:Future Generation Computer SystemsVolume:174Scopus© Citations 3 - Some of the metrics are blocked by yourconsent settings
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, An 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:2Scopus© Citations 38 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Leveraging Digital Trace Data to Investigate and Support Human-Centered Work ProcessesThe ongoing digitization of processes in all domains of everyday life driven by IT systems shows great potential for process automation, analysis, and optimization. In the last decade process mining has advanced to an important and mature discipline of computer science research and has been widely adopted in industry. More recently,-acknowledging the huge potential of digital trace data to study processes-process science has been introduced as an interdisciplinary field studying how processes unfold over time. This paper discusses the potential that arises when using digital trace data not only in the context of highly automated processes but also to investigate humancentered (work) processes and elaborates on associated challenges. Examples range from the semi-automated storage and production processes in a smart factory to healthcare processes to process analysts performing process mining tasks and software engineers reading software artifacts like source code and process models.Type:conference paperJournal:Communications in Computer and Information ScienceScopus© Citations 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Leveraging Digital Trace Data to Investigate and Support Human-Centered Work Processes(Springer, 2024); ; ; ; The ongoing digitization of processes in all domains of everyday life driven by IT systems shows great potential for process automation, analysis, and optimization. In the last decade process mining has advanced to an important and mature discipline of computer science research and has been widely adopted in industry. More recently,-acknowledging the huge potential of digital trace data to study processes-process science has been introduced as an interdisciplinary field studying how processes unfold over time. This paper discusses the potential that arises when using digital trace data not only in the context of highly automated processes but also to investigate humancentered (work) processes and elaborates on associated challenges. Examples range from the semi-automated storage and production processes in a smart factory to healthcare processes to process analysts performing process mining tasks and software engineers reading software artifacts like source code and process models.Type:conference paperVolume:2028Scopus© Citations 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Framework to Support the Validation of Process Mining Inquiries(2024); ; In exploratory process mining, analysts often start with limited knowledge of the log. As they seek to improve their understanding of the log, they develop expectations about what the results might be. Based on these expectations, they then make inquiries and translate them into queries against the log. However, during the analysis, analysts need to evaluate and compare the results of their queries to be able to validate them against their expectations. In this paper, we propose a framework to support process analysts in validating their query results and to enable them to reflect on their analytical process. The framework helps analysts to record their queries and results and allows them to characterize and compare the results obtained with different queries, thereby facilitating the validation process. We implemented the framework as a Python library that can be easily extended and integrated into existing process mining environments. We also demonstrated the usefulness of the framework through an extensive analysis of a real event log.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Process to Non-invasively Augment Legacy IoT Systems Using Business Processes and MicroservicesNowadays, Internet of Things (IoT) systems can be found everywhere. Thereby, the IoT systems' software greatly varies in terms of openness and extensibility. Especially older legacy IoT systems only provide limited means of interacting and extending their functionality due to proprietary and closed software interfaces. Nevertheless, IoT systems evolve constantly based on new functional requirements and the need for integration with new systems and IoT devices. In this work we investigate the use of Business Process Management (BPM) technologies in combination with Microservices to augment IoT systems in a non-invasive, lightweight manner focusing on integration, data augmentations, and human-in-the-loop. We start by analyzing the interfaces and processes of a smart factory as a representative legacy system to identify missing functionality, which we generalize into functional requirements for IoT systems. We show how these requirements can be addressed using BPM and microservices based on a discussion of suitable BPM concepts and software architecture, which is accompanied by a prototypical implementation. These results inform our proposal and discussion of a generic development process to non-invasively augment processoriented legacy IoT systems using BPM and microservices.Type:conference paperJournal:International Conference on the Internet of ThingsScopus© Citations 6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, On-the-fly Event Disambiguation via Alignments(2026); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multimodal Process Monitoring with On-Demand Disambiguation(2026); ; Torres-huitzil CesarType:conference contribution - 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)
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