Marco Franceschetti
Thumbnail Image
Last Name
Franceschetti
First name
Marco
Email
marco.franceschetti@unisg.ch
ORCID
Phone
+41 71 224 2806

Filters

7
Reset filters

Settings
Now showing 1 - 7 of 7
No Thumbnail Available
Publication

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.

View
No Thumbnail Available
Publication

A Characterisation of Ambiguity in BPM

2023 , Marco Franceschetti , Ronny Seiger , Hugo A. Lopez , Andrea Burattin , Luciano Garcia Banuelos , Barbara Weber

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.

View
No Thumbnail Available
Publication

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 , Gallik, Florian , Malburg, Lukas , Ehrendorfer, Matthias , Bertrand, Yannis , Marco Franceschetti , Barbara Weber , Rinderle-Ma, Stefanie , Bergmann, Ralph , Asensio, Serral , Reichert, Manfred

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.

View
No Thumbnail Available
Publication

Dynamic Controllability of Parameterized CSTNUs

2023-03 , Marco Franceschetti , Roberto Posenato , Carlo Combi , Johann Eder

A Conditional Simple Temporal Network with Uncertainty (CSTNU) models temporal constraint satisfaction problems in which the environment sets uncontrollable timepoints and conditions. The executor observes and reacts to such uncontrollable assignments as time advances with the CSTNU execution. However, there exist scenarios in which the occurrence of some future timepoints must be fixed as soon as the execution starts. We call these timepoints parameters. For a correct execution, parameters must assume values that guarantee the possibility of satisfying all temporal constraints, whatever the environment decides the execution time for uncontrollable timepoints and the truth value of conditions, i.e., dynamic controllability (DC). Here, we formalize the extension of the CSTNU with parameters. Furthermore, we define a set of rules to check the DC of such extended CSTNU. These rules additionally solve the problem inverse to checking DC: computing restrictions on parameter values that yield DC guarantees. The proposed rules can be composed into a sound and complete procedure.

View
No Thumbnail Available
Publication

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

2023-06 , Marco Franceschetti , Ronny Seiger , Mauricio Jacobo González González , Enrique Garcia-Ceja , Luis Armando Rodríguez Flores , Luciano García-Bañuelos , Barbara Weber , Jaime Font , Lorena Arcega , José-Fabián Reyes-Román , Giovanni Giachetti

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.

View
No Thumbnail Available
Publication

An Event-Centric Metamodel for IoT-Driven Process Monitoring and Conformance Checking

2023-09 , Marco Franceschetti , Ronny Seiger , Barbara Weber

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.

View
No Thumbnail Available
Publication

Data-driven Generation of Services for IoT-based Online Activity Detection

2023-11 , Ronny Seiger , Marco Franceschetti , Barbara Weber

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.

View