Enabling Process Mining in Airbus Manufacturing

Valencia-Parra, Álvaro; Ramos-Gutiérrez, Belén; Varela-Vaca, Ángel Jesús; Gómez-López, María Teresa; Bernal, Antonio García

doi:10.1007/978-3-662-63047-1_10

Cited by 6 publications

(9 citation statements)

References 19 publications

(17 reference statements)

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“…The vast majority of the PM literature involving IoT data has focused on mining high-level events of the process from lowlevel IoT data to create XES event logs. Various frameworks to extract high-level logs from IoT data have been presented, e.g., [29,26,23,27,16,15,14,6,19]. Traditional PM techniques can then be applied to these event logs to, e.g., discover control-flow models of the processes.…”

Section: Process Mining Using Iot Datamentioning

confidence: 99%

NICE: The Native IoT-Centric Event Log Model for Process Mining

Bertrand,

Veneruso,

Leotta

et al. 2024

Lecture Notes in Business Information Processing

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More and more so-called IoT-enhanced business processes (BPs) are supported by IoT devices, which collect large amounts of data about the execution of such processes. While these data have the potential to reveal crucial insights into the execution of the BPs, the absence of a suitable event log format integrating IoT data to process data greatly hampers the realisation of this potential. In this paper, we present the Native Iot-Centric Event (NICE) log, a new event log format designed to incorporate IoT data into a process event log ensuring traceability, flexibility and limiting data loss. The new format was linked to a smart spaces data simulator to generate synthetic logs. We evaluate our format against requirements previously established for an IoTenhanced event log format, showing that it meets all requirements, contrarily to other alternative formats. We then perform an analysis of a synthetic log to show how IoT data can easily be used to explain anomalies in the process.

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Section: Process Mining Using Iot Datamentioning

confidence: 99%

NICE: The Native IoT-Centric Event Log Model for Process Mining

Bertrand,

Veneruso,

Leotta

et al. 2024

Lecture Notes in Business Information Processing

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“…The most prominent existing stream of research relates to activity mining, i.e. approaches to derive higher level process events from low-level IoT data [10,17,21,28,34,36]. Nonetheless, as illustrated by an only evocative set of so far unaddressed research opportunities, denoted in italics in Table 1, the widespread and grounded development of IoT-enhanced process mining techniques, covering all process mining tasks and possible applications, is far from realised yet.…”

Section: Motivationmentioning

confidence: 99%

“…•Control-flow discovery [1,19,37] •Data-aware process discovery [20,23] •Activity mining [10,17,21,28,27,34,36] •Trace clustering [31,6] •Multi-perspective trace clustering [14] •IoT context-aware trace clustering •Multi-perspective process discovery [22] •IoT-enhanced dataaware process discovery [2] Conformance checking…”

Section: Discoverymentioning

confidence: 99%

Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum

Marrella

Matulevičius²,

Gabryelczyk³

et al. 2022

Lecture Notes in Business Information Processing

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Since IoT devices supporting business processes (BPs) in sectors like manufacturing, logistics or healthcare can collect data on the execution of the processes, there is a growing awareness of the opportunity to use IoT data for process mining (PM). However, several challenges need to be addressed to enable IoT-enhanced PM, among which the need for a standard IoT-enhanced event log model. In this paper, we identify four main challenges for the integration of IoT data in event logs, from which 10 requirements for an IoT-enhanced event log model are derived. We then analyse the two main current event log models, the extensible event stream (XES) and the object-centric event log (OCEL), and confront them with the requirements, showing that both present important limitations to storing IoT-enhanced event logs. Based on this analysis, we conclude that a comprehensive data model is required to develop more advanced IoT-enhanced PM techniques.

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“…Applying process mining in an organization requires as input an event log. Usually such logs are extracted from the organization's databases [44,119]. Therefore, access to the database supporting the corresponding information system is necessary to complete the event log extraction process [18,101].…”

Section: Motivationmentioning

confidence: 99%