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A Joint Analysis of Trajectory Mining and Process Mining for Smartphone User Behaviour

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023)

Abstract

Business Process Management (BPM) is an important area of research. It encompasses the management of both production processes, which involve the creation of products and services, and processes that handle data and information. In today’s digital landscape, the business opportunities associated with information management have gained significant prominence due to the widespread use of smartphones, social networks and similar digital tools in everyday life.

Process mining techniques play a crucial role in supporting various stages of process management, enabling the identification of processes and their specifications. This paper explores the application of process mining techniques to smartphone usage data, complemented by trajectory mining techniques. The aim is to investigate whether location-based information derived from smartphones can contribute to process management or, conversely, whether process management can support the use of location-based data.

The results of this research can provide valuable insights for organisations looking to harness the power of digital tools and data-driven approaches to optimise their processes and improve overall performance.

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Notes

  1. 1.

    The dataset is available at: https://github.com/contextkit/ContextLabeler-Dataset.

  2. 2.

    https://contextkit.github.io.

  3. 3.

    PowerBI: https://powerbi.microsoft.com/it-it/.

  4. 4.

    https://www.celonis.com/academic-signup.

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Correspondence to Simona Fioretto .

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Fioretto, S., Masciari, E., Napolitano, E.V. (2025). A Joint Analysis of Trajectory Mining and Process Mining for Smartphone User Behaviour. In: Meo, R., Silvestri, F. (eds) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2023. Communications in Computer and Information Science, vol 2135. Springer, Cham. https://doi.org/10.1007/978-3-031-74633-8_43

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  • DOI: https://doi.org/10.1007/978-3-031-74633-8_43

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