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Social Events Analyzer (SEA): A Toolkit for Mining Social Workflows by Means of Federated Process Mining

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Web Engineering (ICWE 2022)

Abstract

Users’ smartphones collect information about the different interactions they perform in their daily life, including web interactions. Mining this information to discover user’s processes provides information about them as individuals and as part of a social group. However, analyzing events produced by human behavior, where indeterminism and variability prevail, is a complex task. Techniques such as process mining focus on analyzing customary event logs produced by a system where all the possible interactions are predefined. The analysis become even harder when it involves a group of people whose joint activity is considered part of a Social Workflow. In this demo we present Social Events Analyzer (SEA), a toolkit for easy Social Workflow analysis using a technique called Federated Process Mining. The tool offers models more faithful to the behavior of the users that make up a Social Workflow and opens the door to the use of process mining as a basis for the creation of new automatic procedures adapted to the user behavior.

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Notes

  1. 1.

    https://youtu.be/d2tOYWWhYC0.

  2. 2.

    https://bitbucket.org/spilab/individualpmmodule.

  3. 3.

    https://bitbucket.org/spilab/fpmserver.

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Acknowledgments

This work was supported by the projects 0499_4IE_PLUS_4_E (Interreg V-A España-Portugal 2014–2020), RTI2018-094591-B-I00 (MCIU/AEI/FEDER, UE), and UMA18-FEDERJA-180 (Junta de Andalucía/ATech/FEDER), by the Department of Economy and Infrastructure of the Government of Extremadura (GR18112, IB18030), by the FPU19/03965 grant and by the European Regional Development Fund.

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Correspondence to Javier Rojo .

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Rojo, J., García-Alonso, J., Berrocal, J., Hernández, J., Murillo, J.M., Canal, C. (2022). Social Events Analyzer (SEA): A Toolkit for Mining Social Workflows by Means of Federated Process Mining. In: Di Noia, T., Ko, IY., Schedl, M., Ardito, C. (eds) Web Engineering. ICWE 2022. Lecture Notes in Computer Science, vol 13362. Springer, Cham. https://doi.org/10.1007/978-3-031-09917-5_39

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  • DOI: https://doi.org/10.1007/978-3-031-09917-5_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09916-8

  • Online ISBN: 978-3-031-09917-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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