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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References
Berrocal, J., et al.: Early evaluation of mobile applications’ resource consumption and operating costs. IEEE Access 8, 146648–146665 (2020). https://doi.org/10.1109/ACCESS.2020.3015082
Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008). https://doi.org/10.1038/nature06958
Görg, S., Bergmann, R.: Social workflows - vision and potential study. Inf. Syst. 50, 1–19 (2015). https://doi.org/10.1016/j.is.2014.12.007
Jablonski, S., Röglinger, M., Schönig, S., Wyrtki, K.M.: Multi-perspective clustering of process execution traces. EMISAJ Int. J. Concept. Model. 14(2), 1–22 (2019). https://doi.org/10.18417/emisa.14.2
Laso, S., Linaje, M., Garcia-Alonso, J., Murillo, J.M., Berrocal, J.: Artifact abstract: deployment of apis on android mobile devices and microcontrollers. In: 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1–2 (2020). https://doi.org/10.1109/PerCom45495.2020.9127353
Poggi, N., Muthusamy, V., Carrera, D., Khalaf, R.: Business process mining from e-commerce web logs. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 65–80. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40176-3_7
Rojo, J., Flores-Martin, D., Garcia-Alonso, J., Murillo, J.M., Berrocal, J.: Automating the interactions among iot devices using neural networks. In: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 1–6 (2020). https://doi.org/10.1109/PerComWorkshops48775.2020.9156111
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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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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