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Mining Valuable Collaborations from Event Data Using the Recency-Frequency-Monetary Principle

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Advanced Information Systems Engineering (CAiSE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13295))

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Abstract

Collaborative work leads to better organizational performance. However, a team leader’s view on collaboration does not always match reality. Due to the increased adoption of (online) collaboration systems in the wake of the COVID pandemic, more digital traces on collaboration are available for a wide variety of use cases. These traces allow for the discovery of accurate and objective insights into a team’s inner workings. Existing social network discovery algorithms however, are often not tailored to discover collaborations. These techniques often have a different view on collaboration by mostly focusing on handover of work, resource profile similarity, or establishing relationships between resources when they work on the same case or activities without any restrictions. Furthermore, only the frequency of appearance of patterns is typically used as a measure of interestingness, which limits the kind of insights one can discover. Therefore we propose an algorithm to discover collaborations from event data using a more realistic approach than basing collaboration on the sequence of resources that carry out activities for the same case. Furthermore, a new research path is explored by adopting the Recency-Frequency-Monetary (RFM) concept, which is used in the marketing research field to assess customer value, in this context to value both the resource and the collaboration on these three dimensions. Our approach and the benefits of adopting RFM to gain insights are empirically demonstrated on a use case of collaboratively developing a curriculum.

Supported by the Special Research Fund (BOF19OWB10) of Hasselt University.

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Notes

  1. 1.

    https://github.com/LeenJooken/RFMCollaborationMiner.

  2. 2.

    https://github.com/microsoft/ML-For-Beginners.

References

  1. van der Aalst, W.: Process mining: Data science in action (2016). ISBN 978-3-662-49851-4

    Google Scholar 

  2. van der Aalst, W.M.P., Reijers, H.A., Song, M.: Discovering social networks from event logs. Comput. Supp. Cooper. Work (CSCW) 14(6), 549–593 (2005)

    Article  Google Scholar 

  3. van der Aalst, W.M.P., Song, M.: Mining social networks: uncovering interaction patterns in business processes. In: Desel, J., Pernici, B., Weske, M. (eds.) BPM 2004. LNCS, vol. 3080, pp. 244–260. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25970-1_16

    Chapter  Google Scholar 

  4. Aljemabi, M.A., Wang, Z.: Empirical study on the similarity and difference between vcs-dsn and bts-dsn. In: Proceedings of the 2017 International Conference on Management Engineering, Software Engineering and Service Sciences. ICMSS 2017, New York, NY, USA, pp. 30–37. Association for Computing Machinery (2017)

    Google Scholar 

  5. Bird, C., Pattison, D., D’Souza, R., Filkov, V., Devanbu, P.: Latent social structure in open source projects. In: Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of Software Engineering. SIGSOFT 2008/FSE-16, pp. 24–35. Association for Computing Machinery (2008)

    Google Scholar 

  6. Bult, J.R., Wansbeek, T.: Optimal selection for direct mail. Mark. Sci. 14(4), 378–394 (1995)

    Article  Google Scholar 

  7. Casciaro, T.: Seeing things clearly: social structure, personality, and accuracy in social network perception. Soc. Networks 20(4), 331–351 (1998)

    Article  Google Scholar 

  8. Chen, Y.L., Kuo, M.H., Wu, S.Y., Tang, K.: Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data. Electron. Commer. Res. Appl. 8(5), 241–251 (2009)

    Article  Google Scholar 

  9. Cross, R., Ehrlich, K., Dawson, R., Helferich, J.: Managing collaboration: improving team effectiveness through a network perspective. Calif. Manage. Rev. 50(4), 74–98 (2008)

    Article  Google Scholar 

  10. Cullen, K.L., Palus, C.J., Appaneal, C.: Developing Network Perspective: Understanding the Basics of Social Networks and their Role in Leadership [White paper]. Technical report, Center for Creative Leadership (2014). https://doi.org/10.35613/ccl.2014.1019

  11. Guzzo, R.A., Dickson, M.W.: Teams in organizations: recent research on performance and effectiveness. Annu. Rev. Psychol. 47(1), 307–338 (1996)

    Article  Google Scholar 

  12. Herbold, S., Amirfallah, A., Trautsch, F., Grabowski, J.: A systematic mapping study of developer social network research. J. Syst. Softw. 171, 110802 (2021)

    Article  Google Scholar 

  13. Jermakovics, A., Sillitti, A., Succi, G.: Mining and visualizing developer networks from version control systems. In: Proceedings of the 4th International Workshop on Cooperative and Human Aspects of Software Engineering. CHASE 2011, New York, NY, USA, pp. 24–31, Association for Computing Machinery (2011)

    Google Scholar 

  14. Lai, C.Y., Li, Y.M., Lin, L.F.: A social referral appraising mechanism for the e-marketplace. Inf. Manage. 54(3), 269–280 (2017)

    Article  Google Scholar 

  15. Li, J., Cao, S.: The study on high-value user identification of localized information platform. J. Phys: Conf. Ser. 1883(1), 012109 (2021)

    Google Scholar 

  16. Ly, L.T., Rinderle, S., Dadam, P., Reichert, M.: Mining staff assignment rules from event-based data. In: Bussler, C.J., Haller, A. (eds.) BPM 2005. LNCS, vol. 3812, pp. 177–190. Springer, Heidelberg (2006). https://doi.org/10.1007/11678564_16

    Chapter  Google Scholar 

  17. Madey, G., Freeh, V., Tynan, R.: The open source software development phenomenon: an analysis based on social network theory. In: AMCIS 2002 Proceedings, pp. 1806–1813. Association for Information Systems (2002)

    Google Scholar 

  18. Mehra, A., Smith, B.R., Dixon, A.L., Robertson, B.: Distributed leadership in teams: The network of leadership perceptions and team performance. Leadersh. Q. 17(3), 232–245 (2006)

    Article  Google Scholar 

  19. Meneely, A., Williams, L.: Socio-technical developer networks: should we trust our measurements? In: Proceedings of the 33rd International Conference on Software Engineering. ICSE 2011, pp. 281–290. Association for Computing Machinery (2011)

    Google Scholar 

  20. Mitrovic, S., Baesens, B., Lemahieu, W., De Weerdt, J.: tcc2vec: RFM-informed representation learning on call graphs for churn prediction. Inf. Sci. 557, 1–16 (2019)

    Google Scholar 

  21. Mitrovic, S., De Weerdt, J.: Dyn2Vec: Exploiting dynamic behaviour using difference networks-based node embeddings for classification. In: Proceedings of the International Conference on Data Science. pp. 194–200. CSREA Press (2019)

    Google Scholar 

  22. Mitrovic, S., Singh, G., Baesens, B., Lemahieu, W., De Weerdt, J.: Scalable RFM-enriched representation learning for churn prediction, vol. 2018-January, pp. 79–88. IEEE (2017)

    Google Scholar 

  23. Pika, A., Leyer, M., Wynn, M.T., Fidge, C.J., Hofstede, A.H.M.T., Aalst, W.M.P.V.D.: Mining resource profiles from event logs. ACM Trans. Manag. Inf. Syst. 8(1), 1:1–1:30

    Google Scholar 

  24. Pika, A., Wynn, M.T., Fidge, C.J., ter Hofstede, A.H.M., Leyer, M., van der Aalst, W.M.P.: An extensible framework for analysing resource behaviour using event logs. In: Jarke, M., et al. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 564–579. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07881-6_38

    Chapter  Google Scholar 

  25. Recardo, R., Wade, D., Mention, C.: Teams: Who Needs Them and Why? Teams: Who Needs Them and Why?, Gulf Publishing Company (1996)

    Google Scholar 

  26. Song, M., van der Aalst, W.M.P.: Towards comprehensive support for organizational mining. Decis. Support Syst. 46(1), 300–317 (2008)

    Article  Google Scholar 

  27. Tymchuk, Y., Mocci, A., Lanza, M.: Collaboration in open-source projects: Myth or reality? In: 11th Working Conference on Mining Software Repositories, MSR 2014 - Proceedings (2014)

    Google Scholar 

  28. Wasserman, S., Faust, K., et al.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994).ISBN 0-521-38707-8

    Book  Google Scholar 

  29. Wolf, T., Schroter, A., Damian, D., Nguyen, T.: Predicting build failures using social network analysis on developer communication. In: 2009 IEEE 31st International Conference on Software Engineering, pp. 1–11 (2009)

    Google Scholar 

  30. Xue, Y., Chen, J., Zhou, Y.: Research on user discovery based on loyalty in SNS. In: Proceedings of the 2017 International Seminar on Social Science and Humanities Research (SSHR 2017), pp. 399–406. Atlantis Press, December 2017

    Google Scholar 

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Authors and Affiliations

  1. Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium

    Leen Jooken, Mieke Jans & Benoît Depaire

Authors
  1. Leen Jooken
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  2. Mieke Jans
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  3. Benoît Depaire
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Correspondence to Leen Jooken .

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Editors and Affiliations

  1. Department of Service and Information System Engineering (ESSI), Universitat Politècnica de Catalunya, Barcelona, Spain

    Xavier Franch

  2. Ghent University, Gent, Belgium

    Geert Poels

  3. Ghent University, Gent, Belgium

    Frederik Gailly

  4. KU Leuven, Leuven, Belgium

    Monique Snoeck

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Jooken, L., Jans, M., Depaire, B. (2022). Mining Valuable Collaborations from Event Data Using the Recency-Frequency-Monetary Principle. In: Franch, X., Poels, G., Gailly, F., Snoeck, M. (eds) Advanced Information Systems Engineering. CAiSE 2022. Lecture Notes in Computer Science, vol 13295. Springer, Cham. https://doi.org/10.1007/978-3-031-07472-1_20

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  • DOI: https://doi.org/10.1007/978-3-031-07472-1_20

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-07472-1

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