Skip to main content
SpringerLink
Log in

A Multi-criteria Approach for Team Recommendation

  • Conference paper
  • First Online:
Business Process Management Workshops (BPM 2016)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 281))

Included in the following conference series:

Abstract

Team recommendation is a key and little-explored aspect within the area of business process management. The efficiency with which the team is conformed may influence the success of the process execution. The formation of work teams is often done manually, without a comparative analysis based on multiple criteria between the individual performance of the resources and their collective performance in different teams. In this article, we present a multi-criteria framework to allocate work teams dynamically. The framework considers four elements: (i) a resource request characterization, (ii) historical information on the process execution and expertise information, (iii) different metrics which calculate the suitability of the work teams taking into account both individual performance as well as collective performance of the resources, and (iv) a recommender system based on the Best Position Algorithm (BPA2) to obtain a ranking for the recommended work teams. A software development process was used to test the usefulness of our approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://svn.win.tue.nl/trac/prom/browser/Packages/ResourceRecommendation/ Trunk/src/org/processmining/resourcerecommendation/utils/resrecxes.

References

  1. van der Aalst, W.M.P., Verbeek, H.M.W.: Process discovery and conformance checking using passages. Fundam. Inform. 131(1), 103–138 (2014)

    MathSciNet  MATH  Google Scholar 

  2. Akbarinia, R., Pacitti, E., Valduriez, P.: Best position algorithms for efficient top-k query processing. Inf. Syst. 36(6), 973–989 (2011)

    Article  Google Scholar 

  3. Arias, M., Rojas, E., Munoz-Gama, J., Sepúlveda, M.: A framework for recommending resource allocation based on process mining. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 458–470. Springer, Cham (2016). doi:10.1007/978-3-319-42887-1_37

    Chapter  Google Scholar 

  4. Ballesteros-Pérez, P., González-Cruz, M.C., Fernández-Diego, M.: Human resource allocation management in multiple projects using sociometric techniques. Intl. J. Project Manage. 30(8), 901–913 (2012)

    Article  Google Scholar 

  5. Barreto, A., de Oliveira Barros, M., Werner, C.M.L.: Staffing a software project a constraint satisfaction and optimization-based approach. Comput. OR 35(10), 3073–3089 (2008)

    Article  MATH  Google Scholar 

  6. Britto, R., de Alcântara dos Santos Neto, P., Rabelo, R.A.L., Ayala, W., Soares, T.: A hybrid approach to solve the agile team allocation problem. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC, pp. 1–8 (2012)

    Google Scholar 

  7. Cabanillas, C., Resinas, M., Ruiz-Cortés, A.: Automated resource assignment in BPMN models using RACI matrices. In: Meersman, R., et al. (eds.) OTM 2012. LNCS, vol. 7565, pp. 56–73. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33606-5_5

    Chapter  Google Scholar 

  8. Cabanillas, C., Resinas, M., Mendling, J., Cortés, A.R.: Automated team selection and compliance checking in business processes. In: Proceedings of the 2015 International Conference on Software and System Process, ICSSP, pp. 42–51 (2015)

    Google Scholar 

  9. Chaudhuri, S., Dayal, U.: An overview of data warehousing and olap technology. ACM Sigmod Rec. 26(1), 65–74 (1997)

    Article  Google Scholar 

  10. Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Heidelberg (2013)

    Book  Google Scholar 

  11. Gerogiannis, V.C., Rapti, E., Karageorgos, A., Fitsilis, P.: Human resource assessment in software development projects using fuzzy linguistic 2-tuples. In: Artificial Intelligence, Modelling and Simulation (AIMS), pp. 217–222. IEEE (2014)

    Google Scholar 

  12. Huang, Z., van der Aalst, W.M.P., Lu, X., Duan, H.: Reinforcement learning based resource allocation in business process management. DKE 70(1), 127–145 (2011)

    Article  Google Scholar 

  13. Huang, Z., Lu, X., Duan, H.: Mining association rules to support resource allocation in business process management. Expert Syst. Appl. 38(8), 9483–9490 (2011)

    Article  Google Scholar 

  14. Kim, A., Obregon, J., Jung, J.-Y.: Constructing decision trees from process logs for performer recommendation. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 224–236. Springer, Cham (2014). doi:10.1007/978-3-319-06257-0_18

    Chapter  Google Scholar 

  15. Kumar, A., Dijkman, R., Song, M.: Optimal resource assignment in workflows for maximizing cooperation. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 235–250. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40176-3_20

    Chapter  Google Scholar 

  16. Li, C., Akker, J.M., Brinkkemper, S., Diepen, G.: Integrated requirement selection and scheduling for the release planning of a software product. In: Sawyer, P., Paech, B., Heymans, P. (eds.) REFSQ 2007. LNCS, vol. 4542, pp. 93–108. Springer, Heidelberg (2007). doi:10.1007/978-3-540-73031-6_7

    Chapter  Google Scholar 

  17. Liu, X., Chen, J., Ji, Y., Yu, Y.: Q-learning algorithm for task allocation based on social relation. In: Cao, J., Wen, L., Liu, X. (eds.) PAS 2014. CCIS, vol. 495, pp. 49–58. Springer, Heidelberg (2015). doi:10.1007/978-3-662-46170-9_5

    Google Scholar 

  18. Munoz-Gama, J., Carmona, J., van der Aalst, W.M.P.: Single-entry single-exit decomposed conformance checking. Inf. Syst. 46, 102–122 (2014)

    Article  Google Scholar 

  19. Narendra, N.C., Ponnalagu, K., Zhou, N., Gifford, W.M.: Towards a formal model for optimal task-site allocation and effort estimation in global software development. In: 2012 Annual SRII Global Conference, pp. 470–477 (2012)

    Google Scholar 

  20. Oberweis, A., Schuster, T.: A meta-model based approach to the description of resources and skills. In: AMCIS, p. 383 (2010)

    Google Scholar 

  21. Royce, W.W.: Managing the development of large software systems. In: proceedings of IEEE WESCON, vol. 26, pp. 1–9 (1970)

    Google Scholar 

  22. Russell, N., Aalst, W.M.P., Hofstede, A.H.M., Edmond, D.: Workflow resource patterns: identification, representation and tool support. In: Pastor, O., Falcão e Cunha, J. (eds.) CAiSE 2005. LNCS, vol. 3520, pp. 216–232. Springer, Heidelberg (2005). doi:10.1007/11431855_16

    Chapter  Google Scholar 

  23. Schönig, S., Cabanillas, C., Jablonski, S., Mendling, J.: A framework for efficiently mining the organisational perspective of business processes. DSSs 89, 87–97 (2016)

    Google Scholar 

  24. e Silva, L., Costa, A.P.: Decision model for allocating human resources in information system projects. Intl. J. Proj. Manage. 31(1), 100–108 (2013)

    Article  Google Scholar 

  25. Sommerville, I.: Software Engineering. Pearson, London (2015)

    MATH  Google Scholar 

  26. Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Satzilla: Portfolio-based algorithm selection for SAT. CoRR abs/1111.2249 (2011)

    Google Scholar 

  27. Zhao, W., Zhao, X.: Process mining from the organizational perspective. In: Wen, Z., Li, T. (eds.) Foundations of Intelligent Systems. AISC, vol. 277, pp. 701–708. Springer, Heidelberg (2014). doi:10.1007/978-3-642-54924-3_66

    Google Scholar 

Download references

Acknowledgments

This project was partially funded by the Ph.D. Scholarship Program of CONICYT Chile (Doctorado Nacional/2014-63140181), Universidad de Costa Rica and by Fondecyt (Chile) Project No.1150365.

Author information

Authors and Affiliations

  1. Department of Computer Science, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

    Michael Arias, Jorge Munoz-Gama & Marcos Sepúlveda

Authors
  1. Michael Arias
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jorge Munoz-Gama
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marcos Sepúlveda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Arias .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Marlon Dumas

  2. School of Arts, Sciences and Humanities, University of São Paulo, Sao Paulo, São Paulo, Brazil

    Marcelo Fantinato

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Arias, M., Munoz-Gama, J., Sepúlveda, M. (2017). A Multi-criteria Approach for Team Recommendation. In: Dumas, M., Fantinato, M. (eds) Business Process Management Workshops. BPM 2016. Lecture Notes in Business Information Processing, vol 281. Springer, Cham. https://doi.org/10.1007/978-3-319-58457-7_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-58457-7_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58456-0

  • Online ISBN: 978-3-319-58457-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation