Abstract
Lack of information on the infrastructure resources needed to execute business processes may interfere with the execution flow of the BPM lifecycle phases. If an organization recognizes that it does not have the resources needed to execute a process as planned, it might have to redesign the process. This paper presents an approach to recommending the infrastructure resources needed to execute a process. The recommendation relies on the task labels of the process model and comprises two phases: resource type classification and resource recommendation.
The approach contributes to the redesign phase as it provides the process analyst with information on the resources needed to execute the process. It also supports decision-making process before the implementation phase regarding, for example, remodeling, project cancellation, resource procurement etc. The developed approach was validated based on a set of real processes of a public university through a cross-fold validation that reached 83% of accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdulhameed, N., Helal, I., Awad, A., Ezat, E.: A resource recommendation approach based on co-working history. Int. J. Adv. Comput. Sci. Appl. 9(7), 236–245 (2018)
Arias, M., Munoz-Gama, J., Sepúlveda, M., Miranda, J.: Human resource allocation or recommendation based on multi-factor criteria in on-demand and batch scenarios. Eur. J. Ind. Eng. 12(3), 364–404 (2018)
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). https://doi.org/10.1007/978-3-319-42887-1_37
Brander, S., et al.: Refining process models through the analysis of informal work practice. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 116–131. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23059-2_12
Cabanillas, C., García, J.M., Resinas, M., Ruiz, D., Mendling, J., Ruiz-Cortés, A.: Priority-based human resource allocation in business processes. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 374–388. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45005-1_26
Confort, V.T.F.: The BPM Issues in Brazilian Perspective. Master’s thesis, Federal University of the State of Rio de janeiro, Brazil (2016)
Dumas, M., Rosa, M.L., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-662-56509-4
Huang, Z., van der Aalst, W., Lu, X., Duan, H.: Reinforcement learning based resource allocation in business process management. Data Knowl. Eng. 70(1), 127–145 (2011)
Huang, Z., Lu, X., Duan, H.: Mining association rules to support resource allocation in business process management. Exp. Sys. App. 38(8), 9483–9490 (2011)
Jaiswal, R., Lokhande, S.: Analysis of early traffic processing and comparison of machine learning algorithms for real time internet traffic identification using statistical approach. In: Kumar Kundu, M., Mohapatra, D.P., Konar, A., Chakraborty, A. (eds.) Advanced Computing, Networking and Informatics- Volume 2. SIST, vol. 28, pp. 577–587. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07350-7_64
Koschmider, A., Yingbo, L., Schuster, T.: Role assignment in business process models. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 37–49. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_4
Li, H., Chen, Q., Wang, X.: An improved method for semantic similarity calculation based on stop-words. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds.) ICMLC 2014. CCIS, vol. 481, pp. 339–347. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45652-1_34
Liu, T., Cheng, Y., Ni, Z.: Mining event logs to support workflow resource allocation. Knowl. Based Syst. 35, 320–331 (2012)
Liu, Y., Wang, J., Yang, Y., Sun, J.: A semi-automatic approach for workflow staff assignment. Comput. Ind. 59(5), 463–476 (2008)
Mendling, J., Reijers, H.A., van der Aalst, W.M.P.: Seven process modeling guidelines (7PMG). Inf. Softw. Technol. 52(2), 127–136 (2010)
OMG: Business process model and notation (BPMN), version 2.0 (2011)
Ricci, F., Rokach, L., Shapira, B.: Recommender systems: introduction and challenges. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 1–34. Springer, Boston, MA (2015). https://doi.org/10.1007/978-1-4899-7637-6_1
Sindhgatta, R., Ghose, A., Dam, H.K.: Context-aware analysis of past process executions to aid resource allocation decisions. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 575–589. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39696-5_35
Yang, H., Wen, L., Liu, Y., Wang, J.: An approach to recommend resources for business processes. In: Herrero, P., Panetto, H., Meersman, R., Dillon, T. (eds.) OTM 2012. LNCS, vol. 7567, pp. 662–665. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33618-8_88
Zhao, W., Liu, H., Dai, W., Ma, J.: An entropy-based clustering ensemble method to support resource allocation in business process management. Knowl. Inf. Syst. 48(2), 305–330 (2016)
Acknowledgments
Lucinéia Heloisa Thom is a CAPES scholarship holder, Program Professor Visitante no Exterior, grant 88881.172071/2018-01; José Palazzo Moreira de Oliveira receive support from CNPq by grants 301425/2018-3 and 400954/2016-8; Carlos Habekost dos Santos and Larissa Narumi Takeda are scholarship holders from CNPq; Marcelo Fantinato is funded by FAPESP, grant 2017/26491-1; this study was financed in part by the CAPES - Brazil - Finance Code 001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Biazus, M. et al. (2019). Software Resource Recommendation for Process Execution Based on the Organization’s Profile. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11707. Springer, Cham. https://doi.org/10.1007/978-3-030-27618-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-27618-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27617-1
Online ISBN: 978-3-030-27618-8
eBook Packages: Computer ScienceComputer Science (R0)