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A Conceptual Framework for Resource Analysis in Process Mining

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Enterprise Design, Operations, and Computing. EDOC 2024 Workshops (EDOC 2024)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 537))

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Abstract

Resource analysis is an emerging branch in process mining that aims to understand behavioral and structural aspects of resources in business processes. A problem of current resource analysis is its fragmentation. The spectrum of corresponding process mining techniques is diverse but scattered, with contributions often focusing on one or the other specific aspects. An overarching framework that could organize resource analysis, tie it to theoretical foundations, and, in turn, inform the development of new analytical methods is missing. In this work, we address this research problem by conducting a systematic literature review to organize the scattered landscape of the state-of-the-art resource analysis methods in process mining. Our work is guided by the question of what resource-related organizational and behavioral patterns can be analyzed with current methods. We classify the methods according to two aspects: what type of phenomenon was analyzed and what design principles were utilized in the development. Our findings highlight that most resource analysis methods in process mining are data-driven, developed to solve a specific business problem, or loosely based on resource analysis concepts from other disciplines. Some good examples of techniques defined for theoretical questions give directions for future research.

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Notes

  1. 1.

    During the publication phase of this article, Martin and Beerepoot [38] published a similar study on resource analysis. However, we differ in two fundamental ways. First, [38] focus on resource analysis use cases, whereas we investigate behavioral constructs. Second, [38] have adopted a broader understanding of resource analysis, as they also examine adjacent resource-related areas, such as resource assignment and resource-aware process model discovery. Our study is, in contrast, more focused on the analytical aspect. Because of these slight differences in perspective, our findings complement each other.

  2. 2.

    Index terms from Web of science, please see: https://webofscience.help.clarivate.com/en-us/Content/wos-core-collection/wos-full-record.htm (accessed: 2024-09-18).

  3. 3.

    https://support.clarivate.com/ScientificandAcademicResearch/s/article/Web-of-Science-Search-in-All-Databases-refined-by-an-individual-database-may-return-more-results-than-the-same-search-in-that-individual-database?language=en_US (accessed: 2024-09-18).

  4. 4.

    We define a technique according to the classification framework of information systems development methodologies by Iivari et al. [33] as a “well-defined sequence of elementary operations that more or less guarantee the achievement of certain outcomes if executed correctly” [33, p. 186]. In other words, a technique could be a simple function, a metric, an algorithm, or similar. A technique is to be differentiated from higher abstraction levels development methodologies starting with methodologies, continuing with approaches, and after that paradigms on the highest level [33, p. 186]. In the context of resource analysis, examples of techniques are the handover of work metrics by Aalst et al. [3] and the competence measure by Huang et al. [30, pp. 6461-6462]. We use the terms technique and method interchangeably in this article.

  5. 5.

    https://scopus.com (accessed: 2024-09-18).

  6. 6.

    We refer to a concept as a resource-related behavioral pattern that an author aims to measure, directly or indirectly, using some technique. In the literature, other termonologies are often used and sometimes interchangeably, such as notion, construct, or perspective.

  7. 7.

    All concepts starting with the term execution are umbrella terms for multiple metrics with or without specific concept names. An example is Pika et al. [44], who propose multiple execution frequency metrics to measure the concept utilization, such as the indicators “activity completions” and “number of case completions” to count the instance involvement of a resource on an activity and case level respectively [44, p. 1:9].

  8. 8.

    Performance and productivity are often used interchangeably in the literature. Yet, we aimed to separate these concepts.

  9. 9.

    Delegation is often referred to as reassignment (e.g., [3]) or previous owner [10, p. 414] in the literature.

  10. 10.

    Handover is an umbrella term for different handover relation concepts, such as handover of work [3, 21, 51, 54, 57], handover of roles [12], or simply hand-offs [36] or handovers [10]. Some work are less explicit (e.g., Zhao et al. refers to “transfer [of] work-items” [62, p. 309]).

  11. 11.

    Joint work refers to metrics based on “joint activities” or “joint cases” (cf., [3, p. 560]).

  12. 12.

    Entity discovery is an umbrella term. The methods in this category are commonly named after the type of entity they discover.

  13. 13.

    Collaboration includes the concepts cooperation and compatibility, as they are often used interchangeably in the process mining literature (cf., [36, 57]).

  14. 14.

    In a strict sense, the Yerkes-Dodson law is not a theory but an empirical phenomenon in psychology (cf., [22]) based on the original findings by Yerkes and Dodson [60]. Nonetheless, the phenomenon is well-studied and has a long history in the psychological literature, often characterized as a U-curve-shaped model (cf., [22]). Hence, we have treated the law as a theory for our purposes.

  15. 15.

    https://promtools.org (accessed: 2024-09-18).

  16. 16.

    https://processintelligence.solutions (accessed: 2024-09-18).

References

  1. IEEE standard for eXtensible Event Stream (XES) for achieving interoperability in event logs and event streams. IEEE STD 1849-2016, pp. 1–50 (2016). https://doi.org/10.1109/IEEESTD.2016.7740858

  2. van der Aalst, W.M.P.: Process Mining - Data Science in Action, 2 edn. Springer (2016). https://doi.org/10.1007/978-3-662-49851-4

  3. van der Aalst, W.M.P., Reijers, H.A., Song, M.: Discovering social networks from event logs. Comput. Support. Cooperative Work. 14(6), 549–593 (2005). https://doi.org/10.1007/S10606-005-9005-9

    Article  MATH  Google Scholar 

  4. Alvarez, C., et al.: Discovering role interaction models in the emergency room using process mining. J. Biomed. Informatics 78, 60–77 (2018). https://doi.org/10.1016/J.JBI.2017.12.015

    Article  MATH  Google Scholar 

  5. Appice, A.: Towards mining the organizational structure of a dynamic event scenario. J. Intell. Inf. Syst. 50(1), 165–193 (2018). https://doi.org/10.1007/S10844-017-0451-X

    Article  MATH  Google Scholar 

  6. Arias, M., Munoz-Gama, J., Sepúlveda, M., Miranda, J.C.: 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). https://doi.org/10.1504/ejie.2018.092009

    Article  Google Scholar 

  7. Arias, M., Saavedra, R., Marques, M.R., Munoz-Gama, J., Sepúlveda, M.: Human resource allocation in business process management and process mining: a systematic mapping study. Manag. Decis. 56(2), 376–405 (2018). https://doi.org/10.1108/md-05-2017-0476

    Article  Google Scholar 

  8. Barney, J.B.: Firm resources and sustained competitive advantage. Advances in Strategic Management, vol. 17, pp. 203–227. Emerald (MCB UP) (2000). https://doi.org/10.1016/s0742-3322(00)17018-4

  9. Baumgrass, A., Strembeck, M.: Bridging the gap between role mining and role engineering via migration guides. Inf. Secur. Tech. Rep. 17(4), 148–172 (2013). https://doi.org/10.1016/j.istr.2013.03.003

    Article  Google Scholar 

  10. Bidar, R., ter Hofstede, A., Sindhgatta, R., Ouyang, C.: Preference-based resource and task allocation in business process automation. In: Panetto, H., Debruyne, C., Hepp, M., Lewis, D., Ardagna, C.A., Meersman, R. (eds.) OTM 2019. LNCS, vol. 11877, pp. 404–421. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33246-4_26

    Chapter  Google Scholar 

  11. Bostrom, R.P., Heinen, J.S.: MIS problems and failures: a socio-technical perspective. Part I: the causes. MIS Q. 1(3), 17–32 (1977). https://doi.org/10.2307/248710

    Article  MATH  Google Scholar 

  12. Burattin, A., Sperduti, A., Veluscek, M.: Business models enhancement through discovery of roles. In: IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013, Singapore, 16–19 April 2013, pp. 103–110. IEEE (2013). https://doi.org/10.1109/CIDM.2013.6597224

  13. Cabanillas, C.: Process- and resource-aware information systems. In: Matthes, F., Mendling, J., Rinderle-Ma, S. (eds.) 20th IEEE International Enterprise Distributed Object Computing Conference, EDOC 2016, Vienna, Austria, 5–9 September 2016, pp. 1–10. IEEE Computer Society (2016). https://doi.org/10.1109/EDOC.2016.7579383

  14. Cabanillas, C., Knuplesch, D., Resinas, M., Reichert, M., Mendling, J., Ruiz-Cortés, A.: RALph: a graphical notation for resource assignments in business processes. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 53–68. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19069-3_4

    Chapter  Google Scholar 

  15. Cabanillas, C., Resinas, M., del-Río-Ortega, A., Ruiz-Cortés, A.: Specification and automated design-time analysis of the business process human resource perspective. Inf. Syst. 52, 55–82 (2015). https://doi.org/10.1016/J.IS.2015.03.002

  16. Carrera-Rivera, A., Ochoa, W., Larrinaga, F., Lasa, G.: How-to conduct a systematic literature review: a quick guide for computer science research. MethodsX 9, 101895 (2022). https://doi.org/10.1016/j.mex.2022.101895

    Article  Google Scholar 

  17. Conca, T., et al.: Multidisciplinary collaboration in the treatment of patients with type 2 diabetes in primary care: analysis using process mining. J. Med. Internet Res. 20(4), e127 (2018). https://doi.org/10.2196/jmir.8884

    Article  MATH  Google Scholar 

  18. Delcoucq, L., Dupiereux-Fettweis, T., Lecron, F., Fortemps, P.: Resource and activity clustering based on a hierarchical cell formation algorithm. Appl. Intell. 53(1), 532–541 (2023). https://doi.org/10.1007/S10489-022-03457-9

    Article  Google Scholar 

  19. Delcoucq, L., Lecron, F., Fortemps, P., van der Aalst, W.M.P.: Resource-centric process mining: clustering using local process models. In: Hung, C., Cerný, T., Shin, D., Bechini, A. (eds.) SAC 2020: The 35th ACM/SIGAPP Symposium on Applied Computing, online event, [Brno, Czech Republic], March 30 - April 3, 2020, pp. 45–52. ACM (2020). https://doi.org/10.1145/3341105.3373864

  20. Deokar, A.V., Tao, J.: OrgMiner: a framework for discovering user-related process intelligence from event logs. Inf. Syst. Front. 23(3), 753–772 (2021). https://doi.org/10.1007/S10796-020-09990-7

    Article  MATH  Google Scholar 

  21. Diamantini, C., Pisacane, O., Potena, D., Storti, E.: Combining an LNS-based approach and organizational mining for the resource replacement problem. Comput. Oper. Res. 161, 106446 (2024). https://doi.org/10.1016/J.COR.2023.106446

    Article  MathSciNet  MATH  Google Scholar 

  22. Diamond, D.M., Campbell, A.M., Park, C.R., Halonen, J., Zoladz, P.R.: The temporal dynamics model of emotional memory processing: a synthesis on the neurobiological basis of stress-induced amnesia, flashbulb and traumatic memories, and the Yerkes-Dodson Law. Neural Plast. 2007(60803), 1–33 (2007). https://doi.org/10.1155/2007/60803

    Article  Google Scholar 

  23. Dumas, M., Rosa, M.L., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management, 2 edn. Springer (2018). https://doi.org/10.1007/978-3-662-56509-4

  24. Estrada-Torres, B., Camargo, M., Dumas, M., García-Bañuelos, L., Mahdy, I., Yerokhin, M.: Discovering business process simulation models in the presence of multitasking and availability constraints. Data Knowl. Eng. 134, 101897 (2021). https://doi.org/10.1016/J.DATAK.2021.101897

    Article  MATH  Google Scholar 

  25. Fernández-Llatas, C., Benedí, J., García-Gómez, J.M., Traver, V.: Process mining for individualized behavior modeling using wireless tracking in nursing homes. Sensors 13(11), 15434–15451 (2013). https://doi.org/10.3390/S131115434

    Article  Google Scholar 

  26. Ferreira, D.R., Alves, C.: Discovering user communities in large event logs. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 123–134. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_11

    Chapter  MATH  Google Scholar 

  27. Gregor, S., Hevner, A.R.: Positioning and presenting design science research for maximum impact. MIS Q. 37(2), 337–355 (2013). https://doi.org/10.25300/MISQ/2013/37.2.01

  28. Grisold, T., Wurm, B., Mendling, J., vom Brocke, J.: Using process mining to support theorizing about change in organizations. In: 53rd Hawaii International Conference on System Sciences, HICSS 2020, Maui, Hawaii, USA, 7–10 January 2020, pp. 1–10 (2020)

    Google Scholar 

  29. Hardy, C., Phillips, N., Lawrence, T.B.: Resources, knowledge and influence: the organizational effects of interorganizational collaboration*. J. Manag. Stud. 40(2), 321–347 (2003). https://doi.org/10.1111/1467-6486.00342

    Article  MATH  Google Scholar 

  30. Huang, Z., Lu, X., Duan, H.: Resource behavior measure and application in business process management. Expert Syst. Appl. 39(7), 6458–6468 (2012). https://doi.org/10.1016/J.ESWA.2011.12.061

    Article  MATH  Google Scholar 

  31. van Hulzen, G.A.W.M., Li, C., Martin, N., van Zelst, S.J., Depaire, B.: Mining context-aware resource profiles in the presence of multitasking. Artif. Intell. Med. 134, 102434 (2022). https://doi.org/10.1016/J.ARTMED.2022.102434

    Article  MATH  Google Scholar 

  32. Ihde, S., Pufahl, L., Völker, M., Goel, A., Weske, M.: A framework for modeling and executing task-specific resource allocations in business processes. Computing 104(11), 2405–2429 (2022). https://doi.org/10.1007/S00607-022-01093-2

    Article  Google Scholar 

  33. Iivari, J., Hirschheim, R., Klein, H.K.: A dynamic framework for classifying information systems development methodologies and approaches. J. Manag. Inf. Syst. 17(3), 179–218 (2000). https://doi.org/10.1080/07421222.2000.11045656

    Article  MATH  Google Scholar 

  34. Jin, T., Wang, J., Wen, L.: Organizational modeling from event logs. In: Grid and Cooperative Computing, Sixth International Conference on Grid and Cooperative Computing, GCC 2007, 16–18 August 2007, Urumchi, Xinjiang, China, Proceedings, pp. 670–675. IEEE Computer Society (2007). https://doi.org/10.1109/GCC.2007.93

  35. Kitchenham, B.: Procedures for performing systematic reviews. Technical Report TR/SE-0401, Keele University, Keele, UK (2004)

    Google Scholar 

  36. 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). https://doi.org/10.1007/978-3-642-40176-3_20

    Chapter  MATH  Google Scholar 

  37. Liu, R., Kumar, A., Lee, J.: Multi-level team assignment in social business processes: an algorithm and simulation study. Inf. Syst. Front. 24, 1949–1969 (2022). https://doi.org/10.1007/S10796-021-10211-Y

    Article  MATH  Google Scholar 

  38. Martin, N., Beerepoot, I.: Unveiling use cases for human resource mining: a framework of past and future research. Bus. Inf. Syst. Eng. (2024). https://doi.org/10.1007/s12599-024-00894-3

    Article  Google Scholar 

  39. Martin, N., Depaire, B., Caris, A., Schepers, D.: Retrieving the resource availability calendars of a process from an event log. Inf. Syst. 88, 101463 (2020). https://doi.org/10.1016/J.IS.2019.101463

    Article  MATH  Google Scholar 

  40. Martin, N., Swennen, M., Depaire, B., Jans, M., Caris, A., Vanhoof, K.: Retrieving batch organisation of work insights from event logs. Decis. Support Syst. 100, 119–128 (2017). https://doi.org/10.1016/J.DSS.2017.02.012

    Article  Google Scholar 

  41. Nakatumba, J., van der Aalst, W.M.P.: Analyzing resource behavior using process mining. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 69–80. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12186-9_8

    Chapter  MATH  Google Scholar 

  42. Ouyang, C., Wynn, M.T., Fidge, C.J., ter Hofstede, A.H.M., Kuhr, J.: Modelling complex resource requirements in business process management systems. In: Australasian Conference on Information Systems, ACIS 2010, Brisbane, Australia, 1–3 December 2010 (2010)

    Google Scholar 

  43. Paré, G., Trudel, M., Jaana, M., Kitsiou, S.: Synthesizing information systems knowledge: a typology of literature reviews. Inf. Manag. 52(2), 183–199 (2015). https://doi.org/10.1016/J.IM.2014.08.008

    Article  MATH  Google Scholar 

  44. Pika, A., Leyer, M., Wynn, M.T., Fidge, C.J., ter Hofstede, A.H.M., van der Aalst, W.M.P.: Mining resource profiles from event logs. ACM Trans. Manag. Inf. Syst. 8(1), 1:1–1:30 (2017). https://doi.org/10.1145/3041218

  45. Pika, A., Ouyang, C., ter Hofstede, A.H.M.: Configurable batch-processing discovery from event logs. ACM Trans. Manag. Inf. Syst. 13(3), 28:1–28:25 (2022). https://doi.org/10.1145/3490394

  46. Pufahl, L., Ihde, S., Stiehle, F., Weske, M., Weber, I.: Automatic resource allocation in business processes: a systematic literature survey. CoRR abs/2107.07264 (2021). https://doi.org/10.48550/arXiv.2107.07264

  47. Russell, N., van der Aalst, W.M.P., ter 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). https://doi.org/10.1007/11431855_16

    Chapter  MATH  Google Scholar 

  48. Schönig, S., Cabanillas, C., Ciccio, C.D., Jablonski, S., Mendling, J.: Mining team compositions for collaborative work in business processes. Softw. Syst. Model. 17(2), 675–693 (2018). https://doi.org/10.1007/S10270-016-0567-4

    Article  Google Scholar 

  49. Schönig, S., Cabanillas, C., Jablonski, S., Mendling, J.: A framework for efficiently mining the organisational perspective of business processes. Decis. Support Syst. 89, 87–97 (2016). https://doi.org/10.1016/J.DSS.2016.06.012

    Article  Google Scholar 

  50. Scott, J.: What is social network analysis? Bloomsbury Acad. (2012). https://doi.org/10.5040/9781849668187

    Article  MATH  Google Scholar 

  51. Song, M., van der Aalst, W.M.P.: Towards comprehensive support for organizational mining. Decis. Support Syst. 46(1), 300–317 (2008). https://doi.org/10.1016/J.DSS.2008.07.002

    Article  MATH  Google Scholar 

  52. Strembeck, M., Mendling, J.: Modeling process-related RBAC models with extended UML activity models. Inf. Softw. Technol. 53(5), 456–483 (2011). https://doi.org/10.1016/J.INFSOF.2010.11.015

    Article  Google Scholar 

  53. Suriadi, S., Wynn, M.T., Xu, J., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Discovering work prioritisation patterns from event logs. Decis. Support Syst. 100, 77–92 (2017). https://doi.org/10.1016/J.DSS.2017.02.002

    Article  Google Scholar 

  54. Tan, W., Zhao, L., Xu, L., Huang, L., Xie, N.: Method towards discovering potential opportunity information during cross-organisational business processes using role identification analysis within complex social network. Enterp. Inf. Syst. 14(4), 436–462 (2020). https://doi.org/10.1080/17517575.2018.1562106

    Article  Google Scholar 

  55. Utama, N.I., Sutrisnowati, R.A., Kamal, I.M., Bae, H., Park, Y.J.: Mining shift work operation from event logs. Appl. Sci. 10(20), 7202 (2020). https://doi.org/10.3390/app10207202

    Article  Google Scholar 

  56. Yang, J., Ouyang, C., van der Aalst, W.M.P., ter Hofstede, A.H.M., Yu, Y.: OrdinoR: a framework for discovering, evaluating, and analyzing organizational models using event logs. Decis. Support Syst. 158, 113771 (2022). https://doi.org/10.1016/J.DSS.2022.113771

    Article  MATH  Google Scholar 

  57. Yang, J., Ouyang, C., ter Hofstede, A.H.M., van der Aalst, W.M.P., Leyer, M.: Seeing the forest for the trees: group-oriented workforce analytics. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds.) BPM 2021. LNCS, vol. 12875, pp. 345–362. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85469-0_22

    Chapter  MATH  Google Scholar 

  58. Ye, J., Li, Z., Yi, K., Al-Ahmari, A.: Mining resource community and resource role network from event logs. IEEE Access 6, 77685–77694 (2018). https://doi.org/10.1109/ACCESS.2018.2883774

    Article  Google Scholar 

  59. Yeon, M., Lee, Y., Pham, D., Kim, K.P.: Experimental verification on human-centric network-based resource allocation approaches for process-aware information systems. IEEE Access 10, 23342–23354 (2022). https://doi.org/10.1109/ACCESS.2022.3152778

    Article  MATH  Google Scholar 

  60. Yerkes, R.M., Dodson, J.D.: The relation of strength of stimulus to rapidity of habit-formation. J. Comp. Neurol. Psychol. 18(5), 459–482 (1908). https://doi.org/10.1002/cne.920180503

    Article  MATH  Google Scholar 

  61. Yingbo, L., Li, Z., Jianmin, W.: Mining workflow event log to facilitate parallel work item sharing among human resources. Int. J. Comput. Integr. Manuf. 24(9), 864–877 (2011). https://doi.org/10.1080/0951192X.2011.579168

    Article  Google Scholar 

  62. 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). https://doi.org/10.1007/S10115-015-0879-7

    Article  MATH  Google Scholar 

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Acknowledgements

The research of the authors was supported by the Einstein Foundation Berlin under grant EPP-2019-524, by the German Federal Ministry of Education and Research under grant 16DII133, and by Deutsche Forschungsgemeinschaft under grant 496119880.

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Authors and Affiliations

  1. Humboldt-Universität zu Berlin, Berlin, Germany

    Christoffer Rubensson & Jan Mendling

  2. Technische Universität München, Heilbronn, Germany

    Luise Pufahl

  3. Wirtschaftsuniversität Wien, Vienna, Austria

    Jan Mendling

  4. Weizenbaum Institute, Berlin, Germany

    Christoffer Rubensson, Luise Pufahl & Jan Mendling

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  1. University of Duisburg-Essen, Essen, Germany

    Monika Kaczmarek-Heß

  2. Niederrhein University of Applied Sciences, Mönchengladbach, Germany

    Kristina Rosenthal

  3. Czech Technical University in Prague, Prague, Czech Republic

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  4. Universidade de Lisboa, Lisbon, Portugal

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Rubensson, C., Pufahl, L., Mendling, J. (2025). A Conceptual Framework for Resource Analysis in Process Mining. In: Kaczmarek-Heß, M., Rosenthal, K., Suchánek, M., Da Silva, M.M., Proper, H.A., Schnellmann, M. (eds) Enterprise Design, Operations, and Computing. EDOC 2024 Workshops . EDOC 2024. Lecture Notes in Business Information Processing, vol 537. Springer, Cham. https://doi.org/10.1007/978-3-031-79059-1_12

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