| Business process variability modeling: A survey ML Rosa, WMPVD Aalst, M Dumas, FP Milani ACM Computing Surveys (CSUR) 50 (1), 1-45, 2017 | 310 | 2017 |
| Predictive process monitoring methods: Which one suits me best? C Di Francescomarino, C Ghidini, FM Maggi, F Milani International conference on business process management, 462-479, 2018 | 260 | 2018 |
| Adaptations of data mining methodologies: a systematic literature review V Plotnikova, M Dumas, F Milani PeerJ Computer Science 6 (e267), 2020 | 172 | 2020 |
| Blockchain Oracles: A Framework for Blockchain-Based Applications K Mammadzada, M Iqbal, F Milani, L García-Bañuelos, R Matulevičius. International Conference on Business Process Management, 2020 | 100 | 2020 |
| Blockchain and business process improvement F Milani, L García-Bañuelos, M Dumas BPTrends newsletter (October 2016), 2016 | 88 | 2016 |
| Prescriptive process monitoring: Quo vadis? K Kubrak, F Milani, A Nolte, M Dumas PeerJ Computer Science 8, e1097, 2022 | 85 | 2022 |
| Digital Business Analysis F Milani | 63 | 2019 |
| Modelling families of business process variants: a decomposition driven method F Milani, M Dumas, N Ahmed, R Matulevičius Information Systems 56, 55-72, 2016 | 63 | 2016 |
| Applying the CRISP-DM data mining process in the financial services industry: Elicitation of adaptation requirements FM Plotnikova, Veronika, Marlon Dumas Data & Knowledge Engineering, 2022 | 62 | 2022 |
| Process mining: a guide for practitioners F Milani, K Lashkevich, FM Maggi, C Di Francescomarino International conference on research challenges in information science, 265-282, 2022 | 39 | 2022 |
| Modeling Blockchain-based Business Processes: A Comparative Analysis of BPMN vs CMMN F Milani, L Garcia-Banuelos, S Filipova, M Markovska Business Process Management Journal, 2021 | 33 | 2021 |
| A visual approach to support process analysts in working with process improvement opportunities K Kubrak, F Milani, A Nolte Business Process Management Journal 29 (8), 101-132, 2023 | 32 | 2023 |
| Agile software process improvement by learning from financial and fintech companies: LHV bank case study E Kilu, F Milani, E Scott, D Pfahl International Conference on Software Quality, 57-69, 2018 | 30 | 2018 |
| Enhancing agile software development in the banking sector—A comprehensive case study at LHV E Scott, F Milani, E Kilu, D Pfahl Journal of Software: Evolution and Process, 2021 | 29 | 2021 |
| Criteria and heuristics for business process model decomposition: review and comparative evaluation F Milani, M Dumas, R Matulevičius, N Ahmed, S Kasela Business & Information Systems Engineering 58 (1), 7-17, 2016 | 26 | 2016 |
| Designing a data mining process for the financial services domain V Plotnikova, M Dumas, A Nolte, F Milani Journal of Business Analytics 6 (2), 140-166, 2023 | 23 | 2023 |
| Process mining for process improvement-an evaluation of analysis practices K Kubrak, F Milani, A Nolte International Conference on Research Challenges in Information Science, 214-230, 2022 | 23 | 2022 |
| Decomposition driven consolidation of process models F Milani, M Dumas, R Matulevičius International Conference on Advanced Information Systems Engineering, 193-207, 2013 | 23 | 2013 |
| Why am I waiting? Data-driven analysis of waiting times in business processes K Lashkevich, F Milani, D Chapela-Campa, I Suvorau, M Dumas International Conference on Advanced Information Systems Engineering, 174-190, 2023 | 22 | 2023 |
| Adapting the CRISP-DM Data Mining Process: A Case Study in the Financial Services Domain. V Plotnikova, M Dumas, F Milani Research Challenges in Information Science, 55-72, 2021 | 20 | 2021 |
Marlon DumasProfessor at University of Tartu | Chief Product Officer at ApromoreVerified email at ut.ee
