| Predictive business process monitoring with LSTM neural networks N Tax, I Verenich, M La Rosa, M Dumas International Conference on Advanced Information Systems Engineering, 477-492, 2017 | 203 | 2017 |
| Complex symbolic sequence clustering and multiple classifiers for predictive process monitoring I Verenich, M Dumas, M La Rosa, FM Maggi, C Di Francescomarino International Conference on Business Process Management, 218-229, 2016 | 40 | 2016 |
| Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring I Verenich, M Dumas, ML Rosa, FM Maggi, I Teinemaa ACM Transactions on Intelligent Systems and Technology (TIST) 10 (4), 1-34, 2019 | 23 | 2019 |
| Minimizing overprocessing waste in business processes via predictive activity ordering I Verenich, M Dumas, M La Rosa, FM Maggi, C Di Francescomarino International Conference on Advanced Information Systems Engineering, 186-202, 2016 | 20 | 2016 |
| White-box prediction of process performance indicators via flow analysis I Verenich, H Nguyen, M La Rosa, M Dumas Proceedings of the 2017 International Conference on Software and System …, 2017 | 15 | 2017 |
| Nirdizati: A web-based tool for predictive process monitoring K Jorbina, A Rozumnyi, I Verenich, C Di Francescomarino, M Dumas, ... | 9 | 2017 |
| A general framework for predictive business process monitoring I Verenich Proceedings of CAiSE 2016 Doctoral Consortium:, 1-9, 2016 | 9 | 2016 |
| Predicting process performance: A white‐box approach based on process models I Verenich, M Dumas, M La Rosa, H Nguyen Journal of Software: Evolution and Process 31 (6), e2170, 2019 | 8 | 2019 |
| Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction F Taymouri, M La Rosa, S Erfani, ZD Bozorgi, I Verenich arXiv preprint arXiv:2003.11268, 2020 | 5 | 2020 |
| Tell me what’s ahead? predicting remaining activity sequences of business process instances I Verenich, M Dumas, M La Rosa, FM Maggi, D Chasovskyi, A Rozumnyi | 5 | 2016 |
| Predictive process monitoring in apromore I Verenich, S Moškovski, S Raboczi, M Dumas, M La Rosa, FM Maggi International Conference on Advanced Information Systems Engineering, 244-253, 2018 | 3 | 2018 |
| Explainable predictive monitoring of temporal measures of business processes I Verenich Queensland University of Technology, 2018 | 3 | 2018 |
| Combining propensity and influence models for product adoption prediction I Verenich, R Kikas, M Dumas, D Melnikov Proceedings of the 2015 IEEE/ACM International Conference on Advances in …, 2015 | 1 | 2015 |
| Планарные и координатные трассировщики на практике ИЮ Веренич, ЮВ Лысенко Вестник Южно-Уральского государственного университета. Серия: Компьютерные …, 2011 | 1 | 2011 |
| Predicting process performance: A white-box approach I Verenich, M Dumas, M La Rosa, H Nguyen, AHM ter Hofstede Predicting Process Performance: A White-Box Approach, 2017 | | 2017 |
| Predictive business process monitoring with LSTMs N Tax, I Verenich, M La Rosa, M Dumas Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on …, 2017 | | 2017 |
| A framework for crowdrating quality evaluation in the statistical machine translation D Chasovskyi, I Verenich | | |
Marcello La RosaSchool of Computing and Information Systems, University of MelbourneVerified email at unimelb.edu.au
