- A. Appice et al. Deliverable – Process Mining Versione 1.0, 2019, http://www.di.uniba.it/~appice/software/KometaKDDE/Deliverable%20kdde%201.0.pdf
- A. Appice et al. Deliverable – Process Mining Versione 2.0, 2019 http://www.di.uniba.it/~appice/software/KometaKDDE/Deliverable%20kdde%202.0.pdf
- A. Appice et al. Risultati della Valutazione di Tecniche di Process Mining in Kometa, http://www.di.uniba.it/~appice/software/KometaKDDE/DeliverableKddeRisultati.pdf v. 1.0
- A. Appice, N. Di Mauro, D. Malerba., Leveraging Shallow Machine Learning to Predict Business Process Behavior, IEEE World Congress on Services 2019, IEEE, July 2019, https://ieeexplore.ieee.org/abstract/document/8814040,
- P. Ardimento, N. Boffoli, C. Mele, A text-based regression approach to predict bug-fix time. Book chapter in Complex Pattern Mining: New Challenges, Methods and Applications Book in Studies in Computational Intelligence, 2020, https://www.springer.com/gp/book/9783030366162
- S. Ferilli & S. Angelastro, Efficient Declarative-based Process Mining using an Enhanced Framework. Book chapter in Complex Pattern Mining: New Challenges, Methods and Applications, New Challenges, Methods and Applications Book in Studies in Computational Intelligence, 2020 https://www.springer.com/gp/book/9783030366162
- V. Pasquadibisceglie, A. Appice, G. Castellano, D. Malerba, Using Convolutional Neural Networks for Predictive Process Analytics, ICPM 2019, IEEE, 2019, pp 129-136, https://ieeexplore.ieee.org/document/8786066
- V. Pasquadibisceglie, A. Appice, G. Castellano, D. Malerba, Predictive Process Mining Meets Computer Vision. BPM (Forum) 2020: 176-192, https://link.springer.com/chapter/10.1007/978-3-030-58638-6_11
- A. Appice, P. Ardimento, D. Malerba, G. Modugno, D. Marra, M. Mottola, Training in a Virtual Learning Environment: A Process Mining Approach. EAIS 2020: 1-8 IEEE, https://ieeexplore.ieee.org/document/9122760
- V. Pasquadibisceglie, A. Appice, G. Castellano, D. Malerba and G. Modugno, ORANGE: Outcome-Oriented Predictive Process Monitoring Based on Image Encoding and CNNs, in IEEE Access, vol. 8, pp. 184073-184086, 2020, doi: 10.1109/ACCESS.2020.3029323
- V. Pasquadibisceglie, A. Appice, G. Castellano, D. Malerba, A multi-view deep learning approach for predictive business process monitoring, IEEE TRANSACTIONS ON SERVICES COMPUTING, IEEE, 2021 https://ieeexplore.ieee.org/document/9325056
- N. Di Mauro, A. Appice, T.m.A. Basile T.M.A. Activity Prediction of Business Process Instances with Inception CNN Models. In: AI*IA 2019 – Advances in Artificial Intelligence. AI*IA 2019. LNCS 11946. Springer, Cham, https://doi.org/10.1007/978-3-030-35166-3_25
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