Rafael is a Ph.D. student in Computer Science at the University of Milan, in an industrial program attached to Avio s.p.a. company. He received his BSc and MSc degrees in 2019 and 2021, respectively, both in Computer Science at the Londrina State University. His current research interests lie primarily in process mining and deep learning. In particular, he works with generative models for enhancing business processes and encoding techniques for event data. Alternative interests include databases for similarity-based retrieval, graph algorithms, and meta-learning.
Scikit-learn for Process Data (under dev)
Conditioned Business Process Simulation
Survey and Benchmark on Trace Encoding Methods
Databases based on similarity-graphs
Trace Encoding in Process Mining: a survey and benchmarking. GM Tavares, RS Oyamada, S Barbon Jr., P Ceravolo. Engineering Applications of Artificial Intelligence, 2023.
A meta-learning configuration framework for graph-based similarity search indexes. RS Oyamada, LC Shimomura, S Barbon Jr., DS Kaster. Information Systems, 2023.
A survey based on graph-based methods for similarity searches in metrics spaces. LC Shimomura, RS Oyamada, MR Vieira, DS Kaster. Information Systems, 2021.
CoSMo: a Framework for Implementing Conditioned Process Simulation Models. RS Oyamada, GM Tavares, P Ceravolo. Preprint available on arXiv: abs/2303.17879, 2024 (to appear on BPM’24).
Enhancing Predictive Process Monitoring with Time-related Feature Engineering. RS Oyamada, GM Tavares, S Barbon Jr., P Ceravolo. International Conference on Advanced Information Systems Engineering (CAiSE), 2024.
Usage of Datasets for Evaluation in Similarity Retrieval: Popular Choices and a New Similarity-based Dataset Selection Approach. M Matiazzo, V Castro-Silva, RS Oyamada and DS Kaster. Similarity Search and Applications (SISAP), 2023.
Towards Proximity Graph Auto-Configuration: an Approach Based on Meta-learning. RS Oyamada, LC Shimomura, S Barbon Jr., DS Kaster. European Conference on Advances in Databases and Information Systems (ADBIS), 2020.