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ORCIDRafael Seidi Oyamada
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2020 – today
- 2024
[c6]Rafael Seidi Oyamada
, Gabriel Marques Tavares, Sylvio Barbon Junior, Paolo Ceravolo:
CoSMo: A Framework to Instantiate Conditioned Process Simulation Models. BPM 2024: 328-344- [c5]Rafael Seidi Oyamada, Gabriel Marques Tavares, Sylvio Barbon Junior, Paolo Ceravolo:
Enhancing Predictive Process Monitoring with Time-Related Feature Engineering. CAiSE 2024: 71-86 - 2023
- [j3]Gabriel M. Tavares, Rafael Seidi Oyamada, Sylvio Barbon, Paolo Ceravolo:
Trace encoding in process mining: A survey and benchmarking. Eng. Appl. Artif. Intell. 126(Part D): 107028 (2023) - [j2]Rafael Seidi Oyamada, Larissa Capobianco Shimomura, Sylvio Barbon Jr., Daniel S. Kaster:
A meta-learning configuration framework for graph-based similarity search indexes. Inf. Syst. 112: 102123 (2023) - [c4]Rafael Seidi Oyamada, Gabriel Marques Tavares, Sylvio Barbon Junior, Paolo Ceravolo:
A Scikit-learn Extension Dedicated to Process Mining Purposes. CoopIS (Demos) 2023: 11-15 - [c3]Andrea Maldonado, Gabriel Marques Tavares, Rafael Seidi Oyamada, Paolo Ceravolo, Thomas Seidl:
FEEED: Feature Extraction from Event Data. ICPM Doctoral Consortium / Demo 2023 - [c2]Matheus A. L. Matiazzo, Vitor de Castro-Silva, Rafael Seidi Oyamada, Daniel S. Kaster:
The Dataset-Similarity-Based Approach to Select Datasets for Evaluation in Similarity Retrieval. SISAP 2023: 125-132 - [i2]Sylvio Barbon Jr., Paolo Ceravolo, Rafael Seidi Oyamada, Gabriel M. Tavares:
Trace Encoding in Process Mining: a survey and benchmarking. CoRR abs/2301.02167 (2023) - [i1]Rafael Seidi Oyamada, Gabriel M. Tavares, Paolo Ceravolo:
CoSMo: a Framework for Implementing Conditioned Process Simulation Models. CoRR abs/2303.17879 (2023) - 2021
- [j1]Larissa Capobianco Shimomura, Rafael Seidi Oyamada, Marcos R. Vieira, Daniel S. Kaster:
A survey on graph-based methods for similarity searches in metric spaces. Inf. Syst. 95: 101507 (2021) - 2020
- [c1]Rafael Seidi Oyamada, Larissa Capobianco Shimomura, Sylvio Barbon Junior, Daniel S. Kaster:
Towards Proximity Graph Auto-configuration: An Approach Based on Meta-learning. ADBIS 2020: 93-107
Coauthor Index
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