| Efficient joinable table discovery in data lakes: A high-dimensional similarity-based approach Y Dong, K Takeoka, C Xiao, M Oyamada 2021 IEEE 37th International Conference on Data Engineering (ICDE), 456-467, 2021 | 45 | 2021 |
| Meimei: An efficient probabilistic approach for semantically annotating tables K Takeoka, M Oyamada, S Nakadai, T Okadome Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) 33 (01 …, 2019 | 39 | 2019 |
| Data stream processing with concurrency control M Oyamada, H Kawashima, H Kitagawa ACM SIGAPP Applied Computing Review 13 (2), 54-65, 2013 | 17* | 2013 |
| Relational Mixture of Experts: Explainable Demographics Prediction with Behavioral Data M Oyamada, S Nakadai IEEE International Conference on Data Mining (ICDM), 357-366, 2017 | 16* | 2017 |
| Low-resource taxonomy enrichment with pretrained language models K Takeoka, K Akimoto, M Oyamada Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021 | 12 | 2021 |
| Extracting feature engineering knowledge from data science notebooks M Oyamada 2019 IEEE International Conference on Big Data (Big Data), 6172-6173, 2019 | 10 | 2019 |
| Continuous top-k spatial–keyword search on dynamic objects Y Dong, C Xiao, H Chen, JX Yu, K Takeoka, M Oyamada, H Kitagawa The VLDB Journal 30 (2), 141-161, 2021 | 9 | 2021 |
| Learning with unsure responses K Takeoka, Y Dong, M Oyamada Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) 34 (01 …, 2020 | 6 | 2020 |
| Data processing device, data processing method, and recording medium M Oyamada US Patent 10,621,173, 2020 | 5 | 2020 |
| Table enrichment system for machine learning Y Dong, M Oyamada Proceedings of the 45th International ACM SIGIR Conference on Research and …, 2022 | 4 | 2022 |
| Compressed Vector Set: A Fast and Space-Efficient Data Mining Framework M Oyamada, J Liu, S Ito, K Narita, T Araki, H Kitagawa Journal of information processing 26, 416-426, 2018 | 4* | 2018 |
| PA-Proxy: Accerelating Data Aggregation in SQL-on-Hadoop Systems M Oyamada, T Cheng, K Narita, T Araki DEIM, 2015 | 3* | 2015 |
| DeepJoin: Joinable Table Discovery with Pre-trained Language Models Y Dong, C Xiao, T Nozawa, M Enomoto, M Oyamada arXiv preprint arXiv:2212.07588, 2022 | 1 | 2022 |
| Quality Control for Hierarchical Classification with Incomplete Annotations M Enomoto, K Takeoka, Y Dong, M Oyamada, T Okadome Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp …, 2021 | 1 | 2021 |
| Table-meaning estimating system, method, and program H Sato, S Nakadai, M Oyamada US Patent App. 16/322,549, 2019 | 1 | 2019 |
| Accelerating Feature Engineering with Adaptive Partial Aggregation Tree M Oyamada 2018 IEEE International Conference on Big Data (Big Data), 5417-5419, 2018 | 1 | 2018 |
| Efficient Machine Learning on Relational Data M Oyamada | 1 | 2018 |
| Efficient invocation of transaction sequences triggered by data streams M Oyamada, H Kawashima, H Kitagawa 2011 International Conference on P2P, Parallel, Grid, Cloud and Internet …, 2011 | 1 | 2011 |
| Jellyfish: A Large Language Model for Data Preprocessing H Zhang, Y Dong, C Xiao, M Oyamada arXiv preprint arXiv:2312.01678, 2023 | | 2023 |
| Correlation index construction device, correlation table search device, method, and program Y Dong, M Oyamada US Patent App. 18/024,188, 2023 | | 2023 |
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