September 29, 2020
Data Mining Technology Group
Data Science Research Labs
My research focuses on developing a new database technology that helps users to explore abundant data by incorporating state-of-the-art machine learning technologies with classical and solid database technologies. Specifically, I'm now interested in the following topics:
- Machine learning
- Latent variable model
- Bayesian nonparametrics
- Variational inference
- Database systems technology
- Query optimization
- Automated database design
- Data stream processing
- Transactional data stream processing
- Yuyang Dong, Chuan Xiao, Hanxiong Chen, Jefferey Xu Yu, Kunihiro Takeoka, Masafumi Oyamada, Hiroyuki Kitagawa, "Continuous Top-k Spatial-Keyword Search on Dynamic Objects", 2020, VLDB Journal
- Masafumi Oyamada, Jianquan Liu, Shinji Ito, Kazuyo Narita, Takuya Araki, Hiroyuki Kitagawa, "Compressed Vector Set: A Fast and Space-Efficient Data Mining Framework", Journal of Information Processing, Vol.26, pp. 416-426, 2018.
- Masafumi Oyamada, Hideyuki Kawashima, and Hiroyuki Kitagawa. Data Stream Processing with Concurrency Control. In SIGAPP Appllied Computing Review. 13, 2 (ACR), pages 54-65, 2013.
- Kunihiro Takeoka, Yuyang Dong, Masafumi Oyamada, "Learning from Unsure Responses", AAAI, 2020.
- Masafumi Oyamada, "Extracting Feature Engineering Knowledge from Data Science Notebooks", IEEE Big Data, 2019.
- Kunihiro Takeoka, Masafumi Oyamada, Shinji Nakadai, Takeshi Okadome, Meimei: An Efficient Probabilistic Approach for Semantically Annotating Tables, 33rd AAAI Conference on Artificial Intelligence (AAAI), 2019. (Acceptance rate: 1150/7095 = 16.2%)
- Masafumi Oyamada, Accelerating Feature Engineering with Adaptive Partial Aggregation Tree, IEEE BigData, 2018.
- Masafumi Oyamada, Adaptive Partial Aggregation Tree, The 2nd. cross-disciplinary Workshop on Computing Systems, Infrastructures, and Programming (xSIG 2018) (to appear), 2018.
- Masafumi Oyamada and Shinji Nakadai, Relational Mixture of Experts: Explainable Demographics Prediction with Behavioral Data, IEEE International Conference on Data Mining (ICDM), 2017 (Regular paper, Acceptance rate: 72/778 = 9.25%)
- Katsufumi Tomobe, Masafumi Oyamada, and Shinji Nakadai, Link Prediction for Isolated Nodes in Heterogeneous Network by Topic-based Co-Clustering, In Proceedings of the 21st Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), to appear, 2017.
- Masafumi Oyamada, Jianquan Liu, Kazuyo Narita, and Takuya Araki. MOARLE: Matrix Operation Accelerator based on Run-Length Encoding, In Proceedings of the 16th Asia-Pacific Web Conference (APWeb), pages 425-436, 2014 (Best paper runner up).
- Masafumi Oyamada, Hideyuki Kawashima, and Hiroyuki Kitagawa. Continuous Query Processing with Concurrency Control: Reading Updatable Resources Consistently, In Proceedings of the 28th ACM Symposium on Applied Computing (SAC), pages 788-794, 2013.
- Masafumi Oyamada, Hideyuki Kawashima, and Hiroyuki Kitagawa. Efficient Invocation of Transaction Sequences Triggered by Data Streams, In Proceedings of the 2nd International Workshop on Streaming Media Delivery and Management Systems (SMDMS), pages 332-337, 2011.
- Masafumi Oyamada, Hideyuki Kawashima, and Hiroyuki Kitagawa. Integration of Data Streams and Relations with Main Memory Database, In Proceedings of the 8th International Conference on Networked Sensing Systems (INSS), 2011 (poster).
- 2019/10 IPSJ Yamashita SIG Research Award
- 2018/09 WebDB Forum 2018 Best-paper Runner-up Award
- Best honorable poster in DEIM 2015
- Best paper runner up award in APWeb 2015
- Honorable poster in DEIM 2014
- Chair of department of computer science award from University of Tsukuba
- Honorable student talk in DEIM 2013
- IEICE Trans. 2014, 2016
- ADMS 2014 (External)
- AAAI 2021
- DEIM 2017
- JSAI 2017
- DEIM 2016
- 2011.3 B.S. in Computer Science from University of Tsukuba, Japan
- 2013.3 M.S. in Computer Science from University of Tsukuba, Japan
- 2018.3 Ph.D. in Computer Science from University of Tsukuba, Japan
- 2013.4 - today Research Staff Member at NEC Corporation