March 30, 2018
Data Mining Technology Group
Data Science Research Labs
- Database, Distributed Computing
- Machine learning
- B.S. (2001), School of Engineering, The University of Tokyo
- M.S. (2003), Graduate School of Engineering, The University of Tokyo
- Visiting Scholar (2012-13), Department of Computer Science, The University of California, Berkeley, (AMPLab / Prof. Michael J. Franklin).
- 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.
- M. Asahara, S. Nakadai, and T. Araki, "LoadAtomizer: A locality and I/O load aware task scheduler for MapReduce, " In Proc. of the 4th IEEE Int'l Conf. on Cloud Computing Technology and Science (CloudCom'12), pp. 317-324, 2012.
- K. Narita, S. Nakadai, and T. Araki, "Landmark-Join: Hash-Join Based String Similarity Joins with Edit Distance Constraints," In Proc. of the 14th Int'l Conf. on Data Warehousing and Knowledge Discovery (DaWaK), pp. 180-191, 2012.
- H. Tamano, S. Nakadai, and T. Araki, “Optimizing Multiple Machine Learning Jobs on MapReduce,” In Proc. of the 3rd IEEE Int'l Conf. on Cloud Computing Technology and Science(CloudCom'11), pp. 59-66, 2011.
- S. Nakadai and K. Taniguchi, "Server Capacity Planning with Priority Allocation for Service Level Management in Heterogeneous Server Clusters, " IFIP/IEEE Int'l Symposium on Integrated Network Management(IM2007), 2007.