Big Data Analytics Laboratory

BIG DATA ANALYTICS LABORATORYBIG DATA ANALYTICS LABORATORY

Achievements

Major Publications

  1. Dao, M. S. and Zettsu, K. : Complex Event Analysis of Urban Environmental Data based on Deep CNN of Spatiotemporal Raster Images, 2018 IEEE International Conference on Big Data (BigData 2018), Seattle, WA, USA (December, 2018).[Accepted]
  2. Dao, M.S. and Zettsu, K.: A Raster-Image-Based Approach for Understanding Associations of Urban Sensing Data, IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE 2018), Laguna Hills, CA, USA, pp.134-137 (September 2018).
  3. Zhao, P. and Zettsu, K.: Convolution Recurrent Neural Networks for Short-Term Prediction of Atmospheric Sensing Data, The 4th IEEE International Conference on Smart Data (SmartData 2018), Halifax, Canada, pp.815-821 (July 2018).
  4. Sato, T. O., Sato, T. M., Sagawa, H., Noguchi, K., Saitoh, N., Irie, H., Kita, K., Mahani, M. E., Zettsu, K., Imasu, R., Hayashida, S., and Kasai, Y.: Vertical profile of tropospheric ozone derived from synergetic retrieval using three different wavelength ranges, UV, IR, and microwave: sensitivity study for satellite observation, Atmospheric Measurement Techniques, Vol. 11, pp.1653-1668 (March 2018). DOI: 10.5194/amt-11-1653-2018
  5. Tran-The, H. and Zettsu, K.: Discovering Co-occurrence Paterns of Heterogeneous Events from Unevenly-distributed Spatiotemporal Data, 2017 IEEE International Conference on Big Data (BigData 2017), Boston, MA, USA, pp.1006-1011 (December 2017). DOI: 10.1109/BigData.2017.8258023
  6. Tran-The, H. and Zettsu, K.: Finding Spatiotemporal Co-occurrence Patterns of Heterogeneous Events for Prediction, The 3rd ACM SIGSPATIAL International Workshop on the Use of GIS in Emergency Management (EM-GIS 2017), Redondo Beach, CA, USA, pp.9:1-9:8 (November 2017). DOI: 10.1145/3152465.3152475
  7. Takeuchi, S., Sugiura, K., Akahoshi, Y. and Zettsu, K.: Spatio-Temporal Pseudo Relevance Feedback for Scientific Data Retrieval, IEEJ Transactions on Electrical and Electronic Engineering, Vol. 12, Issue 1, pp.124-131 (January 2017).
  8. Kuroda, T., Medvedev, A. S., Yiğit, E., Hartogh, P.: Global Distribution of Gravity Wave Sources and Fields in the Martian Atmosphere during Equinox and Solstice Inferred From a High-Resolution General Circulation Model, Journal of the Atmospheric Sciences (September 2016).
  9. Akiba, T., Nakamura, K. and Takaguchi, T.: Fractality of Massive Graphs: Scalable Analysis with Sketch-Based Box-Covering Algorithm, IEEE 16th International Conference on Data Mining (ICDM2016), Barcelona, Spain, pp.769-774 (December 2016).
  10. Ito, S., Fujino, K., Ando, H.: Evaluation of work efficiency in the unmanned construction with communication delays , The 16th Construction Robot Symposium (September 2016). [Excellent Paper Award]
  11. Kuroda, T., Goto, D., Kasai, Y. and Zettsu, K.: Simulation of the Cloud and Aerosol Ddistributions with the Horizontal with the Horizontal resolution of ~5km using NICAM, 2nd workshop on Atmospheric Composition Observation System Simulation Experiments (OSSEs), Reading University, Reading, United Kingdom (November 2016).
  12. Kuroda, T., Goto, D., Y. Kasai, Y., Zettsu, K.:Demonstration of the atmospheric simulation for Kyushu region with the horizontal resolution of ~5km using NICAM: Towards the air pollution prediction system, 7th GEMS Science Team Meeting (October, 2016).