Big Data Integration Research Center

Big Data Integration Research CenterBig Data Integration Research Center




  1. Kiran,R.U.,Ito,S.,Dao,M.S.,Zettsu,K.,Wu,C.W.,Watanobe,Y.,Paik,I.,Thang,T.C.:Distributed Mining of Spatial High Utility Itemsets in Very Large Spatiotemporal Databases using Spark In-Memory Computing Architecture,2020 IEEE International Conference on Big Data(IEEE Big Data 2020),online,pp.4727-4733(December 2020).
  2. Duong,D.Q.,Le,Q.M.,Nguyen,T.T.L.,Bo,D.,Nguyen,D.,Dao,M.S.,Nguyen,B.T.:Multi-source Machine Learning for AQI Estimation,2020 IEEE International Conference on Big Data(IEEE Big Data 2020),online,pp.4567-4576(December 2020).
  3. Zhao,P.,Zettsu,K.:MASTGN: Multi-Attention Spatio-Temporal Graph Networks for Air Pollution Prediction,2020 IEEE International Conference on Big Data(IEEE Big Data 2020),online,pp.1442-1448(December 2020).
  4. Dao,M.S.,Nguyen,N.T.,Kiran,R.U.,Zettsu,K.:Fusion-3DCNN-max3P: A dynamic system for discovering patterns of predicted congestion,2020 IEEE International Conference on Big Data(IEEE Big Data 2020),online,pp.910-915(December 2020).
  5. Likhitha,P.,Ravikumar,P.,Kiran,R.U.,Hayamizu,Y.,Goda,K.,Toyoda,M.,Zettsu,K.,Shrivastava,S.:Discovering Closed Periodic-Frequent Patterns in Very Large Temporal Databases,2020 IEEE International Conference on Big Data(IEEE Big Data 2020),online,(December 2020).
  6. Tsukada,R.,Zhan,H.,Ishiwatari,S.,Toyoda,M.,Umemoto,K.,Shang,H.,Zettsu,K.:Crowd Forecasting at Venues with Microblog Posts Referring to Future Events,2020 IEEE International Conference on Big Data Workshop on Big Spatial Data(IEEE BigData 2020),online(December 2020).
  7. Zhao,P.,Dao,M.S.,Nguyen,N.T.,Nguyen,T.B.,Nguyen,D.T.D.,Gurrin,C.:Overview of MediaEval 2020 Insights for Wellbeing: Multimodal Personal Health Lifelog Data Analysis,2020 MediaEval Multimedia Benchmark(MediaEval 2020),online,pp.1-2(December 2020).
  8. Dao,M.S.,Nguyen,N.T.,Kiran,R.U.,Zettsu,K.:Insights From Urban Sensing Data: From Chaos to Predicted Congestion Patterns,20th IEEE International Conference on Data Mining ICDM 2020 - UDML workshop,online(November 2020).
  9. Kiran,R.U.,Shrivastava,S.,Viger,P,F.,Zettsu,K.,Toyoda,M.,Kitsuregawa,M.:Discovering Frequent Spatial Patterns in Very Large Spatiotemporal Databases,28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2020),online,pp.445-448(November 2020).
  10. Nguyen,A.V.M.,Tran,V.L.,Dao,M.S.,Zettsu,K.:Leverage the Predictive Power Score of Lifelog Data's Attributes to Predict the Expected Athlete Performance,CLEF 2020 - imageCLEFlifelog,online,Vol.2696,pp.1-13(October 2020).
  11. Tran,V.L.,Khoa,T.D.,Nguyen,A.V.M.,Vo,A.K.,Dao,M.S.,Zettsu,K.:An interactive atomic-cluster watershed-based system for lifelog moment retrieval,CLEF 2020 - imageCLEFlifelog,online,Vol.2696,pp.1-16(October 2020).
  12. Nguyen,N.T.,Dao,M.S.,Zettsu,K.:Leveraging 3D-Raster-Images and DeepCNN with Multi-source Urban Sensing Data for Traffic Congestion Prediction,The 31st International Conference on Database and Expert Systems Applications - DEXA2020,online,Vol.12392,pp.396-406(September 2020).
  13. Kiran,R.U.,Watanobe,Y.,Chaudhury,B.,Zettsu,K.,Toyoda,M.,Kitsuregawa,M.:Discovering Maximal Periodic-Frequent Patterns in Very Large Temporal Databases,The 7th IEEE International Conference on Data Science and Advanced Analytics(DSAA 2020),online,pp.11-20(September 2020).
  14. Sato,T.,Kuroda,T.,Kasai,Y.:Novel index to comprehensively evaluate air cleanliness: the Clean aIr Index (CII),Geoscience Communication Vol.3,pp.233-247(August 2020).
  15. Kiran,R.U.,Saideep,C.,Ravikumar,P.,Zettsu,K.,Toyoda,M.,Kitsuregawa,M.,Reddy,P.K.:Discovering Fuzzy Periodic-Frequent Patterns in Quantitative Temporal Databases,2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE),online,pp.1-8(July 2020).
  16. Zettsu,K.:From Data Collection Merit to Data Connection Merit for Smart Sustainable Cities,The 2020 on Intelligent Cross-Data Analysis and Retrieval Workshop (ICDAR '20),online,pp.1-2(July 2020).【基調講演】
  17. Nguyen,A.V.M.,Phan,T.D.,Nguyen,A.K.,Trang,V.L.,Dao,M.S.,Zettsu,K.:BIDAL-HCMUS@LSC2020: An Interactive Multimodal Lifelog Retrieval with Query-to-Sample Attention-based Search Engine,LSC@ICMR 2020,online,pp.43-49(June 2020).
  18. Nguyen,D.,Nguyen,Q.H.,Dao,M.S.,Nguyen,D.T.D.,Gurrin,C.,Nguyen,B.T.:Duplicate Identification Algorithms in SaaS Platforms,The 2020 on Intelligent Cross-Data Analysis and Retrieval Workshop (ICDAR '20) ,online,pp.33-38(June 2020).
  19. Nguyen,T.P.V.,Thanh,T.T.:Microwave Doppler Radar Sensing System for Vital Sign Detection: From Evaluated Accuracy Models to the Intelligent System,The 2020 on Intelligent Cross-Data Analysis and Retrieval Workshop (ICDAR '20),online,pp.3-8(June 2020).
  20. Nguyen,N.T.,Pham,T.T.,Dang,T.X.,Dao,M.S.,Nguyen,D.T.D.,Gurrin,C.,Nguyen,B.T.:Malware Detection Using System Logs,The 2020 on Intelligent Cross-Data Analysis and Retrieval Workshop (ICDAR '20),online,pp.9-14(June 2020).
  21. Cuong,D.V.,Nguyen,D.H.,Huynh,S.,Huynh,P.,Gurrin,C.,Dao,M.S.,Nguyen,D.T.D.,Nguyen,B.T.:A Framework for Paper Submission Recommendation System,ACM ICMR 2020(ICMR 2020),online,pp.393-396(June 2020).
  22. Nguyen,Q.H.,Nguyen,D.H.,Dao,M.S.,Nguyen,D.T.D.,Gurrin,C.,Nguyen,B.T.:An Active Learning Framework for Duplicate Detection in SaaS Platforms,ACM ICMR 2020(ICMR 2020),online,pp.412-415(June 2020).
  23. Tran,V.L.,Nguyen,A.V.M.,Phan,T.D.,Vo,A.K.,Dao,M.S.,Zettsu,K.:An Interactive Multimodal Retrieval System for Memory Assistant and Life Organized Support,ACM ICMR 2020(ICMR 2020),online,pp.416-420(June 2020).
  24. Phan,T.D.,Dao,M.S.,Zettsu,K.:Lifelog Moment Retrieval With Interactive Watershed-Based Clustering and Hierarchical Similarity Search,International Journal of Multimedia Data Engineering and Management (IJMDEM)11(2),pp.31-48(April 2020).
  25. Kiran,R.U.,Reddy,P.P.C.,Zettsu,K.,Toyoda,M.,Kitsuregawa,M.,Reddy,P.K.:Efficient Discovery of Weighted Frequent Neighborhood Itemsets in Very Large Spatiotemporal Databases,IEEE Access Vol.8,pp.27584-27596(January 2020).
  26. Luu,V.H.,Dao,M.S.,Nguyen,T.N.T.,Perry,S.,Zettsu,K.:Semi-supervised Convolutional Neural Networks for Flood Mapping using Multi-modal Remote Sensing Data, 2019 6th NAFOSTED Conference on Information and Computer Science (NICS),Hanoi,, Vietnam(December 2019).
  27. Ito,S.,Zettsu,K.:Assessing a risk-avoidance navigation system based on localized torrential rain data,2019 8th International Conference on Transportation and Traffic Engineering (ICTTE 2019),Auckland,, New Zealand,pp.30-34(December 2019).
  28. Zettsu,K.:Transforming Sensing Data into Smart Data for Smart Sustainable Cities,The Seventh International Conference on Big Data Analytics BDA2019),Ahmedabad,, Gujarat,,India,Vol.11932,pp.3-19(December 2019).【招待講演】
  29. Dao,M.S.,Nguyen,N.T.,Zettsu,K.:Multi-time-horizon Traffic Risk Prediction using Spatio-Temporal Urban Sensing Data Fusion,2019 IEEE International Conference on (Big Data(IEEE Big Data 2019),Los Angeles,, USA,pp.2205-2214(December 2019).
  30. Vo,P.B.,Phan,T.D.,Dao,M.S.,Zettsu,K.:Association Model between Visual Feature and AQI Rank Using Lifelog Data,2019 IEEE International Conference on Big Data (IEEE Big Data 2019),Los Angeles,, USA,pp.4197-4200(December 2019).
  31. Zhao,P.,Zettsu,K.:Decoder Transfer Learning for Predicting Personal Exposure to Air Pollution,2019 IEEE International Conference on Big Data(IEEE Big Data 2020),Los Angeles,, USA,pp.5620-5629(December 2019).
  32. Nguyen,N.T.,Dao,M.S.,Zettsu,K.:Complex Event Analysis for Traffic Risk Prediction based on 3D-CNN with Multi-sources Urban Sensing Data,2019 IEEE International Conference on Big Data(IEEE Big Data 2019),Los Angeles,, USA,pp.1669-1674(November 2019).
  33. Kiran, R. U., Saideep, C., Zettsu, K., Toyoda, M., Reddy, P. K.: Discovering Partial Periodic Spatial Patterns in Spatiotemporal Databases, 2019 IEEE International Conference on Big Data (IEEE BigData 2019), Los Angeles, CA, USA (December 2019).
  34. Dao, M. S., Zhao, P., Sato, T., Zettsu, K., Dang-Nguyen D. T., Gurrin, C., Nguyen, N. T.: Overview of MediaEval 2019: Insights for Wellbeing TaskMultimodal Personal Health Lifelog Data Analysis, MediaEval Benchmarking Initiative for Multimedia Evaluation (MediaEval 2019), Antipolis, France (November 2019).
  35. Dao, M. S., Vo, A. K., Phan, T. D., Zettsu, K.: BIDAL@imageCLEFlifelog2019: The Role of Content and Context of Daily Activities in Insights from Lifelogs, CLEF2019 Working Notes, CEUR Workshop Proceedings, Lugano, Switzerland, (September 2019).
  36. Phan, T. D., Dao, M. S., Zettsu, K.: An Interactive Watershed-Based Approach for Lifelog Moment Retrieval, 5th IEEE International Conference on Multimedia Big Data (BigMM 2019), Singapore, pp.282-286 (September, 2019).
  37. Vo, A. K., Dao, M.S., Zettsu, K.: From Chaos to Order: The Role of Content and Context of Daily Activities in Rearranging Lifelogs Data, 5th IEEE International Conference on Multimedia Big Data (BigMM 2019), Singapore, pp.292-296 (September, 2019).
  38. Ito, S., Zettsu, K.: Report on a Hackathon for Car Navigation Using Traffic Risk Data, 3rd International Conference on Intelligent Traffic and Transportation (ICITT 2019), Amsterdam, The Netherlands (September, 2019).
  39. Kiran, R. U., Zettsu, K., Toyoda, M. Kitsuregawa, M., Philippe F. V., Reddy, P. K.: Discovering Spatial High Utility Itemsets in Spatiotemporal Databases, 31st International Conference on Scientific and Statistical Database Management (SSDBM 2019), Santa Cruz, CA, USA, pp.49-61 (July, 2019).
  40. Lv, Z., Mehmood, I., Vento, M., Dao, M. S., Ota, K., Saggese A.: IEEE Access Special Section Editoria: Multimedia Analysis for Internet-of-Things, IEEE Access Vol. 7, pp.65211-65218 (May 2019). DOI: 10.1109/ACCESS.2019.2915487
  41. 是津耕司: 都市環境ビッグデータの統合分析基盤, 環境技術 48(3), pp.116-120 (2019年5月).
  42. Zhao, P., Zettsu, K.: Convolution Recurrent Neural Networks Based Dynamic Transboundary Air Pollution Prediction, 2019 IEEE Big Data Analytics (ICDBA 2019), Suzhou, China, pp.410-413 (March, 2019).
  43. Sato, T., Dao, M. S., Kuribayashi, K. and Zettsu, K.: SEPHLA: Challenges and Opportunities Within Environment-Personal Health Archives, 25th International Conference on MultiMedia Modeling (MMM 2019) Thessaloniki, Greece, Lecture Notes in Computer Science Vol. 11295, pp 325-337 (January, 2019). DOI: 978-3-030-05710-7_27
  44. 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, pp.2160-2169 (December, 2018).DOI: 10.1109/BigData.2018.8621916
  45. 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).
  46. 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).
  47. 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
  48. 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
  49. 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
  50. 是津耕司(編), スマートIoT推進フォーラム異分野データ連携プロジェクト (著): 異分野データ連携H28年度技術報告書 ~ データでつなぐ人・モノ・コト ~, エクスイズムCAS出版 (2017年6月).[Webページ版]
  51. 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).
  52. 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).
  53. 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).
  54. 伊藤禎宣, 藤野健一, 安藤広志: 映像通信遅延が建機の遠隔操作性に与える影響のモデルタスクによる評価, 第16回建設ロボットシンポジウム (2016年9月). 【優秀論文賞】
  55. 佐藤知紘(2016)「宇宙から観たオゾン同位体」, 河村公隆編『低温環境の科学辞典』, pp.30-31 朝倉書店.
  56. 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).
  57. 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).
  58. 豊村鉄男, 木全崇, 是津耕司, 西永望, 木俵豊: ネットワーク構成装置及び方法、並びにネットワーク構成のためのコンピュータプログラム, 特許第6021000号 (2016年10月).
  59. 石井昌憲, 笠井康子, 白石浩一, 佐藤知紘, 黒田剛史, 栗林康太, 重兼史尚, 松田猛, 水谷耕平, 安井元昭, 是津耕司: スケーラブルな環境データの解析と利活用技術の開発, 福岡から診る大気環境研究所研究会 (2016年9月).
  60. 高口太朗: テンポラル・ネットワークとしての社会コミュニケーション行動の分析, 電子情報通信学会 通信行動工学研究会 (2016年8月).


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  8. 読売新聞朝刊(関西)サイエンスBOX 「局地豪雨 前兆つかみ減災 『京』で30分前に予測/雲粒で察知」 (2016年7月29日).[リンク]
  9. NHKニュースホット関西 「『雲』の発生捉え 予測」 (2016年7月8日).