基本情報

所属
データベースシステム学講座
職位
准教授
E-Mail
udayrage@u-aizu.ac.jp
Webサイト
www.udayrage.com

教育

担当科目 - 大学
B.Tech. in Agricultural Engineering
担当科目 - 大学院
M.S. in IT in Agriculture
PhD in Computer science

研究

研究分野
Data Mining
Recommender systems
Transportation systems
略歴
Researcher at Big Data Analytics Laboratory, National Institute of Information and Communications Technology (NICT), Tokyo, Japan.
Project Assistant professor, Kitsuregawa Lab, Institute of Industrial Science, University of Tokyo, Tokyo, Japan.
Post Doctoral Fellow, Kitsuregawa Lab, Institute of Industrial Science, University of Tokyo, Tokyo, Japan.
PhD at International Institute of Information Technology-Hyderabad, Telangana, India.
Internship during Master degree at International Crop Research Insitute for Semi-Arid Tropics (ICRISAT).
現在の研究課題
Discovering user interest-based patterns in Very Large Spatiotemporal Databases
研究内容キーワード
Periodic pattern mining
Spatial pattern mining
Fuzzy pattern mining
所属学会
ACM

パーソナルデータ

趣味
Gaming and reading books.
子供時代の夢
Wanna design a robot
これからの目標
Become a renowned researcher
座右の銘
愛読書
Wealth of Nations
Fortune at the Bottom of Pyramid
学生へのメッセージ
Quality is often preferred over Quantity.
(E.g. A glass of Cow's milk is preferred over a bucket full of Donkey's milk)

So workhard and try to publish in top-tier conferences/journals. Refer CORE conference and journal ranks for publishing papers.
その他

主な著書・論文

R. Uday Kiran, P. P. C. Reddy, Koji Zettsu, Masashi Toyoda, Masaru Kitsuregawa, P. Krishna Reddy:Efficient Discovery of Weighted Frequent Neighborhood Itemsets in Very Large Spatiotemporal Databases. IEEE Access 8: 27584-27596 (2020)

Philippe Fournier-Viger, Jiaxuan Li, Jerry Chun-Wei Lin, Tin Truong-Chi, R. Uday Kiran:Mining cost-effective patterns in event logs. Knowl. Based Syst. 191: 105241 (2020)

Philippe Fournier-Viger, Zhitian Li, Jerry Chun-Wei Lin, Rage Uday Kiran, Hamido Fujita:Efficient algorithms to identify periodic patterns in multiple sequences. Inf. Sci. 489: 205-226 (2019)

Philippe Fournier-Viger, Chao Cheng, Jerry Chun-Wei Lin, Unil Yun, R. Uday Kiran:TKG: Efficient Mining of Top-K Frequent Subgraphs. BDA 2019: 209-226

P. P. C. Reddy, R. Uday Kiran, Koji Zettsu, Masashi Toyoda, P. Krishna Reddy, Masaru Kitsuregawa:Discovering Spatial High Utility Frequent Itemsets in Spatiotemporal Databases. BDA 2019: 287-306

R. Uday Kiran, C. Saideep, Koji Zettsu, Masashi Toyoda, Masaru Kitsuregawa, P. Krishna Reddy:Discovering Partial Periodic Spatial Patterns in Spatiotemporal Databases. BigData 2019: 233-238

T. Yashwanth Reddy, R. Uday Kiran, Masashi Toyoda, P. Krishna Reddy, Masaru Kitsuregawa:Discovering Partial Periodic High Utility Itemsets in Temporal Databases. DEXA (2) 2019: 351-361

Rage Uday Kiran, P. P. C. Reddy, Koji Zettsu, Masashi Toyoda, Masaru Kitsuregawa, P. Krishna Reddy:Discovering Spatial Weighted Frequent Itemsets in Spatiotemporal Databases. ICDM Workshops 2019: 987-996

C. Saideep, Rage Uday Kiran, Koji Zettsu, Philippe Fournier-Viger, Masaru Kitsuregawa, P. Krishna Reddy:Discovering Periodic Patterns in Irregular Time Series. ICDM Workshops 2019: 1020-1028

Philippe Fournier-Viger, Peng Yang, Jerry Chun-Wei Lin, Rage Uday Kiran:Discovering Stable Periodic-Frequent Patterns in Transactional Data. IEA/AIE 2019: 230-244

R. Uday Kiran, T. Yashwanth Reddy, Philippe Fournier-Viger, Masashi Toyoda, P. Krishna Reddy, Masaru Kitsuregawa:Efficiently Finding High Utility-Frequent Itemsets Using Cutoff and Suffix Utility. PAKDD (2) 2019: 191-203

R. Uday Kiran, Koji Zettsu, Masashi Toyoda, Philippe Fournier-Viger, P. Krishna Reddy, Masaru Kitsuregawa:Discovering Spatial High Utility Itemsets in Spatiotemporal Databases. SSDBM 2019: 49-60

J. N. Venkatesh, R. Uday Kiran, P. Krishna Reddy, Masaru Kitsuregawa:Discovering Periodic-Correlated Patterns in Temporal Databases. Trans. Large Scale Data Knowl. Centered Syst. 38: 146-172 (2018)

R. Uday Kiran, Amulya Kotni, P. Krishna Reddy, Masashi Toyoda, Subhash Bhalla, Masaru Kitsuregawa:Efficient Discovery of Weighted Frequent Itemsets in Very Large Transactional Databases: A Re-visit. BigData 2018: 723-732

Philippe Fournier-Viger, Zhitian Li, Jerry Chun-Wei Lin, Rage Uday Kiran, Hamido Fujita:Discovering Periodic Patterns Common to Multiple Sequences. DaWaK 2018: 231-246

Amulya Kotni, R. Uday Kiran, Masashi Toyoda, P. Krishna Reddy, Masaru Kitsuregawa:Novel Data Segmentation Techniques for Efficient Discovery of Correlated Patterns Using Parallel Algorithms. DaWaK 2018: 355-370

Qian Li, Ziwei Li, Jin-Mao Wei, Zhenglu Yang, Yanhui Gu, R. Uday Kiran:A Story Coherence based Neural Network Model for Predicting Story Ending. WWW (Companion Volume) 2018: 119-120

R. Uday Kiran, J. N. Venkatesh, Masashi Toyoda, Masaru Kitsuregawa, P. Krishna Reddy:Discovering partial periodic-frequent patterns in a transactional database. J. Syst. Softw. 125: 170-182 (2017)

Alampally Anirudh, R. Uday Kiran, P. Krishna Reddy, Masashi Toyoda, Masaru Kitsuregawa:An Efficient Map-Reduce Framework to Mine Periodic Frequent Patterns. DaWaK 2017: 120-129

R. Uday Kiran, J. N. Venkatesh, Philippe Fournier-Viger, Masashi Toyoda, P. Krishna Reddy, Masaru Kitsuregawa:Discovering Periodic Patterns in Non-uniform Temporal Databases. PAKDD (2) 2017: 604-617

R. Uday Kiran, Haichuan Shang, Masashi Toyoda, Masaru Kitsuregawa:Discovering Partial Periodic Itemsets in Temporal Databases. SSDBM 2017: 30:1-30:6

R. Uday Kiran, Masaru Kitsuregawa, P. Krishna Reddy:Efficient discovery of periodic-frequent patterns in very large databases. J. Syst. Softw. 112: 110-121 (2016)

J. N. Venkatesh, R. Uday Kiran, P. Krishna Reddy, Masaru Kitsuregawa:Discovering Periodic-Frequent Patterns in Transactional Databases Using All-Confidence and Periodic-All-Confidence. DEXA (1) 2016: 55-70

...,Our paper titled "Discovering Fuzzy Periodic-Frequent Patterns in Quantitative Temporal Databases" has been accepted for the publication in IEEE FUZZY 2020. (CORE Ranking, A)

Our paper titled "Parallel Mining of Partial Periodic Itemsets in big data" have been accepted for the publication in IEA/AIE 2020. (CORE Ranking, B)

Our paper titled "Efficient Discovery of Weighted Frequent Neighborhood Itemsets in Very Large Spatiotemporal databases" has been accepted for publication in the prestigious IEEE ACCESS.(Impact factor: 4.098)