FACULTY

Research and Industry-Academia Collaboration

研究・産学連携

RAGE Uday Kiran

Associate Professor

Affiliation: Department of Computer Science and Engineering/Division of Information Systems

Title: Associate Professor

E-Mail: udayrage@u-aizu.ac.jp

Web site: https://www.u-aizu.ac.jp/~udayrage

Education

Courses - Undergraduate: B.Tech. in Agricultural Engineering

Courses - Graduate: M.S. in IT in Agriculture
PhD in Computer science

Research

Specialization: Database
Data Mining
Recommender systems
Transportation systems

Educational Background, Biography: 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).

Current Research Theme: Discovering user interest-based patterns in Very Large Spatiotemporal Databases

Key Topic: Periodic pattern mining
Spatial pattern mining
Fuzzy pattern mining

Affiliated Academic Society: ACM

Others

Hobbies: Gaming and reading books.

School days' Dream: Wanna design a robot

Current Dream: Become a renowned researcher

Favorite Books: Wealth of Nations
Fortune at the Bottom of Pyramid

Messages for Students: 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.

Read this article:
https://udayrage.wixsite.com/mysite/post/core-conference-and-journal-rankings

Main research

Discovering Periodic Patterns in Temporal Databases

Periodic Patterns are an important class of regularities that exist in a temporal databases. In this study, we have investigated novel models and fast algorithms to find interesting periodic patterns in very large databases.

We have published papers in top-tier conferences, such as EDBT (CORE rank A), SSDBM (CORE rank A), PAKDD (CORE RANK A) and IEEE BIG DATA (CORE rank not yet available).

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Discovering spatial high utility itemsets in spatiotemporal databases

High Utility Itemset Mining (HUIM) is an important knowledge discovery technique in data mining. It aims to discover all itemsets that have high value in the data. Most previous works focussed on finding these itemsets in transactional databases, and did not take into account the spatiotemporal characteritics of an item in the data. Consequently, HUIM fails to discover useful information hidden in very large spatiotemporal data.

We propose a generic model of Spatial High Utility Itemset (SHUI) that may exist in a spatiotemporal database. The significance of the proposed itemsets has been demonstrated with a case study on traffic congestion data.

(Collaboration with NICT, Japan. NICT has provided the traffic congestion and rainfall data.)

Publication:
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)

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

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

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Dissertation and Published Works

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)

Last updated: 2024.11.21