Rage Uday Kiran ＲａｇｅＵｄａｙ・Ｋｉｒａｎ

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Rage Uday・Kiran ラゲウダイ・キラン

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Researcher Number	20874324
Affiliation (Current)	2025: 会津大学, コンピュータ理工学部, 准教授
Affiliation (based on the past Project Information) *help	2021 – 2023: 会津大学, コンピュータ理工学部, 准教授
Review Section/Research Field	Principal Investigator Basic Section 61030:Intelligent informatics-related
Keywords	Principal Investigator Air pollution analytics / Pattern mining / data mining / spatial information / high utility patterns / Big data

Exploring Novel Mathematical Models and Efficient Algorithms to Discover Periodic Spatial Patterns in Irregular Spatiotemporal Big DataPrincipal Investigator
- Principal Investigator
  
  Rage Uday・Kiran
- Project Period (FY)
  2021 – 2024
- Research Category
  
  Grant-in-Aid for Scientific Research (C)
- Review Section
  
  Basic Section 61030:Intelligent informatics-related
- Research Institution
  The University of Aizu

All 2023 2022 2021

All Journal Article Presentation Book

[Book] Periodic Pattern Mining Theory, Algorithms, and Applications2021
- Author(s)
  Rage Uday Kiran, Philippe Fournier-Viger, Jose Maria Luna, Jerry Chun-Wei Lin, Anirban Mondal
- Total Pages
  263
- Publisher
  Springer
- ISBN
  9789811639630
- Data Source
  KAKENHI-PROJECT-21K12034
[Journal Article] A Novel Explainable Link Forecasting Framework for the Temporal Knowledge Graphs Using Time-Relaxed Cyclic and?Acyclic Rules2023
- Author(s)
  Rage Uday Kiran, Maharana Abinash, Polepalli Krishna Reddy
- Journal Title
  
  PAKDD
  
  Volume: 1 Pages: 264-275
- DOI
  10.1007/978-3-031-33374-3_21
- ISBN
  9783031333736, 9783031333743
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-21K12034
[Journal Article] Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements2023
- Author(s)
  Veena Pamalla, Sreepada Tarun, Kiran Rage Uday, Dao Minh-Son, Zettsu Koji, Watanobe Yutaka, Zhang Ji
- Journal Title
  
  IEEE Access
  
  Volume: 11 Pages: 118676-118688
- DOI
  10.1109/access.2023.3326419
- Open Access / Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-21K12034
[Journal Article] HDSHUI-miner: a novel algorithm for discovering spatial high-utility itemsets in high-dimensional spatiotemporal databases2023
- Author(s)
  Uday Kiran Rage、Veena Pamalla、Ravikumar Penugonda、Venus Vikranth Raj Bathala、Dao Minh-Son、Zettsu Koji、Bommisetti Sai Chithra
- Journal Title
  
  Applied Intelligence
  
  Volume: 53 Issue: 8 Pages: 8536-8561
- DOI
  10.1007/s10489-022-04436-w
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-21K12034
[Journal Article] Discovering Geo-referenced Frequent Patterns in the Uncertain Geo-referenced Transactional Databases2023
- Author(s)
  Likhitha Palla,Veena Pamalla, Rage Uday Kiran, Zettsu Koji
- Journal Title
  
  PAKDD
  
  Volume: 1 Pages: 29-41
- DOI
  10.1007/978-3-031-33380-4_3
- ISBN
  9783031333798, 9783031333804
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-21K12034
[Journal Article] Efficient Discovery of Partial Periodic Patterns in Large Temporal Databases2022
- Author(s)
  Kiran Rage Uday、Veena Pamalla、Ravikumar Penugonda、Saideep Chennupati、Zettsu Koji、Shang Haichuan、Toyoda Masashi、Kitsuregawa Masaru、Reddy P. Krishna
- Journal Title
  
  Electronics
  
  Volume: 11 Issue: 10 Pages: 1523-1523
- DOI
  10.3390/electronics11101523
- Data Source
  KAKENHI-PROJECT-21K12034
[Journal Article] Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases2021
- Author(s)
  Ravikumar Penugonda、Likhitha Palla、Raj Bathala Venus Vikranth、Kiran Rage Uday、Watanobe Yutaka、Zettsu Koji
- Journal Title
  
  Electronics
  
  Volume: 10 Issue: 12 Pages: 1478-1478
- DOI
  10.3390/electronics10121478
- Data Source
  KAKENHI-PROJECT-21K12034
[Presentation] Towards Efficient Discovery of Partial Periodic Patterns in Columnar Temporal Databases2022
- Author(s)
  Penugonda Ravikumar, Bathala Venus Vikranth Raj, Palla Likhitha, Rage Uday Kiran, Yutaka Watanobe, Sadanori Ito, Koji Zettsu, Masashi Toyoda:
- Organizer
  ACIIDS
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-21K12034
[Presentation] UPFP-growth++: An Efficient Algorithm to Find Periodic-Frequent Patterns in Uncertain Temporal Databases.2022
- Author(s)
  Palla Likhitha, Rage Veena, Rage Uday Kiran, Koji Zettsu, Masashi Toyoda, Philippe Fournier-Viger:
- Organizer
  ICONIP
- Int'l Joint Research
- Data Source
  KAKENHI-PROJECT-21K12034

1. 是津耕司 (40415857)

# of Collaborated Projects: 1 results

# of Collaborated Products: 7 results

Rage Uday Kiran Ｒａｇｅ Ｕｄａｙ・Ｋｉｒａｎ

Rage Uday・Kiran ラゲ ウダイ・キラン

Research Projects

Research Products

Co-Researchers

Exploring Novel Mathematical Models and Efficient Algorithms to Discover Periodic Spatial Patterns in Irregular Spatiotemporal Big DataPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

[Book] Periodic Pattern Mining Theory, Algorithms, and Applications2021

Author(s)

Total Pages

Publisher

ISBN

Data Source

[Journal Article] A Novel Explainable Link Forecasting Framework for the Temporal Knowledge Graphs Using Time-Relaxed Cyclic and?Acyclic Rules2023

Author(s)

Journal Title

DOI

ISBN

Data Source

[Journal Article] Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements2023

Author(s)

Journal Title

DOI

Data Source

[Journal Article] HDSHUI-miner: a novel algorithm for discovering spatial high-utility itemsets in high-dimensional spatiotemporal databases2023

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Discovering Geo-referenced Frequent Patterns in the Uncertain Geo-referenced Transactional Databases2023

Author(s)

Journal Title

DOI

ISBN

Data Source

[Journal Article] Efficient Discovery of Partial Periodic Patterns in Large Temporal Databases2022

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases2021

Author(s)

Journal Title

DOI

Data Source

[Presentation] Towards Efficient Discovery of Partial Periodic Patterns in Columnar Temporal Databases2022

Author(s)

Organizer

Data Source

[Presentation] UPFP-growth++: An Efficient Algorithm to Find Periodic-Frequent Patterns in Uncertain Temporal Databases.2022

Author(s)

Organizer

Data Source

Rage Uday Kiran ＲａｇｅＵｄａｙ・Ｋｉｒａｎ

Rage Uday・Kiran ラゲウダイ・キラン