Discovering Closed Periodic-Frequent Patterns in Very Large Temporal Databases. - Palla Likhitha; Penugonda Ravikumar; R. Uday Kiran; Yuto Hayamizu; Kazuo Goda; Masashi Toyoda; Koji Zettsu; Sourabh Shrivastava (IEEE BigData, 2020)

Discovering Maximal Partial Periodic Patterns in Very Large Temporal Databases. - Palla Likhitha; Pamalla Veena; R. Uday Kiran; Yutaka Watanobe; Koji Zettsu (IEEE BigData, 2021)

Efficient Discovery of Partial Periodic-Frequent Patterns in Temporal Databases. - So Nakamura; R. Uday Kiran; Palla Likhitha; Penugonda Ravikumar; Yutaka Watanobe; Minh-Son Dao; Koji Zettsu; Masashi Toyoda (DEXA (1), 2021)

Discovering Periodic-Frequent Patterns in Uncertain Temporal Databases. - R. Uday Kiran; Palla Likhitha; Minh-Son Dao; Koji Zettsu; Ji Zhang 001 (ICONIP (5), 2021)

Towards Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases. - Penugonda Ravikumar; Likhitha Palla; Rage Uday Kiran; Yutaka Watanobe; Koji Zettsu (IEA/AIE (1), 2021)

A Unified Framework to Discover Partial Periodic-Frequent Patterns in Row and Columnar Temporal Databases. - Pamalla Veena; So Nakamura; Palla Likhitha; R. Uday Kiran; Yutaka Watanobe; Koji Zettsu (ICDM (Workshops), 2021)

Towards Efficient Discovery of Partial Periodic Patterns in Columnar Temporal Databases. - Penugonda Ravikumar; Bathala Venus Vikranth Raj; Palla Likhitha; Rage Uday Kiran; Yutaka Watanobe; Sadanori Ito; Koji Zettsu; Masashi Toyoda (ACIIDS (2), 2022)

Discovering Top-k Periodic-Frequent Patterns in Very Large Temporal Databases. - Palla Likhitha; Penugonda Ravikumar; Rage Uday Kiran; Yutaka Watanobe (BDA, 2022)

Discovering Geo-referenced Periodic-Frequent Patterns in Geo-referenced Time Series Databases. - Penugonda Ravikumar; R. Uday Kiran; Palla Likhitha; T. Chandrasekhar; Yutaka Watanobe; Koji Zettsu (DSAA, 2022)

Towards developing energy efficient algorithms to discover partial periodic patterns in big temporal databases. - Palla Likhitha; Rage Uday Kiran (SIGSPATIAL/GIS, 2022)

UPFP-growth++: An Efficient Algorithm to Find Periodic-Frequent Patterns in Uncertain Temporal Databases. - Palla Likhitha; Rage Veena; Rage Uday Kiran; Koji Zettsu; Masashi Toyoda; Philippe Fournier-Viger (ICONIP (5), 2022)

Towards Efficient Discovery of Stable Periodic Patterns in Big Columnar Temporal Databases. - Hong N. Dao; Penugonda Ravikumar; Palla Likhitha; Bathala Venus Vikranth Raj; R. Uday Kiran; Yutaka Watanobe; Incheon Paik (IEA/AIE, 2022)

Discovering Top-K Partial Periodic Patterns in Big Temporal Databases. - Palla Likhitha; Rage Uday Kiran (DEXA (1), 2023)

Discovering Fuzzy Partial Periodic Patterns in Quantitative Irregular Multiple Time Series. - Pamalla Veena; Palla Likhitha; R. Uday Kiran; José María Luna; Philippe Fournier-Viger; Koji Zettsu (FUZZ, 2023)

Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases. - Palla Likhitha; Pamalla Veena; Rage Uday Kiran; Koji Zettsu (PAKDD (3), 2023)

Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases. - Hong N. Dao; Penugonda Ravikumar; Palla Likhitha; Rage Uday Kiran; Yutaka Watanobe; Incheon Paik (IEEE Access, 2023)

k-PFPMiner: Top-k Periodic Frequent Patterns in Big Temporal Databases. - Palla Likhitha; Penugonda Ravikumar; Deepika Saxena; Rage Uday Kiran; Yutaka Watanobe (IEEE Access, 2023)

A fundamental approach to discover closed periodic-frequent patterns in very large temporal databases. - Pamalla Veena; Rage Uday Kiran; Penugonda Ravikumar; Likhitha Palla; Yuto Hayamizu; Kazuo Goda; Masashi Toyoda; Koji Zettsu; Sourabh Shrivastava (Appl. Intell., 2023)

3P-ECLAT: mining partial periodic patterns in columnar temporal databases. - Pamalla Veena; Rage Uday Kiran; Penugonda Ravikumar; Likhitha Palla; Yutaka Watanobe; Sadanori Ito; Koji Zettsu; Masashi Toyoda; Bathala Venus Vikranth Raj (Appl. Intell., 2024)

A Novel Depth-First Search Algorithm for Partial Periodic-Frequent Pattern Mining in Temporal Databases. - Pamalla Veena; Vanitha Kattumuri; Yutaka Watanobe; Rage Uday Kiran; So Nakamura; Palla Likhitha; Koji Zettsu (IEEE Access, 2025)