Likhitha Palla

According to our database, Likhitha Palla authored at least 19 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

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., January, 2024

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., November, 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

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

Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases.

Palla Likhitha, Pamalla Veena, Rage Uday Kiran, Koji Zettsu

Proceedings of the Advances in Knowledge Discovery and Data Mining, 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

Proceedings of the IEEE International Conference on Fuzzy Systems, 2023

Discovering Top-K Partial Periodic Patterns in Big Temporal Databases.

Palla Likhitha, Rage Uday Kiran

Proceedings of the Database and Expert Systems Applications, 2023

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

Proceedings of the Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence, 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

Proceedings of the Neural Information Processing - 29th International Conference, 2022

Towards developing energy efficient algorithms to discover partial periodic patterns in big temporal databases.

Palla Likhitha, Rage Uday Kiran

Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 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

Proceedings of the 9th IEEE International Conference on Data Science and Advanced Analytics, 2022

Discovering Top-k Periodic-Frequent Patterns in Very Large Temporal Databases.

Palla Likhitha, Penugonda Ravikumar, Rage Uday Kiran, Yutaka Watanobe

Proceedings of the Big Data Analytics - 10th International Conference, 2022

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

Proceedings of the Intelligent Information and Database Systems - 14th Asian Conference, 2022

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

Proceedings of the 2021 International Conference on Data Mining, 2021

Towards Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases.

Penugonda Ravikumar, Likhitha Palla, Rage Uday Kiran, Yutaka Watanobe, Koji Zettsu

Proceedings of the Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices, 2021

Discovering Periodic-Frequent Patterns in Uncertain Temporal Databases.

R. Uday Kiran, Palla Likhitha, Minh-Son Dao, Koji Zettsu, Ji Zhang

Proceedings of the Neural Information Processing - 28th International Conference, 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

Proceedings of the Database and Expert Systems Applications, 2021

Discovering Maximal Partial Periodic Patterns in Very Large Temporal Databases.

Palla Likhitha, Pamalla Veena, R. Uday Kiran, Yutaka Watanobe, Koji Zettsu

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020

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

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020