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Note: Due to very high competition and fast paced research in the field of computer science, any publication in a top-tier conference (i.e., CORE rank A* and A) were treated on par with any major journal publication. CORE 2010 ranking were employed to rank conferences.

S. No. Title CORE rank
86 Palla Likhitha, Pamalla Veena, Rage Uday Kiran, Koji Zettsu:Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases. PAKDD (3) 2023: 29-41 A
85 Rage Uday Kiran, Abinash Maharana, Krishna Reddy Polepalli:A Novel Explainable Link Forecasting Framework for Temporal Knowledge Graphs Using Time-Relaxed Cyclic and Acyclic Rules. PAKDD (1) 2023: 264-275 A
84 Penugonda Ravikumar, Bathala Venus Vikranth Raj, Palla Likhitha, Rage Uday Kiran, Yutaka Watanobe, Sadanori Ito, Koji Zettsu, Masashi Toyoda:Towards Efficient Discovery of Partial Periodic Patterns in Columnar Temporal Databases. ACIIDS (2) 2022: 141-154 B
83 Palla Likhitha, Penugonda Ravikumar, Rage Uday Kiran, Yutaka Watanobe:Discovering Top-k Periodic-Frequent Patterns in Very Large Temporal Databases. BDA 2022: 200-210 -
82 Sreepada Tarun, Rage Uday Kiran, Yutaka Watanobe, Kazuo Goda: A Novel GPU-Accelerated Algorithm to Discover Periodic-Frequent Patterns in Temporal Databases. Big Data 2022: 121-126 B
81 R. Uday Kiran, Vipul Chhabra, Saideep Chennupati, P. Krishna Reddy, Minh-Son Dao, Koji Zettsu:A Novel Null-Invariant Temporal Measure to Discover Partial Periodic Patterns in Non-uniform Temporal Databases. DASFAA (1) 2022: 569-577 B
80 Qiliang Liang, Ji Zhang, Mohamed Jaward Bah, Hongzhou Li, Liang Chang, Rage Uday Kiran:Effective and Robust Boundary-Based Outlier Detection Using Generative Adversarial Networks. DEXA (2) 2022: 174-187 B
79 Pamalla Veena, Sreepada Tarun, R. Uday Kiran, Minh-Son Dao, Koji Zettsu, Yutaka Watanobe, Ji Zhang:Towards Efficient Discovery of Periodic-Frequent Patterns in Dense Temporal Databases Using Complements. DEXA (2) 2022: 204-215 B
78 Pamalla Veena, Penugonda Ravikumar, Kundai Kwangwari, R. Uday Kiran, Kazuo Goda, Yutaka Watanobe, Koji Zettsu: Discovering Fuzzy Geo-referenced Periodic-Frequent Patterns in Geo-referenced Time Series Databases. FUZZ-IEEE 2022: 1-8 A
77 Vipul Chhabra, R. Uday Kiran, Juan Xiao, P. Krishna Reddy, Ram Avtar:A Spatiotemporal Image Fusion Method for Predicting High-Resolution Satellite Images. IEA/AIE 2022: 470-481 B
76 Hong N. Dao, Penugonda Ravikumar, P. Likitha, Bathala Venus Vikranth Raj, R. Uday Kiran, Yutaka Watanobe, Incheon Paik:Towards Efficient Discovery of Stable Periodic Patterns in Big Columnar Temporal Databases. IEA/AIE 2022: 831-843 B
75 R. Uday Kiran, Pradeep Pallikila, José María Luna, Philippe Fournier-Viger, Masashi Toyoda, P. Krishna Reddy: Discovering Relative High Utility Itemsets in Very Large Transactional Databases Using Null-Invariant Measure. IEEE BigData 2021: 252-262 (yet to be ranked; A*/A approx.)
74 Palla Likhitha, Pamalla Veena, R. Uday Kiran, Yutaka Watanobe, Koji Zettsu:Discovering Maximal Partial Periodic Patterns in Very Large Temporal Databases. IEEE BigData 2021: 1460-1469 (yet to be ranked; A*/A approx.)
73 Tuan-Vinh La, Minh-Son Dao, Kazuki Tejima, Rage Uday Kiran, Koji Zettsu:Improving the Awareness of Sustainable Smart Cities by Analyzing Lifelog Images and IoT Air Pollution Data. IEEE BigData 2021: 3589-3594 (yet to be ranked; A*/A approx.)
72 Pradeep Pallikila, Pamalla Veena, R. Uday Kiran, Ram Avatar, Sadanori Ito, Koji Zettsu, P. Krishna Reddy: Discovering Top-k Spatial High Utility Itemsets in Very Large Quantitative Spatiotemporal databases. IEEE BigData 2021: 4925-4935 (yet to be ranked; A*/A approx.)
71 So Nakamura, R. Uday Kiran, Palla Likhitha, Penugonda Ravikumar, Yutaka Watanobe, Minh-Son Dao, Koji Zettsu, Masashi Toyoda:Efficient Discovery of Partial Periodic-Frequent Patterns in Temporal Databases. DEXA (1) 2021: 221-227 B
70 Penugonda Ravikumar, R. Uday Kiran, Narendra Babu Unnam, Yutaka Watanobe, Kazuo Goda, V. Susheela Devi, P. Krishna Reddy:A Novel Parameter-Free Energy Efficient Fuzzy Nearest Neighbor Classifier for Time Series Data. FUZZ-IEEE 2021: 1-6 A
69 Pamalla Veena, Sai Chithra Bommisetty, R. Uday Kiran, Sonali Agarwal, Koji Zettsu:Discovering Fuzzy Frequent Spatial Patterns in Large Quantitative Spatiotemporal databases. FUZZ-IEEE 2021: 1-8 A
68 R. Uday Kiran, Palla Likhitha, Minh-Son Dao, Koji Zettsu, Ji Zhang:Discovering Periodic-Frequent Patterns in Uncertain Temporal Databases. ICONIP (5) 2021: 710-718 A
67 Md. Mostafizer Rahman, Yutaka Watanobe, Rage Uday Kiran, Keita Nakamura:A Novel Rule-Based Online Judge Recommender System to Promote Computer Programming Education. IEA/AIE (2) 2021: 15-27 B
66 Penugonda Ravikumar, Likhitha Palla, Rage Uday Kiran, Yutaka Watanobe, Koji Zettsu:Towards Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases. IEA/AIE (1) 2021: 28-40 B
65 Sai Chithra Bommisetty, Penugonda Ravikumar, Rage Uday Kiran, Minh-Son Dao, Koji Zettsu:Discovering Spatial High Utility Itemsets in High-Dimensional Spatiotemporal Databases. IEA/AIE (1) 2021: 53-65 B
64 Yutaka Watanobe, Md. Mostafizer Rahman, Rage Uday Kiran, Penugonda Ravikumar:Online Automatic Assessment System for Program Code: Architecture and Experiences. IEA/AIE (2) 2021: 272-283 B
63 Minh-Son Dao, Koji Zettsu, Rage Uday Kiran:IMAGE-2-AQI: Aware of the Surrounding Air Qualification by a Few Images. IEA/AIE (2) 2021: 335-346 B
62 Mohit Dandekar, Narinder Singh Punn, Sanjay Kumar Sonbhadra, Sonali Agarwal, Rage Uday Kiran:Fruit classification using deep feature maps in the presence of deceptive similar classes. IJCNN 2021: 1-6 A
61 Pamalla Veena, So Nakamura, Palla Likhitha, R. Uday Kiran, Yutaka Watanobe, Koji Zettsu:A Unified Framework to Discover Partial Periodic-Frequent Patterns in Row and Columnar Temporal Databases. ICDM (Workshops) 2021: 607-614 -
60 R. Uday Kiran:Discovering Knowledge Hidden in Raster Images using RasterMiner. ICDAR@ICMR 2021: 1 -
59 Zhen Wang, Ji Zhang, Yizheng Chen, Chenhao Lu, Jerry Chun-Wei Lin, Jing Xiao, Rage Uday Kiran:Learning Probabilistic Latent Structure for Outlier Detection from Multi-view Data. PAKDD (1) 2021: 53-65. A
58 Md. Mostafizer Rahman, Yutaka Watanobe, Rage Uday Kiran, Raihan Kabir:A Stacked Bidirectional LSTM Model for Classifying Source Codes Built in MPLs. PKDD/ECML Workshops (2) 2021: 75-89 -
57 Md. Mostafizer Rahman, Yutaka Watanobe, Rage Uday Kiran, Truong Cong Thang, Incheon Paik:Challenges and Exit Strategies for Adapting Interactive Online Education Amid the Pandemic and its Aftermath. TALE 2021: 595-602 B
56 Minh-Son Dao, Ngoc-Thanh Nguyen, R. Uday Kiran, Koji Zettsu: Fusion-3DCNN-max3P: A dynamic system for discovering patterns of predicted congestion. IEEE BigData 2020: 910-915. (yet to be ranked; A*/A approx.)
55 Palla Likhitha, Penugonda Ravikumar, R. Uday Kiran, Yuto Hayamizu, Kazuo Goda, Masashi Toyoda, Koji Zettsu, Sourabh Shrivastava:Discovering Closed Periodic-Frequent Patterns in Very Large Temporal Databases. IEEE BigData 2020: 4700-4709 (yet to be ranked; A*/A approx.)
54 R. Uday Kiran, Sadanori Ito, Minh-Son Dao, Koji Zettsu, Cheng-Wei Wu, Yutaka Watanobe, Incheon Paik, Truong Cong Thang:Distributed Mining of Spatial High Utility Itemsets in Very Large Spatiotemporal Databases using Spark In-Memory Computing Architecture. IEEE BigData 2020: 4724-4733 (yet to be ranked; A*/A approx.)
53 R. Uday Kiran, Yutaka Watanobe, Bhaskar Chaudhury, Koji Zettsu, Masashi Toyoda, Masaru Kitsuregawa:Discovering Maximal Periodic-Frequent Patterns in Very Large Temporal Databases. DSAA 2020: 11-20 (yet to be ranked; A*/A approx.)
52 R.Uday Kiran, C.Saideep, Penugonda Ravikumar, Koji Zettsu, Masashi Toyoda, Masaru Kitsuregawa, P. Krishna Reddy:Discovering Fuzzy Periodic-Frequent Patterns in Quantitative Temporal Databases. FUZZ-IEEE 2020: 1-8 A
51 R. R. Uday Kiran, Sourabh Shrivastava, Philippe Fournier-Viger, Koji Zettsu, Masashi Toyoda, Masaru Kitsuregawa:Discovering Frequent Spatial Patterns in Very Large Spatiotemporal Databases. SIGSPATIAL/GIS 2020: 445-448 (yet to be ranked; A approx.)
50 Minh-Son Dao, Ngoc-Thanh Nguyen, R. Uday Kiran, Koji Zettsu: Insights From Urban Sensing Data: From Chaos to Predicted Congestion Patterns. ICDM (Workshops) 2020: 661-668 A
49 C. Saideep, R. Uday Kiran, Koji Zettsu, Cheng-Wei Wu, P. Krishna Reddy, Masashi Toyoda, Masaru Kitsuregawa: Parallel Mining of Partial Periodic Itemsets in Big Data. IEA/AIE 2020: 807-819 B
48 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 (yet to be ranked; B approx.)
47 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 (yet to be ranked; B approx.)
46 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 (yet to be ranked; A*/A approx.)
45 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 B
44 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 -
43 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 -
42 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 B
41 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 A
40 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 A
39 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 (yet to be ranked; A*/A approx.)
38 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 B
37 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 B
36 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 A*
35 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 B
34 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 A
33 R. Uday Kiran, Haichuan Shang, Masashi Toyoda, Masaru Kitsuregawa: Discovering Partial Periodic Itemsets in Temporal Databases. SSDBM 2017: 30:1-30:6 A
32 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 B
31 Alampally Anirudh, R. Uday Kiran, P. Krishna Reddy, Masaru Kitsuregawa: Memory efficient mining of periodic-frequent patterns in transactional databases. SSCI 2016: 1-8 2015 C
30 R. Uday Kiran, Masaru Kitsuregawa: Finding Periodic Patterns in Big Data. BDA 2015: 121-133 (yet to be ranked; B approx.)
29 Haichuan Shang, Xiang Zhao, R. Uday Kiran, Masaru Kitsuregawa: Towards Scale-out Capability on Social Graphs. CIKM 2015: 253-262 A
28 R. Uday Kiran, Masaru Kitsuregawa: Discovering Chronic-Frequent Patterns in Transactional Databases. DNIS 2015: 12-26 -
27 R. Uday Kiran, Haichuan Shang, Masashi Toyoda, Masaru Kitsuregawa: Discovering Recurring Patterns in Time Series. EDBT 2015: 97-108 A
26 R. Uday Kiran, Masaru Kitsuregawa: Novel Techniques to Reduce Search Space in Periodic-Frequent Pattern Mining. DASFAA (2) 2014: 377-391 A
25 R. Uday Kiran, Masaru Kitsuregawa: An Improved Neighborhood-Restricted Association Rule-based Recommender System. ADC 2013: 43-50 B
24 R. Uday Kiran, Masaru Kitsuregawa: Discovering Quasi-Periodic-Frequent Patterns in Transactional Databases. BDA 2013: 97-115 -
23 R. Uday Kiran, Masaru Kitsuregawa: Towards Addressing the Coverage Problem in Association Rule-Based Recommender Systems. DEXA (2) 2013: 418-425 B
22 R. Uday Kiran, Masaru Kitsuregawa: Mining Correlated Patterns with Multiple Minimum All-Confidence Thresholds. PAKDD Workshops 2013: 295-306 -
21 R. Uday Kiran, Masashi Toyoda, Masaru Kitsuregawa: Towards efficient discovery of coverage patterns in transactional databases. SSDBM 2013: 38:1-38:4 A
20 R. Uday Kiran, Masaru Kitsuregawa: Towards Efficient Discovery of Frequent Patterns with Relative Support. COMAD 2012: 92-99 B
19 M. Venu, R. Uday Kiran, R. Kiranmai: A robust neural network classifier to model the compressive strength of high performance concrete using feature subset selection. ACM COMPUTE 2012: 1 -
18 R. Uday Kiran, Masaru Kitsuregawa: Efficient Discovery of Correlated Patterns in Transactional Databases Using Items' Support Intervals. DEXA (1) 2012: 234-248 B
17 P. Gowtham Srinivas, P. Krishna Reddy, Bhargav Sripada, R. Uday Kiran, D. Satheesh Kumar: Discovering Coverage Patterns for Banner Advertisement Placement. PAKDD (2) 2012: 133-144 A
16 Somya Srivastava, R. Uday Kiran, P. Krishna Reddy: Discovering Diverse-Frequent Patterns in Transactional Databases. COMAD 2011: 69-78 B
15 R. Uday Kiran, P. Krishna Reddy: An Alternative Interestingness Measure for Mining Periodic-Frequent Patterns. DASFAA (1) 2011: 183-192 A
14 Mohak Sharma, P. Krishna Reddy, R. Uday Kiran, Thirumalaisamy Ragunathan: Improving the Performance of Recommender System by Exploiting the Categories of Products. DNIS 2011: 137-146 -
13 R. Uday Kiran, P. Krishna Reddy: Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms. EDBT 2011: 11-20 A
12 Akshat Surana, R. Uday Kiran, P. Krishna Reddy: An Efficient Approach to Mine Periodic-Frequent Patterns in Transactional Databases. PAKDD Workshops 2011: 254-266 -
11 Bhargav Sripada, Polepalli Krishna Reddy, Rage Uday Kiran: Coverage patterns for efficient banner advertisement placement. WWW (Companion Volume) 2011: 131-132 A*
10 R. Uday Kiran, Polepalli Krishna Reddy: An Efficient Approach to Mine Rare Association Rules Using Maximum Items' Support Constraints. BNCOD 2010: 84-95 C
9 Akshat Surana, R. Uday Kiran, P. Krishna Reddy: Selecting a Right Interestingness Measure for Rare Association Rules. COMAD 2010: 115 B
8 R. Uday Kiran, P. Krishna Reddy: Mining periodic-frequent patterns with maximum items' support constraints. Bangalore Compute Conf. 2010: 1:1-1:8 -
7 R. Uday Kiran, P. Krishna Reddy: Mining Rare Association Rules in the Datasets with Widely Varying Items' Frequencies. DASFAA (1) 2010: 49-62 A
6 R. Uday Kiran, P. Krishna Reddy: Towards Efficient Mining of Periodic-Frequent Patterns in Transactional Databases. DEXA (2) 2010: 194-208 B
5 Mittapally Kumara Swamy, P. Krishna Reddy, R. Uday Kiran, M. Venugopal Reddy: Interface Tailoring by Exploiting Temporality of Attributes for Small Screens. DNIS 2010: 284-295 -
4 R. Uday Kiran, P. Krishna Reddy: An improved multiple minimum support based approach to mine rare association rules. CIDM 2009: 340-347 B
3 R. Uday Kiran, P. Krishna Reddy: Mining Rare Periodic-Frequent Patterns Using Multiple Minimum Supports. COMAD 2009 B
2 R. Uday Kiran, P. Krishna Reddy: An Improved Frequent Pattern-growth Approach to Discover Rare Association Rules. KDIR 2009: 43-52 -
1 R. Uday Kiran, P. Krishna Reddy: Understanding the Dynamics of Crop Problems by Analyzing Farm Advisory Data in eSagu TM . DNIS 2007: 272-284 -

Note: (i) Citation count was last updated on 2-Feb-2020. (ii) "SJR" stands for "Scientific Journal Rankings". A click on each rank will direct you to the respective SJR page.

S.No. Publication Citation count Impact factor SJR
27 P. Veena, R. Uday Kiran, P. Ravikumar, P. Likhitha, Yuto Hayamizu, Kazuo Goda, Masashi Toyoda, Koji Zettsu, Sourabh Shrivastava: A Fundamental Approach to Discover Closed Periodic-Frequent Patterns in Very Large Temporal Databases. To be appeared in the Journal of Applied Intelligence 2023 NA 5.524 SCImago Journal & Country Rank
26 Hong N. Dao, Penugonda Ravikumar, Palla Likhitha, Rage Uday Kiran, Yutaka Watanobe, Incheon Paik: Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases. IEEE Access 11: 12504-12524 (2023) NA 5.524 SCImago Journal & Country Rank
25 Phuc-Thinh Nguyen, Minh-Son Dao, Michael A. Riegler, Rage Uday Kiran, Thai-Thinh Dang, Duy-Dong Le, Kieu-Chinh Nguyen-Ly, Thanh-Qui Pham, Van-Luong Nguyen: Training Performance Indications for Amateur Athletes Based on Nutrition and Activity Lifelogs. Algorithms 16(1): 30 (2023) NA 5.524 SCImago Journal & Country Rank
24 Rage Uday Kiran, Pamalla Veena, Penugonda Ravikumar, Bathala Venus Vikranth Raj, Minh-Son Dao, Koji Zettsu, Sai Chithra Bommisetti: HDSHUI-miner: a novel algorithm for discovering spatial high-utility itemsets in high-dimensional spatiotemporal databases Appl. Intell. 53(8): 8536-8561 (2023) NA 5.524 SCImago Journal & Country Rank
23 José María Luna, Rage Uday Kiran, Philippe Fournier-Viger, Sebastián Ventura:Efficient mining of top-k high utility itemsets through genetic algorithms. Inf. Sci. 624: 529-553 (2023) NA 5.524 SCImago Journal & Country Rank
22 Efficient Discovery of Partial Periodic Patterns in Large Temporal Databases. Kiran, R.U., Veena, P., Ravikumar, P., ...Kitsuregawa, M., Reddy, P.K. Electronics (Switzerland), 2022, 11(10), 1523 NA 5.524 SCImago Journal & Country Rank
21 Estimation of the Standardized Precipitation Evapotranspiration Index (SPEI) Using a Multilayer Perceptron Artificial Neural Network Model for Central India Shrivastava, S., Kiran, R.U., Bal, P.K., Singh, K.K.Pure and Applied Geophysics, 2022, 179(4), pp. 1461–1473 NA 5.524 SCImago Journal & Country Rank
20 Md. Mostafizer Rahman, Yutaka Watanobe, Taku Matsumoto, Rage Uday Kiran, Keita Nakamura:Educational Data Mining to Support Programming Learning Using Problem-Solving Data. IEEE Access 10: 26186-26202 (2022) NA 5.524 SCImago Journal & Country Rank
19 Philippe Fournier-Viger, Ying Wang, Peng Yang, Jerry Chun-Wei Lin, Unil Yun, Rage Uday Kiran:TSPIN: mining top-k stable periodic patterns. Appl. Intell. 52(6): 6917-6938 (2022) NA 5.524 SCImago Journal & Country Rank
18 Yutaka Watanobe, Md. Mostafizer Rahman, Taku Matsumoto, Rage Uday Kiran, Penugonda Ravikumar:Online Judge System: Requirements, Architecture, and Experiences. Int. J. Softw. Eng. Knowl. Eng. 32(6): 917-946 (2022) NA 5.524 SCImago Journal & Country Rank
18 Efficient discovery of periodic-frequent patterns in columnar temporal databases. Ravikumar, P., Likhitha, P., Raj, B.V.V., ...Watanobe, Y., Zettsu, K. Electronics (Switzerland), 2021, 10(12), 1478 NA 5.524 SCImago Journal & Country Rank
17 Md. Mostafizer Rahman, Yutaka Watanobe, Rage Uday Kiran, Truong Cong Thang, Incheon Paik: Impact of Practical Skills on Academic Performance: A Data-Driven Analysis. IEEE Access 9: 139975-139993 (2021) NA 5.524 SCImago Journal & Country Rank
16 Philippe Fournier-Viger, Peng Yang, Rage Uday Kiran, Sebastián Ventura, José María Luna: Mining local periodic patterns in a discrete sequence. Inf. Sci. 544: 519-548 (2021) [EI] NA 5.524 SCImago Journal & Country Rank
15 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) [EI] NA 4.098 SCImago Journal & Country Rank
14 Philippe Fournier-Viger, Peng Yang, Zhitian Li, Jerry Chun-Wei Lin, Rage Uday Kiran: Discovering rare correlated periodic patterns in multiple sequences. Data Knowl. Eng. 126: 101733 (2020) [EI] NA 1.5 SCImago Journal & Country Rank
13 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) [EI] NA 5.921 SCImago Journal & Country Rank
12 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) [EI] NA 5.524 SCImago Journal & Country Rank
11 R. Uday Kiran, Alampally Anirudh, Chennupati Saideep, Masashi Toyoda, P. Krishna Reddy, Masaru Kitsuregawa: Finding Periodic-Frequent Patterns in Temporal Databases using Periodic Summaries. Data Science and Pattern Recognition, Vol. 3, No. 2, pp. 24-46 (2019) NA 7.69 -
10 P Fournier-Viger, Peng Yang, Zhitian Li, JCW Lin, R. Uday Kiran: Discovering Rare Correlated Periodic Patterns in Multiple Sequences. To be appeared in the journal of Data and Knowledge Engineering NA 1.467 SCImago Journal & Country Rank
9 P Fournier-Viger, JCW Lin, R. Uday Kiran, YS Koh, R Thomas: A survey of sequential pattern mining. Data Science and Pattern Recognition 1 (1), 54-77 (2018) 102 7.69 -
8 J. N. Venkatesh, R. Uday Kiran, P. Krishna Reddy, Masaru Kitsuregawa:Discovering Periodic-Correlated Patterns in Temporal Databases. T. Large-Scale Data- and Knowledge-Centered Systems 38: 146-172 (2018) [EI] 1 NA -
7 R. Uday Kiran, J. N. Venkatesh, Masashi Toyoda, Masaru Kitsuregawa, P. Krishna Reddy:Discovering partial periodic-frequent patterns in a transactional database. Journal of Systems and Software 125: 170-182 (2017) [EI] 9 2.28 SCImago Journal & Country Rank
6 R. Uday Kiran, Masaru Kitsuregawa, P. Krishna Reddy:Efficient discovery of periodic-frequent patterns in very large databases. Journal of Systems and Software 112: 110-121 (2016) [EI] 22 2.28 SCImago Journal & Country Rank
5 R. Uday Kiran, Masaru Kitsuregawa: Efficient discovery of correlated patterns using multiple minimum all-confidence thresholds. J. Intell. Inf. Syst. 45(3): 357-377 (2015) [EI] 5 1.107 SCImago Journal & Country Rank
4 P. Gowtham Srinivas, P. Krishna Reddy, A. V. Trinath, Bhargav Sripada, R. Uday Kiran(:Mining coverage patterns from transactional databases. J. Intell. Inf. Syst. 45(3): 423-439 (2015) [EI] 13 1.107 SCImago Journal & Country Rank
3 Wei Guo, R. Uday Kiran, and Sesishi Ninomiya: Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model, Journal of Computers and Electronics in Agriculture, Vol. 96, pp. 58-66, 2013. [EI] 96 2.427 SCImago Journal & Country Rank
2 M.Kumara Swamy, P. Krishna Reddy, R. Uday Kiran, and M. Venugopal Reddy: Temporality-based User Interface Design Approaches for Desktop and Small Screens. Appeared in International Journal of Computational Science and Engineering (IJCSE), Vol.7, No.1, 2012. [EI] 5 NA -
1 R. Uday Kiran, P. Krishna Reddy, Mittapally Kumara Swamy, G. Syamasundar Reddy:Analysing dynamics of crop problems by applying text analysis methods on farm advisory data of eSaguTM. IJCSE 5(2): 154-164 (2010) [EI] 3 NA -
S. No. Publication details
1 R. Uday Kiran, Philippe Fournier-Viger, Jose M. Luna, Jerry Chun-Wei Lin, Anirban Mondal: Periodic Pattern Mining Theory, Algorithms, and Applications. Springer. Link
2 Arnab Bhattacharya, Janice Lee, Mong Li, Divyakant Agrawal, P. Krishna Reddy, Mukesh K. Mohania, Anirban Mondal, Vikram Goyal, Rage Uday Kiran: Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Virtual Event, April 11-14, 2022, roceedings, Part I. Lecture Notes in Computer Science 13245, Springer 2022, ISBN 978-3-031-00122-2. Link
3 Arnab Bhattacharya, Janice Lee, Mong Li, Divyakant Agrawal, P. Krishna Reddy, Mukesh K. Mohania, Anirban Mondal, Vikram Goyal, Rage Uday Kiran: Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Virtual Event, April 11-14, 2022, Proceedings, Part II. Lecture Notes in Computer Science 13246, Springer 2022, ISBN 978-3-031-00125-3. Link
4 Arnab Bhattacharya, Janice Lee, Mong Li, Divyakant Agrawal, P. Krishna Reddy, Mukesh K. Mohania, Anirban Mondal, Vikram Goyal, Rage Uday Kiran: Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Virtual Event, April 11-14, 2022, Proceedings, Part III. Lecture Notes in Computer Science 13247, Springer 2022, ISBN 978-3-031-00128-4. Link
5 Rage Uday Kiran, Vikram Goyal, P. Krishna Reddy: Database Systems for Advanced Applications. DASFAA 2022 International Workshops - BDMS, BDQM, GDMA, IWBT, MAQTDS, and PMBD, Virtual Event, April 11-14, 2022, Proceedings. Lecture Notes in Computer Science 13248, Springer 2022, ISBN 978-3-031-11216-4. Link
Patent Application Title of Invention
2021-047837

Periodic neighborhood pattern detector, periodic neighborhood pattern detection program, and periodic neighborhood pattern detection method.

周期的近傍パターン検出装置、周期的近傍パターン検出プログラム 及び周期的近傍パターン検出方法



Introduction: Customers purchase items in a supermarket (see Fig. 1a). This purchase data represents big sales data (see Fig. 1b). Useful information that can facilitate the supermarket owners to gain competitive advantage lies hidden in the sales data. Our research investigates novel pattern mining techniques (see Fig. 1c) to uncover customers' purchase patterns (see Fig. 1d) in sales data. The generated information can be beneficial to the managers for various purposes, such as product placements, inventory management, and introduction of new schemes.

Research: Our research aims to discover various types of patterns hidden in sales data. Some of the interesting patterns that we try to discover in sales data are as follows:
(i) Frequent patterns: itemsets that were frequently purchased by the customers
(ii) Rare patterns: Expensive items that were infrequently purchased by the customers
(iii) Periodic-Frequent patterns: itemsets that were regularly purchased by the customers
(iv) )High utility patterns: itemsets that have generated high revenue for the manager

Real-world application: We have applied our models on the Yahoo! Japan retail data and discovered useful information.
(i) We have discovered that many customers were simultaneously purchasing games of Nintendo3Ds and PlayStation. This information is interesting because it disproves the general assumption that people who purchase Nintendo3Ds games may not purchase Playstation games at the same time.
(ii) Many people were purchasing the items, WhitewhineSet and RedWhineSet, in the morning.

Publications and Patents:
(i) 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
(ii) R. Uday Kiran, Pradeep Pallikila, José María Luna, Philippe Fournier-Viger, Masashi Toyoda, P. Krishna Reddy: Discovering Relative High Utility Itemsets in Very Large Transactional Databases Using Null-Invariant Measure. IEEE BigData 2021: 252-262

Introduction: Disasters, such as earthquakes and typhoons, are very common in Japan. Recommending proper traffic routes by identifying future congestion patterns is a crucial task to save human life and enhance drivers' experience in autonomous driving. Our research (see Figure 1) primarily aims to develop an explainable AI model to predict future traffic congestion patterns in transportation networks.

Research: Our research aims to discover different types of interesting patterns found in congestion data:
(i) Development of explainable traffic congestion prediction system.
(ii) Discovering the neighboring road segements in which people have regularly faced traffic congestion.
(iii) Finding high congested road segements.
(iv) Designing a data warehouse to store large scale traffic congestion data.

Publications and Patents:
(i) R. Uday Kiran, Sourabh Shrivastava, Philippe Fournier-Viger, Koji Zettsu, Masashi Toyoda, Masaru Kitsuregawa: Discovering Frequent Spatial Patterns in Very Large Spatiotemporal Databases. SIGSPATIAL/GIS 2020: 445-448
(ii) R. Uday Kiran, Sadanori Ito, Minh-Son Dao, Koji Zettsu, Cheng-Wei Wu, Yutaka Watanobe, Incheon Paik, Truong Cong Thang: Distributed Mining of Spatial High Utility Itemsets in Very Large Spatiotemporal Databases using Spark In-Memory Computing Architecture. IEEE BigData 2020: 4724-4733
(iii) R. Uday Kiran, Palla Likhitha, Minh-Son Dao, Koji Zettsu, Ji Zhang: Discovering Periodic-Frequent Patterns in Uncertain Temporal Databases. ICONIP (5) 2021: 710-718

Note: This is a joint collaborative work with NICT X-Data platform.

Introduction: Air pollution is a major cause of cardio-respiratory problems for people living in Japan. To tackle this problem, the Japanese Ministry of Environment has set up the Atmospheric Environmental Regional Observation System (AEROS) constituting of several air pollution measuring sensors (or stations) positioned throughout the Japan. The spatial locations of these sensors is shown in Fig. 1a. The data generated by this sensor network is shown in Fig. 1b. Useful information that can facilitate the ecologists to come up with location-specific pollution control policies lies hidden in this data. Our research aims to transform this big data into a spatiotemporal database (see Fig. 1c) using ETL techniques, apply pattern mining techniques (see Fig. 1c), discover the high polluted areas (see Fig. 1d), and visualize them (see Fig. 1e).

Publications and Patents:
(i) Pamalla Veena, Penugonda Ravikumar, Kundai Kwangwari, R. Uday Kiran, Kazuo Goda, Yutaka Watanobe, Koji Zettsu: Discovering Fuzzy Geo-referenced Periodic-Frequent Patterns in Geo-referenced Time Series Databases. FUZZ-IEEE 2022: 1-8
(ii) Pamalla Veena, Sai Chithra Bommisetty, R. Uday Kiran, Sonali Agarwal, Koji Zettsu: Discovering Fuzzy Frequent Spatial Patterns in Large Quantitative Spatiotemporal databases. FUZZ-IEEE 2021: 1-8
(iii) R. Uday Kiran, Palla Likhitha, Minh-Son Dao, Koji Zettsu, Ji Zhang: Discovering Periodic-Frequent Patterns in Uncertain Temporal Databases. ICONIP (5) 2021: 710-718

Grants


Year Grant Name, Project title, Amount, and Duration Role
2022 Name:Open Call for collaboration researches between UoA(ARC-Space) and external Univ./agencies
Title:GeoPatternMiner: Discovering Knowledge Hidden in SpatioTemporal Databases
Amount: ~400,000 yen
Duration: 1 year
PI
Name:UoA Competitive Research Funding
Title: -
Amount: ~2,000,000 yen
Duration: 1 year
PI
Name:UoA Competitive Research Funding
Title:-
Amount: ~2,000,000 yen
Duration: 1 year
Co-PI
Name:UoA Competitive Research Funding
Title:
Amount: ~1,500,000 yen
Duration: 1 year
PI
2021 Name:Open Call for collaboration researches between UoA(ARC-Space) and external Univ./agencies
Title:GeoPatternMiner: Discovering Knowledge Hidden in SpatioTemporal Databases
Amount: ~200,000 yen
Duration: 1 year
PI
Name:UoA Competitive Research Funding
Title: -
Amount: ~2,000,000 yen
Duration: 1 year
PI
Name:UoA Competitive Research Funding
Title:-
Amount: ~2,000,000 yen
Duration: 1 year
Co-PI
Name:UoA Competitive Research Funding
Title: Designing a Hadoop-based Fault-tolerant and Scalable Big Data Warehouse to Store and Process Fukushima Traffic Congestion Data
Amount: ~1,308,000 yen
Duration: 1 year
PI
Name:Kakenhi-C grant
Title: Exploring Novel Mathematical Models and Efficient Algorithms to Discover Periodic Spatial Patterns in Irregular SpatioTemporal Big Data
Amount: ~4,000,000 yen
Duration: 4 Years
PI
2020 Name: UoA Competitive Research Funding
Title: Exploring Novel Models and High-Performance Algorithms to Discover Periodic Patterns from Temporal Big Data
Amount: ~1,300,000 yen
Duration: 1 Year
PI
Name: UoA Competitive Research Funding
Title: SMART Cities
Amount: ~2,000,000 yen
Duration: 1 Year
Co-PI
Name: UoA Competitive Research Funding
Title: End-Spectral Generator
Amount: ~2,000,000 yen
Duration: 1 Year
Co-PI