| Predictive analytics using statistical, learning, and ensemble methods to support real-time exploration of discrete event simulations W Budgaga, M Malensek, S Pallickara, N Harvey, FJ Breidt, S Pallickara Future Generation Computer Systems 56, 360-374, 2016 | 60 | 2016 |
| A framework for scalable real‐time anomaly detection over voluminous, geospatial data streams W Budgaga, M Malensek, S Lee Pallickara, S Pallickara Concurrency and Computation: Practice and Experience 29 (12), e4106, 2017 | 23 | 2017 |
| Using distributed analytics to enable real-time exploration of discrete event simulations M Malensek, W Budgaga, S Pallickara, N Harvey, FJ Breidt, S Pallickara 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, 49-58, 2014 | 14 | 2014 |
| Alleviating i/o inefficiencies to enable effective model training over voluminous, high-dimensional datasets D Rammer, W Budgaga, T Buddhika, S Pallickara, SL Pallickara 2018 IEEE International Conference on Big Data (Big Data), 468-477, 2018 | 9 | 2018 |
| Trident: Distributed storage, analysis, and exploration of multidimensional phenomena M Malensek, W Budgaga, R Stern, S Pallickara, SL Pallickara IEEE Transactions on Big Data 5 (2), 252-265, 2018 | 9 | 2018 |
| A framework for real-time, autonomous anomaly detection over voluminous time-series geospatial data streams W Budgaga Colorado State University, 2014 | 2 | 2014 |
| Concerto: Leveraging Ensembles for Timely, Accurate Model Training Over Voluminous Datasets W Budgaga, M Malensek, SL Pallickara, S Pallickara 2020 IEEE/ACM International Conference on Big Data Computing, Applications …, 2020 | | 2020 |
| Leveraging ensembles: balancing timeliness and accuracy for model training over voluminous datasets W Budgaga Colorado State University, 2020 | | 2020 |
Sangmi Lee PallickaraProfessor of Computer Science, Colorado State UniversityVerified email at cs.colostate.edu
