Članki z zahtevami za javni dostop - Matthew MalensekVeč o tem
Na voljo nekje: 17
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
Zahteve: US National Science Foundation
Analytic queries over geospatial time-series data using distributed hash tables
M Malensek, S Pallickara, S Pallickara
IEEE Transactions on Knowledge and Data Engineering 28 (6), 1408-1422, 2016
Zahteve: US National Science Foundation
Synopsis: A distributed sketch over voluminous spatiotemporal observational streams
T Buddhika, M Malensek, SL Pallickara, S Pallickara
IEEE Transactions on Knowledge and Data Engineering 29 (11), 2552-2566, 2017
Zahteve: US National Science Foundation
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
Zahteve: US National Science Foundation
Hermes: Federating fog and cloud domains to support query evaluations in continuous sensing environments
M Malensek, SL Pallickara, S Pallickara
IEEE Cloud Computing 4 (2), 54-62, 2017
Zahteve: US National Science Foundation
Minerva: proactive disk scheduling for QoS in multitier, multitenant cloud environments
M Malensek, S Pallickara, S Pallickara
IEEE Internet Computing 20 (3), 19-27, 2016
Zahteve: US National Science Foundation
Autonomous cloud federation for high-throughput queries over voluminous datasets
M Malensek, S Pallickara, S Pallickara
IEEE Cloud Computing 3 (3), 40-49, 2016
Zahteve: US National Science Foundation
Living on the edge: Data transmission, storage, and analytics in continuous sensing environments
T Buddhika, M Malensek, S Pallickara, SL Pallickara
ACM Transactions on Internet of Things 2 (3), 1-31, 2021
Zahteve: US National Science Foundation, US Department of Agriculture
Network analysis for identifying and characterizing disease outbreak influence from voluminous epidemiology data
N Shah, H Shah, M Malensek, SL Pallickara, S Pallickara
2016 IEEE International Conference on Big Data (Big Data), 1222-1231, 2016
Zahteve: US National Science Foundation
Enabling fast exploratory analyses over voluminous spatiotemporal data using analytical engines
D Rammer, T Buddhika, M Malensek, S Pallickara, SL Pallickara
IEEE Transactions on Big Data 8 (1), 213-228, 2019
Zahteve: US National Science Foundation
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
Zahteve: US National Science Foundation
Scalable network analytics for characterization of outbreak influence in voluminous epidemiology datasets
N Shah, M Malensek, H Shah, S Pallickara, SL Pallickara
Concurrency and Computation: Practice and Experience 31 (7), e4998, 2019
Zahteve: US National Science Foundation
Understanding professional identity development among computer science students
SN Rollins, A Joshi, X Apedoe, S Engle, M Malensek, G Bruno
2021 ASEE Virtual Annual Conference Content Access, 2021
Zahteve: US National Science Foundation
Engendering community to computer science freshmen through an early arrival program
A Joshi, G Bruno, X Apedoe, S Engle, S Rollins, M Malensek
2020 ASEE Virtual Annual Conference Content Access, 2020
Zahteve: US National Science Foundation
Exploring Computer Science Identity Development Among Undergraduate Computer Science Majors
X Apedoe, W Li, S Rollins, S Engle, A Joshi, M Malensek, C Brooks
Proceedings of the 17th International Conference of the Learning Sciences …, 2023
Zahteve: US National Science Foundation
Acknowledging Inequities in Tech through a Community-Engaged Learning Course
A Joshi, S Engle, M Malensek, C Brooks, X Apedoe, S Moore
Proceedings of the 54th ACM Technical Symposium on Computer Science …, 2022
Zahteve: US National Science Foundation
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
Zahteve: US National Science Foundation, US Department of Agriculture
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