| Machine learning with big data: Challenges and approaches A L’heureux, K Grolinger, HF Elyamany, MAM Capretz Ieee Access 5, 7776-7797, 2017 | 1101 | 2017 |
| Mlaas: Machine learning as a service M Ribeiro, K Grolinger, MAM Capretz 2015 IEEE 14th international conference on machine learning and applications …, 2015 | 497 | 2015 |
| Data management in cloud environments: NoSQL and NewSQL data stores K Grolinger, WA Higashino, A Tiwari, MAM Capretz Journal of Cloud Computing: advances, systems and applications 2, 1-24, 2013 | 481 | 2013 |
| A systematic review of convolutional neural network-based structural condition assessment techniques S Sony, K Dunphy, A Sadhu, M Capretz Engineering Structures 226, 111347, 2021 | 329 | 2021 |
| An ensemble learning framework for anomaly detection in building energy consumption DB Araya, K Grolinger, HF ElYamany, MAM Capretz, G Bitsuamlak Energy and Buildings 144, 191-206, 2017 | 275 | 2017 |
| Challenges for mapreduce in big data K Grolinger, M Hayes, WA Higashino, A L'Heureux, DS Allison, ... 2014 IEEE world congress on services, 182-189, 2014 | 246 | 2014 |
| Energy forecasting for event venues: Big data and prediction accuracy K Grolinger, A L’Heureux, MAM Capretz, L Seewald Energy and buildings 112, 222-233, 2016 | 211 | 2016 |
| Transfer learning with seasonal and trend adjustment for cross-building energy forecasting M Ribeiro, K Grolinger, HF ElYamany, WA Higashino, MAM Capretz Energy and Buildings 165, 352-363, 2018 | 200 | 2018 |
| Contextual anomaly detection framework for big sensor data MA Hayes, MAM Capretz Journal of Big Data 2, 1-22, 2015 | 189 | 2015 |
| Online trust: Definition and principles ZM Aljazzaf, M Perry, MAM Capretz 2010 Fifth International Multi-conference on Computing in the Global …, 2010 | 133 | 2010 |
| Myifogsim: A simulator for virtual machine migration in fog computing MM Lopes, WA Higashino, MAM Capretz, LF Bittencourt Companion proceedings of the10th international conference on utility and …, 2017 | 122 | 2017 |
| Contextual anomaly detection in big sensor data MA Hayes, MAM Capretz 2014 IEEE International Congress on Big Data, 64-71, 2014 | 115 | 2014 |
| Forecasting residential energy consumption: Single household perspective XM Zhang, K Grolinger, MAM Capretz, L Seewald 2018 17th IEEE International Conference on Machine Learning and Applications …, 2018 | 99 | 2018 |
| An approach for SDN traffic monitoring based on big data techniques W Queiroz, MAM Capretz, M Dantas Journal of Network and Computer Applications 131, 28-39, 2019 | 91 | 2019 |
| A dependency impact analysis model for web services evolution S Wang, MAM Capretz 2009 IEEE International Conference on Web Services, 359-365, 2009 | 82 | 2009 |
| Storing massive Resource Description Framework (RDF) data: a survey Z Ma, MAM Capretz, L Yan The Knowledge Engineering Review 31 (4), 391-413, 2016 | 81 | 2016 |
| Collective contextual anomaly detection framework for smart buildings DB Araya, K Grolinger, HF ElYamany, MAM Capretz, G Bitsuamlak 2016 international joint conference on neural networks (IJCNN), 511-518, 2016 | 79 | 2016 |
| Toward smart-building digital twins: BIM and IoT data integration DD Eneyew, MAM Capretz, GT Bitsuamlak IEEE access 10, 130487-130506, 2022 | 75 | 2022 |
| Energy consumption prediction with big data: Balancing prediction accuracy and computational resources K Grolinger, MAM Capretz, L Seewald 2016 IEEE international congress on big data (BigData congress), 157-164, 2016 | 71 | 2016 |
| Transformer-based model for electrical load forecasting A L’Heureux, K Grolinger, MAM Capretz Energies 15 (14), 4993, 2022 | 70 | 2022 |
Katarina GrolingerWestern UniversityBestätigte E-Mail-Adresse bei uwo.ca
