Can machine learning aid in delivering new use cases and scenarios in 5G? TS Buda, H Assem, L Xu, D Raz, U Margolin, E Rosensweig, DR Lopez, ... NOMS 2016-2016 IEEE/IFIP Network Operations and Management Symposium, 1279-1284, 2016 | 31 | 2016 |
CogNet: A network management architecture featuring cognitive capabilities L Xu, H Assem, IGB Yahia, TS Buda, A Martin, D Gallico, M Biancani, ... 2016 European Conference on Networks and Communications (EuCNC), 325-329, 2016 | 29 | 2016 |
Spatio-temporal clustering approach for detecting functional regions in cities H Assem, L Xu, TS Buda, D O'Sullivan 2016 IEEE 28th international conference on tools with artificial …, 2016 | 21 | 2016 |
Wireless ion-selective electrode autonomous sensing system C Fay, S Anastasova, C Slater, ST Buda, R Shepherd, B Corcoran, ... IEEE Sensors Journal 11 (10), 2374-2382, 2011 | 21 | 2011 |
View on 5g architecture S Redana, A Kaloxylos, A Galis, P Rost, V Jungnickel White paper of the 5G-PPP architecture WG, 2016 | 19 | 2016 |
Deepad: A generic framework based on deep learning for time series anomaly detection TS Buda, B Caglayan, H Assem Pacific-Asia Conference on Knowledge Discovery and Data Mining, 577-588, 2018 | 16 | 2018 |
ReX: Extrapolating relational data in a representative way TS Buda, T Cerqueus, J Murphy, M Kristiansen British International Conference on Databases, 95-107, 2015 | 15 | 2015 |
Machine learning as a service for enabling Internet of Things and People H Assem, L Xu, TS Buda, D O’Sullivan Personal and Ubiquitous Computing 20 (6), 899-914, 2016 | 11 | 2016 |
Towards a better replica management for hadoop distributed file system HE Ciritoglu, T Saber, TS Buda, J Murphy, C Thorpe 2018 IEEE International Congress on Big Data (BigData Congress), 104-111, 2018 | 10 | 2018 |
RCMC: Recognizing crowd-mobility patterns in cities based on location based social networks data H Assem, TS Buda, D O’sullivan ACM Transactions on Intelligent Systems and Technology (TIST) 8 (5), 1-30, 2017 | 9 | 2017 |
Investigation of replication factor for performance enhancement in the hadoop distributed file system HE Ciritoglu, L Batista de Almeida, E Cunha de Almeida, TS Buda, ... Companion of the 2018 ACM/SPEC International Conference on Performance …, 2018 | 8 | 2018 |
ADE: An ensemble approach for early Anomaly Detection TS Buda, H Assem, L Xu 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM …, 2017 | 6 | 2017 |
VFDS: An application to generate fast sample databases TS Buda, T Cerqueus, J Murphy, M Kristiansen Proceedings of the 23rd ACM International Conference on Conference on …, 2014 | 6 | 2014 |
VFDS: Very fast database sampling system TS Buda, T Cerqueus, J Murphy, M Kristiansen 2013 IEEE 14th International Conference on Information Reuse & Integration …, 2013 | 6 | 2013 |
ReX: Representative extrapolating relational databases TS Buda, T Cerqueus, C Grava, J Murphy Information Systems 67, 83-99, 2017 | 5 | 2017 |
CoDS: A representative sampling method for relational databases TS Buda, T Cerqueus, J Murphy, M Kristiansen International Conference on Database and Expert Systems Applications, 342-356, 2013 | 4 | 2013 |
Towards realistic sampling: generating dependencies in a relational database TS Buda, J Murphy, M Kristiansen Proceedings of the 7th International Conference on Ubiquitous Information …, 2013 | 4 | 2013 |
St-dennetfus: A new deep learning approach for network demand prediction H Assem, B Caglayan, TS Buda, D O’Sullivan Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018 | 3 | 2018 |
Detecting data anomalies HAAA Salama, TS Buda, PJ O'sullivan, L Xu US Patent App. 15/444,584, 2018 | 3 | 2018 |
A framework for enterprise social network assessment and weak ties recommendation F Ghaffar, TS Buda, H Assem, A Afsharinejad, N Hurley 2018 IEEE/ACM International Conference on Advances in Social Networks …, 2018 | 3 | 2018 |