Jorge Augusto Meira
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| Fakultät oder Zentrum | Interdisciplinary Centre for Security, Reliability and Trust | ||||
| Department | SEDAN | ||||
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Université du Luxembourg 29, avenue JF Kennedy L-1855 Luxembourg |
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| Büroadresse | JFK Building, E02-213 | ||||
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Jorge Augusto MEIRA received his PhD degree in Computer Science at the University of Luxembourg – SnT, in 2014. He then also worked there as a Research Associate from 2016 to 2020. Over the last 15 years, Jorge Meira has gained professional experience in several areas of industry and in research projects in positions as a developer engineer software developer, system analyst, data scientist, project manager, principal investigator, and research consultant. His research interests are in big data and machine learning, more specifically in anomaly detection, anti-money laundering and recommendation systems. Jorge Augusto joined the Services and Data Management research group, SEDAN, headed by Prof. Radu State.
Last updated on: Montag, den 09. Januar 2023
2020
Mobile App to SGX Enclave Secure Channel; ; ;
in 2019 IEEE International Symposium on Software Reliability Engineering Workshops (2020, February 13)
Towards Privacy Preserving Data Centric Super App; ; ;
in Carvalho Ota, Fernando Kaway; Meira, Jorge Augusto; Frank, Raphaël; State, Radu (Eds.) 2020 Mediterranean Communication and Computer Networking Conference, Arona 17-19 June 2020 (2020, September 10)
2019
Database Processing-in-Memory: A Vision; ; ;
in Database Processing-in-Memory: A Vision (2019, August 03)
2018
An Elastic Multi-Core Allocation Mechanism for Database Systems; ; ;
in International Conference on Data Engineering (ICDE) (2018)
Mind Your Dependencies for Semantic Query Optimization; ; ;
in Journal of Information and Data Management (2018)
2017
Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations; ; ; ; ; ;
in Proceedings of the 17th IEEE International Conference on Data Mining Workshops (ICDMW 2017) (2017)
The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study; ; ; ; ; ; ;
in Proceedings of the 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017) (2017)
Introduction to Detection of Non-Technical Losses using Data Analytics; ; ;
Scientific Conference (2017, September)
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey; ; ; ;
in International Journal of Computational Intelligence Systems (2017), 10(1), 760-775
Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?; ; ; ; ;
in Proceedings of the 19th International Conference on Intelligent System Applications to Power Systems (ISAP 2017) (2017, September)
Distilling Provider-Independent Data for General Detection of Non-Technical Losses; ; ; ; ; ;
in Power and Energy Conference, Illinois 23-24 February 2017 (2017)
2016
Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets; ; ; ; ; ;
in Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing Applications and Technologies (BDCAT 2016) (2016)
“Overloaded!” — A Model-based Approach to Database Stress Testing; ; ; ;
in International Conference on Database and Expert Systems Applications, Porto 5-8 September 2016 (2016)
2014
Generic Cloud Platform Multi-objective Optimization Leveraging Models@run.time; ; ; ; ;
Scientific Conference (2014, March)
2013
Under Pressure Benchmark for DDBMS Availability; ; ; ; ;
in Journal of Information and Data Management (2013)
Stress Testing of Transactional Database Systems; ; ; ;
in Journal of Information and Data Management (2013)
2012
Peer-to-Peer Load Testing; ; ;
in Software Testing, Verification and Validation (ICST), 2012 IEEE Fifth International Conference on (2012)
