EDBT 2026 Demo / reviewers in the wild / expert
Édler Lins de Albuquerque
dblp:150/6420
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0001-5982-5267ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Internet of things and sensor networks · 50% Network measurement and analytics · 50% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Energy-efficient computing · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining
dimensionality reduction |
0.2 | 1 | 2014 | MuSA: Multivariate Sampling Algorithmfor Wireless Sensor Networks · IEEE Trans. Computers 2014 |
Network measurement and analytics
sampling |
0.2 | 1 | 2014 | MuSA: Multivariate Sampling Algorithmfor Wireless Sensor Networks · IEEE Trans. Computers 2014 |
Internet of things and sensor networks
wireless sensor network |
0.2 | 1 | 2014 | MuSA: Multivariate Sampling Algorithmfor Wireless Sensor Networks · IEEE Trans. Computers 2014 |
Methods — techniques the papers use, named apart from their topics
data ranking · 0.6component analysis · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Self-organizing maps applied to the analysis and identification of characteristics related to air quality monitoring stations and its pollutants
Emanoel L. R. Costa, Taiane Braga, Leonardo Alves Dias, Édler Lins de Albuquerque, Marcelo A. C. Fernandes |
Neural Comput. Appl. | 4 |
| 2014 | MuSA: Multivariate Sampling Algorithmfor Wireless Sensor NetworksabstractA wireless sensor network can be used to collect and process environmental data, which is often of multivariate nature. This work proposes a multivariate sampling algorithm based on component analysis techniques in wireless sensor networks. To improve the sampling, the algorithm uses component analysis techniques to rank the data. Once ranked, the most representative data is retained. Simulation results show that our technique reduces the data keeping its representativeness. In addition, the energy consumption and delay to deliver the data on the network are reduced. André L. L. de Aquino, Orlando Silva Junior, Alejandro C. Frery, Édler Lins de Albuquerque, Raquel A. F. Mini |
IEEE Trans. Computers | 4 |