EDBT 2026 Demo / reviewers in the wild / expert
Michael Dale
dblp:14/1875
· DBLP profile ↗
4ranked-venue papers
0as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2Computer networks · 2Graphics, computer vision, multimedia, augmented reality and games · 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
2 papers |
Network management and operations · 75% Network optimization and economics · 25% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Embedded and real-time systems · 50% Hardware accelerators and domain-specific architectures · 50% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network management and operations › fault management › fault diagnosis
fault localization |
0.0 | 1 | 1997 | Locating Faults in Tree-Structured Networks · IJCAI (1) 1997 |
Network management and operations
network control |
0.0 | 1 | 1997 | Experiments with Simple Neural Networks for Real-Time Control · IEEE J. Sel. Areas Commun. 1997 |
Network management and operations › fault management › fault diagnosis
network fault diagnosis |
0.0 | 1 | 1997 | Locating Faults in Tree-Structured Networks · IJCAI (1) 1997 |
Network optimization and economics
resource allocation |
0.0 | 1 | 1997 | Experiments with Simple Neural Networks for Real-Time Control · IEEE J. Sel. Areas Commun. 1997 |
Hardware accelerators and domain-specific architectures
neural network control |
0.0 | 1 | 1995 | Experiments with Neural Networks for Real Time Implementation of Control · NIPS 1995 |
Embedded and real-time systems
real-time control |
0.0 | 1 | 1995 | Experiments with Neural Networks for Real Time Implementation of Control · NIPS 1995 |
Methods — techniques the papers use, named apart from their topics
recurrent neural network · 0.0linear programming · 0.0greedy search heuristic · 0.0feedforward neural network · 0.0neural network · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Online Video Using BitTorrent and HTML5 Applied to WikipediaabstractWikipedia started a project in order to enable users to add video and audio on their Wiki pages. The technical downside of this is that its bandwidth requirements will increase manifold. BitTorrent-based peer-to-peer technology from P2P-Next (a European research project) is explored to handle this bandwidth surge. We discuss the impact on the BitTorrent piece picker and outline our ''tribe'' protocol for seamless integration of P2P into the HTML5 video and audio elements. Ongoing work on libswift which uses UDP, an enhanced transport protocol and integrated NAT/Firewall puncturing, is also described. Arno Bakker, Riccardo Petrocco, Michael Dale, Jan Gerber, Victor Grishchenko, Diego Rabaioli, Johan A. Pouwelse |
Peer-to-Peer Computing | 3 |
| 1997 | Locating Faults in Tree-Structured Networks
Christopher Leckie, Michael Dale |
IJCAI (1) | 2 |
| 1997 | Experiments with Simple Neural Networks for Real-Time ControlabstractWe demonstrate the practical ability of neural networks (NNs) trained in a supervised mode to extract useful control "knowledge" from a large, high-dimensional empirical database, and then to deliver almost optimal control in "real time". In particular, this paper describes experiments with NN-based controllers for allocating bandwidth capacity in a telecommunications network (SDH). This system was proposed in order to overcome a "real time" response constraint. Two basic architectures, each consisting of a combination of two methods, are evaluated: (1) a feedforward network-heuristic combination and (2) a feedforward network-recurrent network combination. These architectures are compared against a linear programming (LP) optimizer as a benchmark. This LP optimizer was also used as a teacher to label the data samples for the feedforward NN training algorithm. NN-based solutions are very accurate (/spl sim/98% of optimal throughput) and, in contrast to the algorithmic approach, can be delivered in "real time". It is found that while the "human" generated heuristics (greedy search optimization) fail to find a solution in approximately 30% of cases, the best NN fails only in 4.9% of cases. Moreover, it has been found that in spite of the very high dimensionality of the problem (55 inputs and 126 outputs), the solution can be delivered by surprisingly compact NNs, with as little as around 1000 synaptic weights. This proves that on this occasion the NNs were able to extract simple but powerful "heuristics" hidden in the complex sets of numerical data. Peter K. Campbell, Alan Christiansen, Michael Dale, Herman L. Ferrá, Adam Kowalczyk, Jacek Szymanski |
IEEE J. Sel. Areas Commun. | 3 |
| 1995 | Experiments with Neural Networks for Real Time Implementation of Control
Peter K. Campbell, Michael Dale, Herman L. Ferrá, Adam Kowalczyk |
NIPS | 2 |