Demo Data from EDBT 2026 / reviewers in the wild / expert
M. J. Taylor
dblp:58/5639
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2001
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 architecture, parallel and distributed computing, and storage systems
1 paper |
Distributed systems · 100% | |
| Artificial intelligence
1 paper |
Probabilistic and Bayesian machine learning · 100% | |
| Computer networks
1 paper |
Cellular and mobile networks · 100% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed systems › replication › replica consistency
replica synchronization |
0.0 | 1 | 2001 | Optimising Synchronisation Times for Mobile Devices · NIPS 2001 |
Distributed systems
replication |
0.0 | 1 | 2001 | Optimising Synchronisation Times for Mobile Devices · NIPS 2001 |
Methods — techniques the papers use, named apart from their topics
probabilistic modeling · 0.1objective function minimization · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | Optimising Synchronisation Times for Mobile DevicesabstractWith the increasing number of users of mobile computing devices (e.g. personal digital assistants) and the advent of third generation mobile phones, wireless communications are becoming increasingly important. Many applications rely on the device maintaining a replica of a data-structure which is stored on a server, for exam(cid:173) ple news databases, calendars and e-mail. ill this paper we explore the question of the optimal strategy for synchronising such replicas. We utilise probabilistic models to represent how the data-structures evolve and to model user behaviour. We then formulate objective functions which can be minimised with respect to the synchronisa(cid:173) tion timings. We demonstrate, using two real world data-sets, that a user can obtain more up-to-date information using our approach. Neil D. Lawrence, Antony I. T. Rowstron, Christopher M. Bishop, M. J. Taylor |
NIPS | 4 |