Demo Data from VLDB 2026 / reviewers in the wild / expert
Jade Chien
dblp:167/8647
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1984
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 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 |
Network performance modeling · 56% Network measurement and analytics · 44% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network measurement and analytics › traffic characterization
bursty traffic |
0.0 | 1 | 1984 | Bursty Traffic Local Network Modeling · IEEE J. Sel. Areas Commun. 1984 |
Network performance modeling › network modeling
local area network modeling |
0.0 | 1 | 1984 | Bursty Traffic Local Network Modeling · IEEE J. Sel. Areas Commun. 1984 |
Methods — techniques the papers use, named apart from their topics
simulation · 0.0bulk poisson process · 0.0analytical approximation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1984 | Bursty Traffic Local Network ModelingabstractA computer program to predict the performance of local area networks based on an Ethernet or other medium is described. The effects of bursty traffic are modeled as packet streams which are initially bulk Poisson, but whose clusters spread apart as they propagate through the network. It is shown by example that performance predictions using more conventional Poisson assumptions tend to be optimistic, often extremely so. The program presents a friendly, dialogue-oriented user interface, including a library of rather sophisticated transaction types for network loading. A simulator is included in the program to validate its analytical approximations, and two example simulations are presented. Although approximation errors can be large in highly bursty situations, these errors appear to be much less than those which would be obtained by treating bursty traffic as if it were Poisson. Jeffrey R. Spirn, Jade Chien, William Hawe |
IEEE J. Sel. Areas Commun. | 2 |