Demo Data from EDBT 2026 / reviewers in the wild / expert
Naimul Basher
dblp:43/2347
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2008
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author
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 measurement and analytics · 75% Network performance modeling · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network measurement and analytics › traffic analysis
peer-to-peer traffic analysis |
0.1 | 1 | 2008 | A comparative analysis of web and peer-to-peer traffic · WWW 2008 |
Network measurement and analytics
traffic characterization |
0.1 | 1 | 2008 | A comparative analysis of web and peer-to-peer traffic · WWW 2008 |
Network measurement and analytics › traffic analysis
web traffic analysis |
0.1 | 1 | 2008 | A comparative analysis of web and peer-to-peer traffic · WWW 2008 |
Network performance modeling
workload modeling |
0.1 | 1 | 2008 | A comparative analysis of web and peer-to-peer traffic · WWW 2008 |
Methods — techniques the papers use, named apart from their topics
trace analysis · 0.1distribution fitting · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | A comparative analysis of web and peer-to-peer trafficabstractPeer-to-Peer (P2P) applications continue to grow in popularity, and have reportedly overtaken Web applications as the single largest contributor to Internet traffic. Using traces collected from a large edge network, we conduct an extensive analysis of P2P traffic, compare P2P traffic with Web traffic, and discuss the implications of increased P2P traffic. In addition to studying the aggregate P2P traffic, we also analyze and compare the two main constituents of P2P traffic in our data, namely BitTorrent and Gnutella. The results presented in the paper may be used for generating synthetic workloads, gaining insights into the functioning of P2P applications, and developing network management strategies. For example, our results suggest that new models are necessary for Internet traffic. As a first step, we present flow-level distributional models for Web and P2P traffic that may be used in network simulation and emulation experiments. Naimul Basher, Aniket Mahanti, Anirban Mahanti, Carey L. Williamson, Martin F. Arlitt |
WWW | 1 |