Demo Data from EDBT 2026 / reviewers in the wild / expert
Naomi Novik
dblp:28/3398
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2
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
2 papers |
Performance modeling and evaluation · 85% Distributed systems · 15% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation
benchmarking |
0.1 | 2 | 2003 | Measuring thin-client performance using slow-motion benchmarking · ACM Trans. Comput. Syst. 2003 Measuring Thin-Client Performance Using Slow-Motion Benchmarking · USENIX ATC, General Track 2001 |
Distributed systems › distributed system architecture
thin-client computing |
0.0 | 1 | 2003 | Measuring thin-client performance using slow-motion benchmarking · ACM Trans. Comput. Syst. 2003 |
Methods — techniques the papers use, named apart from their topics
packet trace capture · 0.0performance measurement · 0.0benchmarking · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Measuring thin-client performance using slow-motion benchmarkingabstractModern thin-client systems are designed to provide the same graphical interfaces and applications available on traditional desktop computers while centralizing administration and allowing more efficient use of computing resources. Despite the rapidly increasing popularity of these client-server systems, there are few reliable analyses of their performance. Industry standard benchmark techniques commonly used for measuring desktop system performance are ill-suited for measuring the performance of thin-client systems because these benchmarks only measure application performance on the server, not the actual user-perceived performance on the client. To address this problem, we have developed slow-motion benchmarking, a new measurement technique for evaluating thin-client systems. In slow-motion benchmarking, performance is measured by capturing network packet traces between a thin client and its respective server during the execution of a slow-motion version of a conventional benchmark application. These results can then be used either independently or in conjunction with conventional benchmark results to yield an accurate and objective measure of the performance of thin-client systems. We have demonstrated the effectiveness of slow-motion benchmarking by using this technique to measure the performance of several popular thin-client systems in various network environments on Web and multimedia workloads. Our results show that slow-motion benchmarking solves the problems with using conventional benchmarks on thin-client systems and is an accurate tool for analyzing the performance of these systems. Jason Nieh, S. Jae Yang, Naomi Novik |
ACM Trans. Comput. Syst. | 3 |
| 2001 | Measuring Thin-Client Performance Using Slow-Motion Benchmarking
S. Jae Yang, Jason Nieh, Naomi Novik |
USENIX ATC, General Track | 3 |