Demo Data from EDBT 2026 / reviewers in the wild / expert
Brian Tieman
dblp:27/1969
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2011
—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
1 paper |
High-performance computing · 88% Distributed systems · 12% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing
data transfer |
0.1 | 1 | 2010 | A data transfer framework for large-scale science experiments · HPDC 2010 |
High-performance computing › data transfer
wide-area data transfer |
0.1 | 1 | 2010 | A data transfer framework for large-scale science experiments · HPDC 2010 |
Distributed systems › distributed communication
data dissemination |
0.0 | 1 | 2010 | A data transfer framework for large-scale science experiments · HPDC 2010 |
High-performance computing
scientific data management |
0.0 | 1 | 2010 | A data transfer framework for large-scale science experiments · HPDC 2010 |
Methods — techniques the papers use, named apart from their topics
restart mechanisms · 0.1automatic tuning · 0.1GridFTP · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Moving huge scientific datasets over the InternetabstractSUMMARY Modern scientific experiments can generate hundreds of gigabytes to terabytes or even petabytes of data that may be maintained in large numbers of relatively small files. Frequently, these data must be disseminated to remote collaborators or computational centers for data analysis. Moving this dataset with high performance and strong robustness and providing a simple interface for users are challenging tasks. We present a data transfer framework comprising a high‐performance data transfer library based on GridFTP, an extensible data scheduler with four data scheduling policies, and a GUI that allows users to transfer their dataset easily, reliably, and securely. This system incorporates automatic tuning mechanisms to select at runtime the number of concurrent threads to be used for transfers. Also included are restart mechanisms for handling client, network, and server failures. Experimental results indicate that our data transfer system can significantly improve data transfer performance and can recover well from failures. Copyright © 2011 John Wiley & Sons, Ltd. Wantao Liu, Brian Tieman, Rajkumar Kettimuthu, Ian T. Foster |
Concurr. Comput. Pract. Exp. | 2 |
| 2010 | A data transfer framework for large-scale science experimentsabstractModern scientific experiments can generate hundreds of gigabytes to terabytes or even petabytes of data that may furthermore be maintained in large numbers of relatively small files. Frequently, this data must be disseminated to remote collaborators or computational centers for data analysis. Moving this data with high performance and strong robustness and providing a simple interface for users are challenging tasks. We present a data transfer framework comprising a high-performance data transfer library based on GridFTP, a data scheduler, and a graphical user interface that allows users to transfer their data easily, reliably, and securely. This system incorporates automatic tuning mechanisms to select at runtime the number of concurrent threads to be used for transfers. Also included are restart mechanisms capable of dealing with client, network, and server failures. Experimental results indicate that our data transfer system can significantly improve data transfer performance and can recover well from failures. Wantao Liu, Brian Tieman, Rajkumar Kettimuthu, Ian T. Foster |
HPDC | 2 |