Demo Data from SIGIR 2026 / reviewers in the wild / expert
Alexander Thiem
dblp:74/2162
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2009
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 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.
| Databases, data mining, and information retrieval
1 paper |
Database system architecture and tuning · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Performance modeling and evaluation · 100% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Database system architecture and tuning › database performance management
performance monitoring |
0.1 | 1 | 2009 | An Integrated Approach to Performance Monitoring for Autonomous Tuning · ICDE 2009 |
Database system architecture and tuning › database design
physical database design |
0.1 | 1 | 2009 | An Integrated Approach to Performance Monitoring for Autonomous Tuning · ICDE 2009 |
Methods — techniques the papers use, named apart from their topics
recommendation · 0.2integrated monitoring · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | An Integrated Approach to Performance Monitoring for Autonomous TuningabstractWith an ever growing complexity and data volume, the administration of today's relational database management systems has become one of the most important cost factors in their operation. Dynamic workloads and shifting demands require continuous effort from the DBA to deliver adequate performance. The goal of a modern DBMS must be to support the DBA's work with automated processes and workflows that facilitate quick and precise decisions. In this paper, we present the concept of an integrated performance monitoring in the Ingres DBMS that provides long-term collection of information valuable for performance tuning, problem identification and prediction. The approach of enhancing the DBMS core with monitoring features rather than adding an additional watchdog on top of the system leads to a high data resolution while still having only a minimal overhead. This concept was successfully prototyped in Ingres with a very small overhead for most usage scenarios. The prototype is able to collect and analyze data and to give useful recommendations on the physical database design to improve overall performance of the DBMS. Alexander Thiem, Kai-Uwe Sattler |
ICDE | 1 |