Demo Data from EDBT 2026 / reviewers in the wild / expert
Ian Charlesworth
dblp:116/5143
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2012
0000-0003-0546-2856ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 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.
| Databases, data mining, and information retrieval
1 paper |
Query processing and optimization · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Query processing and optimization › query optimization
join enumeration |
0.1 | 1 | 2012 | Analyzing Query Optimization Process: Portraits of Join Enumeration Algorithms · ICDE 2012 |
Query processing and optimization
query optimization |
0.1 | 1 | 2012 | Analyzing Query Optimization Process: Portraits of Join Enumeration Algorithms · ICDE 2012 |
Methods — techniques the papers use, named apart from their topics
visualization · 0.1optimization log mining · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Analyzing Query Optimization Process: Portraits of Join Enumeration AlgorithmsabstractSearch spaces generated by query optimizers during the optimization process encapsulate characteristics of the join enumeration algorithms, the cost models, as well as critical decisions made for pruning and choosing the best plan. We demonstrate the Join Enumeration Viewer which is a tool designed for visualizing, mining, and comparing plan search spaces generated by different join enumeration algorithms when optimizing same SQL statement. We have enhanced Sybase SQL Anywhere relational database management system to log, in a very compact format, its search space during an optimization process. Such optimization log can then be analyzed by the Join Enumeration Viewer which internally builds the logical and physical plan graphs representing complete and partial plans considered during the optimization process. The optimization logs also contain statistics of the resource consumption during the query optimization such as optimization time breakdown, for example, for logical join enumeration versus costing physical plans, and memory allocation for different optimization structures. The SQL Anywhere Optimizer implements a highly adaptable, self-managing, search space generation algorithm by having several join enumeration algorithms to choose from, each enhanced with different ordering and pruning techniques. The emphasis of the demonstration will be on comparing and contrasting these join enumeration algorithms by analyzing their optimization logs. The demonstration scenarios will include optimizing SQL statements under various conditions which will exercise different algorithms, pruning and ordering techniques. These search spaces will then be visualized and compared using the Join Enumeration Viewer. Anisoara Nica, Ian Charlesworth |
ICDE | 2 |