EDBT 2026 Demo / reviewers in the wild / expert
Anthony Wise
dblp:400/5142
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0003-5594-819XORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 since 2021
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 |
Performance modeling and evaluation · 100% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Performance modeling and evaluation
benchmarking |
0.9 | 1 | 2025 | An Adaptive Benchmark for Modeling User Exploration of Large Datasets · Proc. ACM Manag. Data 2025 |
Performance modeling and evaluation › benchmarking
database system benchmarking |
0.9 | 1 | 2025 | An Adaptive Benchmark for Modeling User Exploration of Large Datasets · Proc. ACM Manag. Data 2025 |
Performance modeling and evaluation
workload characterization |
0.9 | 1 | 2025 | An Adaptive Benchmark for Modeling User Exploration of Large Datasets · Proc. ACM Manag. Data 2025 |
Visualization and visual analytics › visualization design
dashboard design |
0.3 | 1 | 2025 | An Adaptive Benchmark for Modeling User Exploration of Large Datasets · Proc. ACM Manag. Data 2025 |
Methods — techniques the papers use, named apart from their topics
simulation-based benchmarking · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | An Adaptive Benchmark for Modeling User Exploration of Large DatasetsabstractIn this paper, we present a new DBMS performance benchmark that can simulate user exploration with any specified dashboard design made of standard visualization and interaction components. The distinguishing feature of our SImulation-BAsed (or SIMBA) benchmark is its ability to model user analysis goals as a set of SQL queries to be generated through a valid sequence of user interactions, as well as measure the completion of analysis goals by testing for equivalence between the user's previous queries and their goal queries. In this way, the SIMBA benchmark can simulate how an analyst opportunistically searches for interesting insights at the beginning of an exploration session and eventually hones in on specific goals towards the end. To demonstrate the versatility of the SIMBA benchmark, we use it to test the performance of four DBMSs with six different dashboard specifications and compare our results with IDEBench. Our results show how goal-driven simulation can reveal gaps in DBMS performance missed by existing benchmarking methods and across a range of data exploration scenarios. Joanna Purich, Anthony Wise, Leilani Battle |
Proc. ACM Manag. Data | 2 |