Demo Data from EDBT 2026 / reviewers in the wild / expert
Steven Garcia
dblp:76/6530
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorDatabases, data management, data science and information retrieval · 1Human-computer interaction and ubiquitous computing · 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 |
Information retrieval · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval
evaluation |
0.1 | 1 | 2009 | A case for improved evaluation of query difficulty prediction · SIGIR 2009 |
Information retrieval › evaluation
evaluation methodology |
0.1 | 1 | 2009 | A case for improved evaluation of query difficulty prediction · SIGIR 2009 |
Information retrieval › evaluation › query performance prediction
query difficulty estimation |
0.1 | 1 | 2009 | A case for improved evaluation of query difficulty prediction · SIGIR 2009 |
Methods — techniques the papers use, named apart from their topics
correlation coefficient · 0.1TREC runs · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Efficient voxelization using projected optimal scanline
Steven Garcia, Weiwei Xu 0003, Tianjia Shao, Yin Yang 0002 |
Graph. Model. | 2 |
| 2015 | Fast image segmentation on mobile phone using multi-level graph cut
Steven Garcia, Patrick Gage Kelley, Yin Yang 0002 |
Graphics Interface | 1 |
| 2009 | A case for improved evaluation of query difficulty predictionabstractQuery difficulty prediction aims to identify, in advance, how well an information retrieval system will perform when faced with a particular search request. The current standard evaluation methodology involves calculating a correlation coefficient, to indicate how strongly the predicted query difficulty is related with an actual system performance measure, usually Average Precision. We run a series of experiments based on predictors that have been shown to perform well in the literature, comparing these across different TREC runs. Our results demonstrate that the current evaluation methodology is severely limited. Although it can be used to demonstrate the performance of a predictor for a single system, such performance is not consistent over a variety of retrieval systems. We conclude that published results in the query difficulty area are generally not comparable, and recommend that prediction be evaluated against a spectrum of underlying search systems. Falk Scholer, Steven Garcia |
SIGIR | 2 |