Demo Data from VLDB 2026 / reviewers in the wild / expert
Roman Stanchak
dblp:70/5898
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2008
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Medical and health informatics › clinical text processing
patient history analysis |
0.1 | 1 | 2008 | Aligning temporal data by sentinel events: discovering patterns in electronic health records · CHI 2008 |
Visualization and visual analytics
temporal data visualization |
0.1 | 1 | 2008 | Aligning temporal data by sentinel events: discovering patterns in electronic health records · CHI 2008 |
Data mining
pattern mining |
0.0 | 1 | 2008 | Aligning temporal data by sentinel events: discovering patterns in electronic health records · CHI 2008 |
Data mining › temporal data mining
temporal patterns |
0.0 | 1 | 2008 | Aligning temporal data by sentinel events: discovering patterns in electronic health records · CHI 2008 |
Methods — techniques the papers use, named apart from their topics
qualitative study · 0.2interviews · 0.2controlled study · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Aligning temporal data by sentinel events: discovering patterns in electronic health recordsabstractElectronic Health Records (EHRs) and other temporal databases contain hidden patterns that reveal important cause-and-effect phenomena. Finding these patterns is a challenge when using traditional query languages and tabular displays. We present an interactive visual tool that complements query formulation by providing operations to align, rank and filter the results, and to visualize estimates of the intervals of validity of the data. Display of patient histories aligned on sentinel events (such as a first heart attack) enables users to spot precursor, co-occurring, and aftereffect events. A controlled study demonstrates the benefits of providing alignment (with a 61% speed improvement for complex tasks). A qualitative study and interviews with medical professionals demonstrates that the interface can be learned quickly and seems to address their needs. Taowei David Wang, Catherine Plaisant, Alexander J. Quinn, Roman Stanchak, Shawn N. Murphy, Ben Shneiderman |
CHI | 4 |