EDBT 2026 Demo / reviewers in the wild / expert
Abhinav Sikharam
dblp:292/6194
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 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 graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visualization and visual analytics
belief elicitation |
0.5 | 1 | 2021 | Data Prophecy: Exploring the Effects of Belief Elicitation in Visual Analytics · CHI 2021 |
Visualization and visual analytics › visual analytics
exploratory data analysis |
0.1 | 1 | 2021 | Data Prophecy: Exploring the Effects of Belief Elicitation in Visual Analytics · CHI 2021 |
Methods — techniques the papers use, named apart from their topics
comparative study · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Data Prophecy: Exploring the Effects of Belief Elicitation in Visual AnalyticsabstractInteractive visualizations are widely used in exploratory data analysis, but existing systems provide limited support for confirmatory analysis. We introduce PredictMe, a tool for belief-driven visual analysis, enabling users to draw and test their beliefs against data, as an alternative to data-driven exploration. PredictMe combines belief elicitation with traditional visualization interactions to support mixed analysis styles. In a comparative study, we investigated how these affordances impact participants’ cognition. Results show that PredictMe prompts participants to incorporate their working knowledge more frequently in queries. Participants were more likely to attend to discrepancies between their mental models and the data. However, those same participants were also less likely to engage in interactions associated with exploration, and ultimately inspected fewer visualizations and made fewer discoveries. The results suggest that belief elicitation may moderate exploratory behaviors, instead nudging users to be more deliberate in their analysis. We discuss the implications for visualization design. Ratanond Koonchanok, Parul Baser, Abhinav Sikharam, Nirmal Kumar Raveendranath, Khairi Reda |
CHI | 3 |