Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from VLDB 2026 / reviewers in the wild / expert

Peter Au

dblp:54/5704 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2000
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 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.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › search engines
search result clustering		 0.012000
New paradigms in information visualization · SIGIR 2000
Visualization and visual analytics › text visualization
document visualization		 0.012000
New paradigms in information visualization · SIGIR 2000
Visualization and visual analytics
information visualization		 0.012000
New paradigms in information visualization · SIGIR 2000
Information retrieval › interactive information retrieval › exploratory search
query result exploration		 0.012000
New paradigms in information visualization · SIGIR 2000

Methods — techniques the papers use, named apart from their topics

clustering · 0.1
YearPublicationVenuePosition
2000 New paradigms in information visualization
abstract
We present three new visualization front-ends that aid navigation through the set of documents returned by a search engine (hit documents). We cluster the hit documents to visually group these documents and label the groups with related words. The different front-ends cater for different user needs, but all can browse cluster information as well as drilling up or down in one or more clusters and refining the search using one or more of the suggested related keywords.
Peter Au, Matthew Carey, Shalini Sewraz, Yike Guo, Stefan M. Rüger
 SIGIR1