Demo Data from VLDB 2026 / reviewers in the wild / expert
Kengo Fujioka
dblp:82/3058
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2008
—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-authorApplied, interdisciplinary, general and emerging 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 · 67% Indexing and storage engines · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Indexing and storage engines
bitmap index |
0.1 | 1 | 2008 | Application of bitmap index to information retrieval · WWW 2008 |
Information retrieval
indexing |
0.1 | 1 | 2008 | Application of bitmap index to information retrieval · WWW 2008 |
Information retrieval › indexing
inverted index |
0.1 | 1 | 2008 | Application of bitmap index to information retrieval · WWW 2008 |
Methods — techniques the papers use, named apart from their topics
hierarchical document-term matrix · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Application of bitmap index to information retrievalabstractWe developed the HS-bitmap index for efficient information retrieval. The HS-bitmap index is a hierarchical document-term matrix: the original document-term matrix is called the leaf matrix and an upper matrix is the summary of its lower matrix. Our experiment results show the HS-bitmap index performs better than the inverted index with a minor space overhead. Kengo Fujioka, Yukio Uematsu, Makoto Onizuka |
WWW | 1 |