Demo Data from EDBT 2026 / reviewers in the wild / expert
K. S. Venugopal
dblp:67/927
· 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
Databases, data management, data science and information retrieval · 1Applied, interdisciplinary, general and emerging 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.
| Databases, data mining, and information retrieval
1 paper |
Graph data management · 33% Indexing and storage engines · 33% Query processing and optimization · 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
caching |
0.1 | 1 | 2008 | Temporal views over rdf data · WWW 2008 |
Graph data management
RDF data management |
0.1 | 1 | 2008 | Temporal views over rdf data · WWW 2008 |
Query processing and optimization › query result caching
semantic caching |
0.1 | 1 | 2008 | Temporal views over rdf data · WWW 2008 |
Methods — techniques the papers use, named apart from their topics
temporal view mechanism · 0.1in-memory caching · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Temporal views over rdf dataabstractSupporting fast access to large RDF stores has been one of key challenges for enabling use of the Semantic Web in real-life applications, more so in sensor-based systems where large amounts of historic data needs to be stored. We propose a semantics-based temporal view mechanism that enables faster access to time-varying data by caching into memory only the required subset of RDF triples. We describe our experience of implementing such a framework in the context of a wide area network monitoring system. Our preliminary results show that our solution significantly improves client access time and scales well for moderate data sets. Geetha Manjunath, Ramamurthy Badrinath, Craig Sayers, K. S. Venugopal |
WWW | 4 |