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K. S. Venugopal

dblp:67/927 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Indexing and storage engines
caching
0.112008
Temporal views over rdf data · WWW 2008
Graph data management
RDF data management
0.112008
Temporal views over rdf data · WWW 2008
Query processing and optimization › query result caching
semantic caching
0.112008
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
YearPublicationVenuePosition
2008 Temporal views over rdf data
abstract
Supporting 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
WWW4