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Hua Soon Sim

dblp:56/5368 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · none

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

Databases, data management, data science and information retrieval · 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
Query processing and optimization · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Query processing and optimization
preference query		 0.012003
Preference-Driven Query Processing · ICDE 2003
Query processing and optimization › interactive query processing
progressive query processing		 0.012003
Preference-Driven Query Processing · ICDE 2003

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

r-tree partitioning · 0.0
YearPublicationVenuePosition
2003 Preference-Driven Query Processing
abstract
We propose a partition-based framework for evaluating preference queries. The framework is independent of how partitions are generated, and returns answers progressively as the query is being evaluated. We evaluated the framework using partitions obtained from the leaf nodes of R-trees. Our study shows that our approach can shorten the initial response time.
Pin-Kwang Eng, Beng Chin Ooi, Hua Soon Sim, Kian-Lee Tan
 ICDE3