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Twee-Hee Ong

dblp:70/426 · DBLP profile ↗
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4ranked-venue papers
1as 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 · 2 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 2Artificial intelligence and machine learning · 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
Data mining · 44% Query processing and optimization · 44% Data models and query languages · 13%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › sequence analysis › sequence similarity search
sequence database search
0.012003
BLAST++: BLASTing queries in batches · Bioinform. 2003
Data mining › pattern mining
association rule mining
0.012002
Predator-Miner: Ad hoc Mining of Associations Rules within a Database Management System · ICDE 2002
Query processing and optimization
query optimization
0.012002
Predator-Miner: Ad hoc Mining of Associations Rules within a Database Management System · ICDE 2002
Data models and query languages
SQL
0.012002
Predator-Miner: Ad hoc Mining of Associations Rules within a Database Management System · ICDE 2002

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

BLAST · 0.0
YearPublicationVenuePosition
2003 BLAST++ : A Tool for BLASTing Queries in Batches
Twee-Hee Ong, Beng Chin Ooi, Kian-Lee Tan
APBC2
2003 BLAST++: BLASTing queries in batches
abstract
UNLABELLED: BLAST++ is a tool that is integrated with NCBI BLAST, allowing multiple, say K, queries to be searched against a database concurrently. The results obtained by BLAST++ are identical to that obtained by executing BLAST on each of the K queries, but BLAST++ completes the processing in a much shorter time. AVAILABILITY: http://xena1.ddns.comp.nus.edu.sg/~genesis/blast++ SUPPLEMENTARY INFORMATION: http://xena1.ddns.comp.nus.edu.sg/~genesis/blast++
Beng Chin Ooi, Kian-Lee Tan, Twee-Hee Ong
Bioinform.4
2002 Indexing Genomic Databases for Fast Homology Searching
Twee-Hee Ong, Kian-Lee Tan
DEXA1
2002 Predator-Miner: Ad hoc Mining of Associations Rules within a Database Management System
abstract
We present a prototype system, Predator-Miner, which extends Predator with an relational-like association rule mining operator to support data mining operations. Predator-Miner allows a user to combine association rule mining queries with SQL queries. This approach towards tight integration differs from existing techniques of using user-defined functions (UDFs), stored procedures, or re-expressing a mining query as several SQL queries in two aspects. First, by encapsulating the task of association rule mining in a relational operator, we allow association rule mining to be considered as part of the query plan, on which query optimization can be performed on the mining query holistically. Second, by integrating it as a relational operator, we can leverage on the mature field of relational database technology. We extend Predator to support a variant of DMQL, and allow SQL and DMQL to be intermixed in a query. We also demonstrate a cost-based mining query optimization framework.
Wee Hyong Tok, Twee-Hee Ong, Wai Lup Low, Indriyati Atmosukarto, Stéphane Bressan
ICDE2