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Hing Wing To

dblp:97/5693 · DBLP profile ↗
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4ranked-venue papers
0as first author
0since 2021 · last 1997
—ORCID · none

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

Artificial intelligence and machine learning · 2Systems, architecture and hardware · 2Databases, data management, data science and information retrieval · 2

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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 100%
Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › big data analytics
large-scale data mining
0.011997
Large Scale Data Mining: Challenges and Responses · KDD 1997
Parallel and multicore computing › parallel programming models › structured parallelism
algorithmic skeletons
0.011995
Parallel Skeletons for Structured Composition · PPoPP 1995
Parallel and multicore computing
parallel programming models
0.011995
Parallel Skeletons for Structured Composition · PPoPP 1995
YearPublicationVenuePosition
1997 Parallel Induction Algorithms for Data Mining
John Darlington, Yike Guo, Janjao Sutiwaraphun, Hing Wing To
IDA4
1997 Large Scale Data Mining: Challenges and Responses
Jaturon Chattratichat, John Darlington, Moustafa Ghanem, Yike Guo, Harald Frank Hüning, Janjao Sutiwaraphun, Hing Wing To
KDD8
1995 Functional Skeletons for Parallel Coordination
John Darlington, Yike Guo, Hing Wing To, Jin Yang 0002
Euro-Par3
1995 Parallel Skeletons for Structured Composition
abstract
In this paper, we propose a straightforward solution to the problems of compositional parallel programming by using skeletons as the uniform mechanism for structured composition. In our approach parallel programs are constructed by composing procedures in a conventional base language using a set of high-level, pre-defined, functional, parallel computational forms known as skeletons. The ability to compose skeletons provides us with the essential tools for building further and more complex application-oriented skeletons specifying important aspects of parallel computation. Compared with the process network based composition approach, such as PCN, the skeleton approach abstracts away the fine details of connecting communication ports to the higher level mechanism of making data distributions conform, thus avoiding the complexity of using lower level ports as the means of interaction. Thus, the framework provides a natural integration of the compositional programming approach with the data parallel programming paradigm.
John Darlington, Yike Guo, Hing Wing To, Jin Yang 0002
PPoPP3