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Hirotoshi Maegawa

dblp:34/5294 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2004
—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-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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 67% Distributed systems · 33%

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

TopicWeightPapersLastEvidence papers
Distributed systems › distributed coordination › multi-agent systems
distributed problem solving
0.011994
ConClass: A Framework for Real-Time Distributed Knowledge-Based Processing · IEEE Trans. Knowl. Data Eng. 1994
Parallel and multicore computing › load balancing
dynamic load balancing
0.011994
ConClass: A Framework for Real-Time Distributed Knowledge-Based Processing · IEEE Trans. Knowl. Data Eng. 1994
Parallel and multicore computing
load balancing
0.011994
ConClass: A Framework for Real-Time Distributed Knowledge-Based Processing · IEEE Trans. Knowl. Data Eng. 1994

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

object replication · 0.0concurrent subproblem decomposition · 0.0
YearPublicationVenuePosition
2004 Automatic Extraction of Filled-In Items from Bank-Check Images
Katsuhiko Ueda, Hirotoshi Maegawa, Ken'ichi Matsuo
Document Analysis Systems2
1994 ConClass: A Framework for Real-Time Distributed Knowledge-Based Processing
abstract
We have developed a problem-solving framework, called ConClass, that is capable of classifying continuous real-time problems dynamically and concurrently on a distributed system. ConClass provides an efficient development environment for describing and decomposing a classification problem and synthesizing solutions. In ConClass, decomposed concurrent subproblems specified by the application developer effectively correspond to the actual distributed hardware elements. This scheme is useful for designing and implementing efficient distributed processing, making it easier to anticipate and evaluate system behavior. The ConClass system provides an object replication feature that prevents any particular object from being overloaded. In order to deal with an indeterminate amount of problem data, ConClass dynamically creates object networks that justify hypothesized solutions, and thus achieves a dynamic load distribution. A number of efficient execution mechanisms that manage a variety of asynchronous aspects of distributed processing have been implemented without using schedulers or synchronization schemes that are liable to develop bottlenecks. We have confirmed the efficiency of parallel distributed processing and load balancing of ConClass with an experimental application.>
Hirotoshi Maegawa
IEEE Trans. Knowl. Data Eng.1