Demo Data from EDBT 2026 / reviewers in the wild / expert
Hirotoshi Maegawa
dblp:34/5294
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Distributed systems › distributed coordination › multi-agent systems
distributed problem solving |
0.0 | 1 | 1994 | 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 |
0.0 | 1 | 1994 | ConClass: A Framework for Real-Time Distributed Knowledge-Based Processing · IEEE Trans. Knowl. Data Eng. 1994 |
Parallel and multicore computing
load balancing |
0.0 | 1 | 1994 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | Automatic Extraction of Filled-In Items from Bank-Check Images
Katsuhiko Ueda, Hirotoshi Maegawa, Ken'ichi Matsuo |
Document Analysis Systems | 2 |
| 1994 | ConClass: A Framework for Real-Time Distributed Knowledge-Based ProcessingabstractWe 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 |