T. Nakayama

dblp:02/6918 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1984
—ORCID · none

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

Databases, data management, data science and information retrieval · 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
Query processing and optimization · 87% Information retrieval · 13%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Query processing and optimization
join processing
0.011984
Architecture and Algorithm for Parallel Execution of a Join Operation · ICDE 1984
Query processing and optimization › join processing
parallel join
0.011984
Architecture and Algorithm for Parallel Execution of a Join Operation · ICDE 1984
Parallel and multicore computing › parallel architecture
master-slave architecture
0.011984
Architecture and Algorithm for Parallel Execution of a Join Operation · ICDE 1984
Parallel and multicore computing › parallel query processing
parallel database machine
0.011984
Architecture and Algorithm for Parallel Execution of a Join Operation · ICDE 1984
Information retrieval
semantic correlation
0.011984
Architecture and Algorithm for Parallel Execution of a Join Operation · ICDE 1984

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

performance analysis · 0.0hash partitioning · 0.0
YearPublicationVenuePosition
1984 Architecture and Algorithm for Parallel Execution of a Join Operation
abstract
The relational database system ARES which authors have developed can manage semantic aspects of data to be retrieved, and has two types of relations, namely, conventional and semantic relations. The semantic relation is attached to a conventional relation in terms of a join operation every time a flexible interpretation of queries, accepting a certain amount of ambiguity in query conditions, is required. In order to overcome performance degradation caused by the additional join operation, the authors present an algorithm for parallel execution of a join operation assuming an architecture composed of one master unit and n slave units which are linked to each other. Here, relations are distributed to n slave units by means of hash. According to the static analysis of performances, it is expected that the execution efficiency of a join operation is improved by the extent of o(n) and o(n2) compared with the cases of a single processor using hash for restricting source-target pairs of the subrelations, and that not using hash, respectively. Further investigation, however, is needed in a dynamic environment.
T. Nakayama, Masahito Hirakawa, Tadao Ichikawa
ICDE1