Dynamic partitioning in a transputer environment

1990 ◽  
Author(s):  
K. Dussa ◽  
B. Carlson ◽  
L. Dowdy ◽  
K.-H. Park
Keyword(s):  
Dynamic Partitioning

Enhancing mirror adaptive random testing through dynamic partitioning

2015 ◽  
Vol 67 ◽  
pp. 13-29 ◽  
Author(s):  
Rubing Huang ◽  
Huai Liu ◽  
Xiaodong Xie ◽  
Jinfu Chen
Keyword(s):  
Random Testing ◽  
Dynamic Partitioning ◽  
Adaptive Random Testing

scholarly journals Dynamic Partitioning and Convection: A New Mechanism for the Self-Organization Pattern of the Excitable Cortical Waves?

2019 ◽  
Vol 116 (3) ◽  
pp. 532a
Author(s):  
Tatsat Banerjee ◽  
Yuchuan Miao ◽  
Debojyoti Biswas ◽  
Pablo A. Iglesias ◽  
Peter N. Devreotes
Keyword(s):  
The Self ◽  
Self Organization ◽  
Dynamic Partitioning ◽  
Cortical Waves

scholarly journals Dynamic Partitioning Supporting Load Balancing for Distributed RDF Graph Stores

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 926
Author(s):  
Kyoungsoo Bok ◽  
Junwon Kim ◽  
Jaesoo Yoo
Keyword(s):  
Load Balancing ◽  
Distributed Processing ◽  
Data Partitioning ◽  
Rdf Graph ◽  
Dynamic Partitioning ◽  
Usage Frequency ◽  
Partitioning Methods ◽  
Description Framework ◽  
Rdf Graphs ◽  
Distributed Server

Various resource description framework (RDF) partitioning methods have been studied for the efficient distributed processing of a large RDF graph. The RDF graph has symmetrical characteristics because subject and object can be used interchangeably if predicate is changed. This paper proposes a dynamic partitioning method of RDF graphs to support load balancing in distributed environments where data insertion and change continue to occur. The proposed method generates clusters and subclusters by considering the usage frequency of the RDF graph that are used by queries as the criteria to perform graph partitioning. It creates a cluster by grouping RDF subgraphs with higher usage frequency while creating a subcluster with lower usage frequency. These clusters and subclusters conduct load balancing by using the mean frequency of queries for the distributed server and conduct graph data partitioning by considering the size of the data stored in each distributed server. It also minimizes the number of edge-cuts connected to clusters and subclusters to minimize communication costs between servers. This solves the problem of data concentration to specific servers due to ongoing data changes and additions and allows efficient load balancing among servers. The performance results show that the proposed method significantly outperforms the existing partitioning methods in terms of query performance time in a distributed server.

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