Parallel Structural Join Algorithm on Shared-Memory Multi-Core Systems

2008 ◽  
Author(s):  
Le Liu ◽  
Jianhua Feng ◽  
Guoliang Li ◽  
Qian Qian ◽  
Jianhui Li
Keyword(s):  
Shared Memory ◽  
Structural Join ◽  
Join Algorithm

Improvement of XML Structural Join Algorithm with Weaving Multi-documents

2013 ◽  
Vol 12 (23) ◽  
pp. 7240-7244
Author(s):  
Jiang Yan ◽  
Wang Yu-Xuan ◽  
Jin Xin ◽  
Li Xin ◽  
Pan Ping
Keyword(s):  
Structural Join ◽  
Join Algorithm

A Fast Structural Join Algorithm Based on Partitioning

2009 ◽  
Vol 31 (1) ◽  
pp. 77-90
Author(s):  
Guo-Ren WANG ◽  
Bai-You QIAO ◽  
Dong-Hong HAN ◽  
Bin WANG
Keyword(s):  
Structural Join ◽  
Join Algorithm

Counting cliques in parallel without a cluster: Engineering a fork/join algorithm for shared-memory platforms

2019 ◽  
Vol 496 ◽  
pp. 553-571
Author(s):  
Emilio Coppa ◽  
Irene Finocchi ◽  
Renan Leon Garcia
Keyword(s):  
Shared Memory ◽  
Join Algorithm

Labelling-Scheme-Based Subgraph Query Processing on Graph Data

2011 ◽  
pp. 142-174
Author(s):  
Hongzhi Wang ◽  
Jianzhong Li ◽  
Hong Gao
Keyword(s):  
Query Processing ◽  
Structural Join ◽  
Research Issues ◽  
Graph Data ◽  
Join Algorithm ◽  
Reachability Queries ◽  
Subgraph Query ◽  
New Challenges ◽  
General Graphs ◽  
Labelling Scheme

When data are modeled as graphs, many research issues arise. In particular, there are many new challenges in query processing on graph data. This chapter studies the problem of structural queries on graph data. A hash-based structural join algorithm, HGJoin, is first proposed to handle reachability queries on graph data. Then, it is extended to the algorithms to process structural queries in form of bipartite graphs. Finally, based on these algorithms, a strategy to process subgraph queries in form of general DAGs is proposed. It is notable that all the algorithms above can be slightly modified to process structural queries in form of general graphs.

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