scholarly journals Optimized Distributed Subgraph Matching Algorithm Based on Partition Replication

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 184
Author(s):  
Ling Yuan ◽  
Jiali Bin ◽  
Peng Pan
Keyword(s):  
Large Scale ◽  
High Efficiency ◽  
Search Space ◽  
Dynamic Graph ◽  
Matching Algorithm ◽  
Query Graph ◽  
Subgraph Matching ◽  
Match Algorithm ◽  
Large Scale Data ◽  
Scale Data

At present, with the explosive growth of data scale, subgraph matching for massive graph data is difficult to satisfy with efficiency. Meanwhile, the graph index used in existing subgraph matching algorithm is difficult to update and maintain when facing dynamic graphs. We propose a distributed subgraph matching algorithm based on Partition Replica (noted as PR-Match) to process the partition and storage of large-scale data graphs. The PR-Match algorithm first splits the query graph into sub-queries, then assigns the sub-query to each node for sub-graph matching, and finally merges the matching results. In the PR-Match algorithm, we propose a heuristic rule based on prediction cost to select the optimal merging plan, which greatly reduces the cost of merging. In order to accelerate the matching speed of the sub-query graph, a vertex code based on the vertex neighbor label signature is proposed, which greatly reduces the search space for the subquery. As the vertex code is based on the increment, the problem that the feature-based graph index is difficult to maintain in the face of the dynamic graph is solved. An abundance of experiments on real and synthetic datasets demonstrate the high efficiency and strong scalability of the PR-Match algorithm when handling large-scale data graphs.

scholarly journals Subgraph-Indexed Sequential Subdivision for Continuous Subgraph Matching on Dynamic Knowledge Graph

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Yunhao Sun ◽  
Guanyu Li ◽  
Mengmeng Guan ◽  
Bo Ning
Keyword(s):  
Empirical Studies ◽  
Search Space ◽  
Knowledge Graph ◽  
Dynamic Graph ◽  
Matching Problem ◽  
Flow Graph ◽  
Query Graph ◽  
Subgraph Matching ◽  
Wide Range ◽  
Multiple Edges

Continuous subgraph matching problem on dynamic graph has become a popular research topic in the field of graph analysis, which has a wide range of applications including information retrieval and community detection. Specifically, given a query graph q , an initial graph G 0 , and a graph update stream △ G i , the problem of continuous subgraph matching is to sequentially conduct all possible isomorphic subgraphs covering △ G i of q on G i (= G 0   ⊕   △ G i ). Since knowledge graph is a directed labeled multigraph having multiple edges between a pair of vertices, it brings new challenges for the problem focusing on dynamic knowledge graph. One challenge is that the multigraph characteristic of knowledge graph intensifies the complexity of candidate calculation, which is the combination of complex topological and attributed structures. Another challenge is that the isomorphic subgraphs covering a given region are conducted on a huge search space of seed candidates, which causes a lot of time consumption for searching the unpromising candidates. To address these challenges, a method of subgraph-indexed sequential subdivision is proposed to accelerating the continuous subgraph matching on dynamic knowledge graph. Firstly, a flow graph index is proposed to arrange the search space of seed candidates in topological knowledge graph and an adjacent index is designed to accelerate the identification of candidate activation states in attributed knowledge graph. Secondly, the sequential subdivision of flow graph index and the transition state model are employed to incrementally conduct subgraph matching and maintain the regional influence of changed candidates, respectively. Finally, extensive empirical studies on real and synthetic graphs demonstrate that our techniques outperform the state-of-the-art algorithms.

E-Commerce data classification in the cloud environment based on bayesian algorithm

2020 ◽  
pp. 1-8
Author(s):  
Bing Xu
Keyword(s):  
Large Scale ◽  
High Efficiency ◽  
Feature Selection Method ◽  
Data Classification ◽  
Classification Algorithm ◽  
Testing Time ◽  
Bayesian Algorithm ◽  
Large Scale Data ◽  
Distributed Platform ◽  
Scale Data

In the process of e-commerce transactions, a large amount of data will be generated, whose effective classification is one of current research hotspots. An improved feature selection method was proposed based on the characteristics of Bayesian classification algorithm. Due to the long training and testing time of modern large-scale data classification on a single computer, a data classification algorithm based on Naive Bayes was designed and implemented on the Hadoop distributed platform. The experimental results showed that the improved algorithm could effectively improve the accuracy of classification, and the designed parallel Bayesian data classification algorithm had high efficiency, which was suitable for the processing and analysis of massive data.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

COMMUNITY-CURATED DATA RESOURCES AND LARGE-SCALE DATA-MODEL SYNTHESES: THE CHILDREN OF COHMAP

2016 ◽  
Author(s):  
John W. Williams ◽  
◽  
Simon Goring ◽  
Eric Grimm ◽  
Jason McLachlan
Keyword(s):  
Data Model ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Local and global approaches of affinity propagation clustering for large scale data

2008 ◽  
Vol 9 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Ding-yin Xia ◽  
Fei Wu ◽  
Xu-qing Zhang ◽  
Yue-ting Zhuang
Keyword(s):  
Large Scale ◽  
Affinity Propagation ◽  
Large Scale Data ◽  
Affinity Propagation Clustering ◽  
Scale Data

scholarly journals A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  
Keyword(s):  
Real Time ◽  
International Collaboration ◽  
Large Scale ◽  
Data Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Scale Data
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