Graph Compression Storage Based on Spatial Cluster Entity Optimization
Graph storage technology is confronted with an enormous challenge as far as the compact and complex graph-structure data. This phenomenon is derived from social networks with spatially intensive data. Since a hot event can cause the generation of a network cluster, which consists of a massive duplic...
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doaj-f221a422e89f4c3abf9b47741ca612e22021-03-30T02:04:01ZengIEEEIEEE Access2169-35362020-01-018290752908810.1109/ACCESS.2020.29716398981901Graph Compression Storage Based on Spatial Cluster Entity OptimizationDawei Wang0https://orcid.org/0000-0003-2326-4079Wanqiu Cui1https://orcid.org/0000-0002-6793-6394Biao Qin2https://orcid.org/0000-0002-4304-675XSchool of Information, Renmin University of China, Beijing, ChinaSchool of Computer Science, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Information, Renmin University of China, Beijing, ChinaGraph storage technology is confronted with an enormous challenge as far as the compact and complex graph-structure data. This phenomenon is derived from social networks with spatially intensive data. Since a hot event can cause the generation of a network cluster, which consists of a massive duplicate associated entities in the social networks, the space utilization and processing speed of graph data is obstructed. Therefore, it is necessary to design a graph storage mechanism specifically for the above data. In this paper, we propose a Graph compression Storage engine based on spatial Cluster entity Optimization (GSCO), which improves the native graph storage model through the proposed the many-to-one mapping structure and a Heat Evolution Elimination algorithm (H2E). Firstly, we define the spatial cluster entity formally and confirm the compressed storage objects. Then, we introduce the many-to-one relationship to transfer the mapping structure between the node and property. It compresses the data to raise the space utilization of the graph database. Finally, we propose the H2E algorithm that allows the representative nodes to be anchored an extended period in memory according to the heat evolution acceleration. It increases the hit rate and throughput and reduces the I/O operation by deleting the redundancy of data. Extensive experiments results show that the proposed GSCO storage model is better than Neo4j for reading and writing data in spatial clustering entity. It significantly promotes the effectiveness of graph operation, including the data loading, the common queries, and the clustering test.https://ieeexplore.ieee.org/document/8981901/Graph compression storagesocial networksspatial cluster entityheat evolution |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dawei Wang Wanqiu Cui Biao Qin |
spellingShingle |
Dawei Wang Wanqiu Cui Biao Qin Graph Compression Storage Based on Spatial Cluster Entity Optimization IEEE Access Graph compression storage social networks spatial cluster entity heat evolution |
author_facet |
Dawei Wang Wanqiu Cui Biao Qin |
author_sort |
Dawei Wang |
title |
Graph Compression Storage Based on Spatial Cluster Entity Optimization |
title_short |
Graph Compression Storage Based on Spatial Cluster Entity Optimization |
title_full |
Graph Compression Storage Based on Spatial Cluster Entity Optimization |
title_fullStr |
Graph Compression Storage Based on Spatial Cluster Entity Optimization |
title_full_unstemmed |
Graph Compression Storage Based on Spatial Cluster Entity Optimization |
title_sort |
graph compression storage based on spatial cluster entity optimization |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Graph storage technology is confronted with an enormous challenge as far as the compact and complex graph-structure data. This phenomenon is derived from social networks with spatially intensive data. Since a hot event can cause the generation of a network cluster, which consists of a massive duplicate associated entities in the social networks, the space utilization and processing speed of graph data is obstructed. Therefore, it is necessary to design a graph storage mechanism specifically for the above data. In this paper, we propose a Graph compression Storage engine based on spatial Cluster entity Optimization (GSCO), which improves the native graph storage model through the proposed the many-to-one mapping structure and a Heat Evolution Elimination algorithm (H2E). Firstly, we define the spatial cluster entity formally and confirm the compressed storage objects. Then, we introduce the many-to-one relationship to transfer the mapping structure between the node and property. It compresses the data to raise the space utilization of the graph database. Finally, we propose the H2E algorithm that allows the representative nodes to be anchored an extended period in memory according to the heat evolution acceleration. It increases the hit rate and throughput and reduces the I/O operation by deleting the redundancy of data. Extensive experiments results show that the proposed GSCO storage model is better than Neo4j for reading and writing data in spatial clustering entity. It significantly promotes the effectiveness of graph operation, including the data loading, the common queries, and the clustering test. |
topic |
Graph compression storage social networks spatial cluster entity heat evolution |
url |
https://ieeexplore.ieee.org/document/8981901/ |
work_keys_str_mv |
AT daweiwang graphcompressionstoragebasedonspatialclusterentityoptimization AT wanqiucui graphcompressionstoragebasedonspatialclusterentityoptimization AT biaoqin graphcompressionstoragebasedonspatialclusterentityoptimization |
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1724185923295379456 |