Graph compression algorithm based on a two-level index structure
The demand for the analysis and application of graph data in various fields is increasing day by day.The management of large-scale graph data with complicated structure and high degree of coupling faces two challenges:one is querying speed too slow,the other is space consumption too large.Facing the...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2018-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018104/ |
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| _version_ | 1841539410379669504 |
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| author | Gaochao LI Ben LI Yuhai LU Mengya LIU Yanbing LIU |
| author_facet | Gaochao LI Ben LI Yuhai LU Mengya LIU Yanbing LIU |
| author_sort | Gaochao LI |
| collection | DOAJ |
| description | The demand for the analysis and application of graph data in various fields is increasing day by day.The management of large-scale graph data with complicated structure and high degree of coupling faces two challenges:one is querying speed too slow,the other is space consumption too large.Facing the problems of long query time and large space occupation in graph data management,a two-level index compression algorithm named GComIdx for graph data was proposed.GComIdx algorithm used the ordered Key-Value structure to store the associated nodes and edges as closely as possible,and constructed two-level index and hash node index for efficient attribute query and neighbor query.Furthermore,GComIdx algorithm used a graph data compressed technology to compress the graph data before it directly stored in hard disk,which could effectively reduce the storing space consumption.The experimental results show that GComIdx algorithm can effectively reduce the initialization time of the graph data calculation and the disk space occupancy of the graph data storing,meanwhile,the query time is less than common graph databases and other Key-Value storage solutions. |
| format | Article |
| id | doaj-art-27230cb0fa3d403abe5ce1ce4a4b14d1 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2018-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-27230cb0fa3d403abe5ce1ce4a4b14d12025-01-14T07:14:57ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2018-06-013910911559718888Graph compression algorithm based on a two-level index structureGaochao LIBen LIYuhai LUMengya LIUYanbing LIUThe demand for the analysis and application of graph data in various fields is increasing day by day.The management of large-scale graph data with complicated structure and high degree of coupling faces two challenges:one is querying speed too slow,the other is space consumption too large.Facing the problems of long query time and large space occupation in graph data management,a two-level index compression algorithm named GComIdx for graph data was proposed.GComIdx algorithm used the ordered Key-Value structure to store the associated nodes and edges as closely as possible,and constructed two-level index and hash node index for efficient attribute query and neighbor query.Furthermore,GComIdx algorithm used a graph data compressed technology to compress the graph data before it directly stored in hard disk,which could effectively reduce the storing space consumption.The experimental results show that GComIdx algorithm can effectively reduce the initialization time of the graph data calculation and the disk space occupancy of the graph data storing,meanwhile,the query time is less than common graph databases and other Key-Value storage solutions.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018104/two-level indexgraph compressKey-Value structureattribute queryneighbors query |
| spellingShingle | Gaochao LI Ben LI Yuhai LU Mengya LIU Yanbing LIU Graph compression algorithm based on a two-level index structure Tongxin xuebao two-level index graph compress Key-Value structure attribute query neighbors query |
| title | Graph compression algorithm based on a two-level index structure |
| title_full | Graph compression algorithm based on a two-level index structure |
| title_fullStr | Graph compression algorithm based on a two-level index structure |
| title_full_unstemmed | Graph compression algorithm based on a two-level index structure |
| title_short | Graph compression algorithm based on a two-level index structure |
| title_sort | graph compression algorithm based on a two level index structure |
| topic | two-level index graph compress Key-Value structure attribute query neighbors query |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018104/ |
| work_keys_str_mv | AT gaochaoli graphcompressionalgorithmbasedonatwolevelindexstructure AT benli graphcompressionalgorithmbasedonatwolevelindexstructure AT yuhailu graphcompressionalgorithmbasedonatwolevelindexstructure AT mengyaliu graphcompressionalgorithmbasedonatwolevelindexstructure AT yanbingliu graphcompressionalgorithmbasedonatwolevelindexstructure |