Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
Spark ȯ°æ¿¡¼ ´ë¿ë·® ±×·¡ÇÁ À¯»ç ¼ºê ±×·¡ÇÁ ¸ÅĪ ±â¹ý |
¿µ¹®Á¦¸ñ(English Title) |
Approximate Sub-Graph Matching Scheme for Large-Scale Graph Data in Spark Environments |
ÀúÀÚ(Author) |
ÀÓÁ¾ÅÂ
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Seok Jong Yu
Kyoungsoo Bok
Jaesoo Yoo
Jongtae Lim
Dojin Choi
Dongmin Seo
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¿ø¹®¼ö·Ïó(Citation) |
VOL 24 NO. 09 PP. 0463 ~ 0469 (2018. 09) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
With the development of various experiment tools, the amount of science data generated for fields such as astronomy, cosmology, biology, and humanities has increased rapidly. Among these science data, graph data occupies a very high proportion. Approximate sub-graph matching is the analytic technique that searches for the similar subgraphs with a query graph in target graph. However, the existing approximate subgraph matching schemes have limits to process large scale network data because they do not consider the distributed computing environments. In this paper, we propose an approximate subgraph matching scheme for large-scale graph data in distributed computing environments. The proposed scheme uses big data processing platform to process the large-scale graph data. And the proposed scheme improves the performance of the query processing using efficiently pruning algorithm and similarity calculate algorithm.
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Å°¿öµå(Keyword) |
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¾ÆÆÄÄ¡ ½ºÆÄÅ©
±×·¡ÇÁ ºÐ¼®
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approximate subgraph matching
apache spark
graph analysis
large-sclae graph
bigdata
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