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支持动态图数据的子图查询方法 被引量:4

Subgraph Queries over Dynamic Graph Data
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摘要 近年来,子图查询作为图数据库管理的一项重要课题受到国内外学者的广泛关注。在现实应用中大部分图数据是频繁更新的,而现有方法对图数据的频繁更新的维护代价较高。子图查询本身就是NP完全问题,在动态图数据上子图查询问题就变得更加困难。针对上述问题,提出了支持动态图数据的子图查询方法。该方法首先构造出每张图的拓扑层次序列作为索引,在序列中加入标号以便数据更新后对索引进行维护,再根据序列间的匹配关系过滤出候选集合,最后采用图同构算法验证候选集中的图,最终得到结果集合。该方法的索引构造简单且体积小,并且在图数据库更新后无需重构索引,不仅支持动态图数据上的子图查询,在静态图数据上也表现出良好的性能。 Subgraph query has attracted wide attention of scholars as an important topic of graph database manage-ment in recent years. In real life most of the graph data are frequently updated, and maintenance cost of the existing methods for frequently updated graph data is higher. Subgraph query is a NP complete problem, while frequently updated subgraph queries will become more difficult. In light of the above problem, this paper proposes the method of subgraph queries over dynamic graph data. Firstly the method constructs every graph’s topological hierarchical sequence as index, then filters out the candidate set according to the sequence matching relationship, and finally uses graph isomorphism algorithm to verify the candidate set and gets final result set. The index is constructed easily and its size is small. The method doesn’t need to rebuild index after database updating, and it not only supports sub-graph queries on dynamic graph data, but also shows good performance on static graph data.
出处 《计算机科学与探索》 CSCD 2014年第2期139-149,共11页 Journal of Frontiers of Computer Science and Technology
基金 国家自然科学基金 Grant Nos.61173031 61272178 国家自然科学基金海外及港澳学者合作基金 Grant No.61129002 高等学校博士学科点专项科研基金 Grant No.20110042110028 中央高校基本科研业务费专项资金 Grant Nos.N120504001 N110404015~~
关键词 子图查询 动态图数据 拓扑序列 图索引 subgraph query dynamic graph data topological sequence graph index
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  • 1郭聪敏.图集的子图查询算法研究[D].秦皇岛:燕山大学,2011.
  • 2马帅,李佳,刘旭东,等.图查询:社会计算时代的新型搜索[J].计算机学会通讯,2012,8(11):26-31.
  • 3ADIYA 13, BHALOTIA G, CHAKRABARTI S, et aL BANKS: browsing and keyword searching in relational databases[ C]// Pro- ceedings of the 28th International Conference on Very Large Data- bases. New York: VLDB Endowment, 2002:1083 - 1086.
  • 4AGRAWAL S, CHAUDHURI S, DAS G. DBXplorer: a system for keyword-based search over relational databases[ C] // Proceedings of the 18th International Conference on Data Engineering. Washington, DC: IEEE Computer Society, 2002:5 - 16.
  • 5DING B, XU J, YU S, et al. Finding top-k rain-cost connected trees in databases[ C]//Proceedings of the 23rd International Con- ference on Data Engineering. Washington, DC: IEEE Computer So- ciety, 2007:836 - 845.
  • 6GOLENBERG K, KIMELFELD B, SAGIV Y. Keyword proximity search in complex data graphs[ C] // Proceedings of the 2008 ACM Special Interest Group on Management of Data. New York: ACM, 2008:927 - 940.
  • 7GUO L, SHAO F, BOTEV C, et al. Xrank: ranked keyword search over XML documents[ C]// Proceedings of the 2006 ACM Special Interest Group on Management of Data. New York: ACM, 2003:16 -27.
  • 8LI G, FENG J, BENG C O, et al. EASE: an effective 3-in-1 key- word search method over heterogeneous sources[ J]. Information Sys- tems, 2011, 36(2): 248-266.
  • 9KIM S, LEE W, ARORA N R, et al. Retrieving keyworded subgraphs with graph ranking score[ J]. Expert Systems with Applica- tions, 2012, 39(5) :4647 -4656.
  • 10HE H, WANG H, YANG J, et al. BLINKS: ranked keyword sear- ches on graphs[ C]//Proceedings of the 2007 ACM Special Interest Group on Management of Data. New York: ACbl, 2007:305 - 316.

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