Let High-level Graph Queries Be Parallel Efficient: An Approach Over Structural Recursion On Pregel
Let High-level Graph Queries Be Parallel Efficient: An Approach Over Structural Recursion On Pregel
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DOI：
10.2197/ipsjjip.24.928
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Chong Li;Le-Duc Tung;Xiaodong Meng;Zhenjiang Hu
中科院分区：
文献类型：
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作者：
Chong Li;Le-Duc Tung;Xiaodong Meng;Zhenjiang Hu
如今，图表在管理大数据中起着重要作用。支持声明的图形查询是有效操纵图形数据库的最关键部分之一。已经研究了用于图查询和图形转换的结构递归。但是，以前关于图形结构递归的大多数研究都不能利用并行计算的实用性。用于平行评估结构递归的批量语义仍然施加了许多限制并行查询性能的约束。在本文中，我们提出了一个框架，该框架系统地从高级声明的图形查询中生成结构递归函数，然后在我们的框架上，在我们的框架上有效地评估了生成的函数。因此，我们的解决方案放松了发展有效的结构递归功能的复杂性。
Graphs play an important role today in managing big data. Supporting declarative graph queries is one of the most crucial parts for efficiently manipulating graph databases. Structural recursion has been studied for graph querying and graph transformations. However, most of the previous studies about graph structural recursion do not exploit in practical the power of parallel computing. The bulk semantics, which is used for parallel evaluation of structural recursion, still impose many constraints that limit the performance of querying in parallel. In this paper, we propose a framework that systematically generates structural recursive functions from high-level declarative graph queries, then the generated functions are evaluated efficiently on our framework on top of the Pregel model. Therefore, the complexity in developing efficient structural recursive functions is relaxed by our solution.