GraphGen: Exploring Interesting Graphs in Relational Data
GraphGen: Exploring Interesting Graphs in Relational Data
复制标题
GraphGen：探索关系数据中有趣的图表
DOI：
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发表时间：
2015
影响因子：
2.5
通讯作者：
A. Deshpande
中科院分区：
文献类型：
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作者：
Konstantinos Xirogiannopoulos;Udayan Khurana;A. Deshpande
通过使用图算法和图分析来分析数据之间的互连结构已被证明在许多应用领域中提供了巨大的价值。然而，图形并不是当前存储大多数数据的主要选择，想要使用图形分析的用户被迫从其数据存储中提取数据，构建必要的图形，然后使用专用引擎来编写和执行其图形分析任务。这个繁琐而昂贵的过程不仅增加了使用图形分析的障碍，而且也使得难以探索和识别数据中隐藏或隐含的图形。在这里，我们展示了一个系统，称为GraphGen，使用户能够声明性地指定关系数据库上的图形提取任务，可视化地探索提取的图形，并直接或使用现有的图形库（如广泛使用的NetworkX Python库）在它们上面编写和执行图形算法。我们还演示了如何统一的提取任务和图形算法，使显着的优化，否则是不可能的。
Analyzing interconnection structures among the data through the use of graph algorithms and graph analytics has been shown to provide tremendous value in many application domains. However, graphs are not the primary choice for how most data is currently stored, and users who want to employ graph analytics are forced to extract data from their data stores, construct the requisite graphs, and then use a specialized engine to write and execute their graph analysis tasks. This cumbersome and costly process not only raises barriers in using graph analytics, but also makes it hard to explore and identify hidden or implicit graphs in the data. Here we demonstrate a system, called GraphGen, that enables users to declaratively specify graph extraction tasks over relational databases, visually explore the extracted graphs, and write and execute graph algorithms over them, either directly or using existing graph libraries like the widely used NetworkX Python library. We also demonstrate how unifying the extraction tasks and the graph algorithms enables significant optimizations that would not be possible otherwise.