图中结构重要数据的OLAP
OLAP on Structurally Significant Data in Graphs
摘要：
利用联机分析处理（OLAP）对图形进行汇总数据分析是目前比较流行的方法。然而，由于数据的高维性和庞大的规模，并不容易决定哪些数据应该被聚合的OLAP分析。虽然冰山立方体是有用的，但它不知道的重要性，尺寸值的结构图。在本文中，我们提出了一个结构的意义，SS，措施，以确定在每个维度的结构上显着的维度值。这导致结构感知修剪。然后，我们提出了一个算法，iGraphCubing，计算的图形立方体分析的结构上有意义的数据，使用所提出的措施。我们在真实和合成数据集上评估了所提出的想法，并观察到非常令人鼓舞的结果。
英文摘要：
Summarized data analysis of graphs using OLAP (Online Analytical Processing) is very popular these days. However due to high dimensionality and large size, it is not easy to decide which data should be aggregated for OLAP analysis. Though iceberg cubing is useful, but it is unaware of the significance of dimensional values with respect to the structure of the graph. In this paper, we propose a Structural Significance, SS, measure to identify the structurally significant dimensional values in each dimension. This leads to structure aware pruning. We then propose an algorithm, iGraphCubing, to compute the graph cube to analyze the structurally significant data using the proposed measure. We evaluated the proposed ideas on real and synthetic data sets and observed very encouraging results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
