Using entropy metrics for pruning very large graph cubes
作者:
Highlights:
• Propose entropy metrics for evaluating interactions within large graph datasets.
• Propose techniques to weigh possible OLAP drill-down operations on graph cubes.
• Present an efficient algorithm for fast selection of aggregated sub-graphs.
• Evaluation of the presented techniques in large real and synthetic graph datasets.
摘要
•Propose entropy metrics for evaluating interactions within large graph datasets.•Propose techniques to weigh possible OLAP drill-down operations on graph cubes.•Present an efficient algorithm for fast selection of aggregated sub-graphs.•Evaluation of the presented techniques in large real and synthetic graph datasets.
论文关键词:Graph cube,Entropy,Big data
论文评审过程:Received 4 December 2017, Revised 25 September 2018, Accepted 19 November 2018, Available online 22 November 2018, Version of Record 27 November 2018.
论文官网地址:https://doi.org/10.1016/j.is.2018.11.007