GreyCat: Efficient what-if analytics for data in motion at scale

作者：

Highlights：

• Existing analytics fail to effectively explore the effects of what-if decisions.

• We combine graphs and time series into multi-dimensional models (Many-Worlds Graphs).

• These are able to update thousands of parallel worlds composed of millions of nodes.

• We benchmark our Many-Worlds Graph open source implementation, called GreyCat.

摘要

•Existing analytics fail to effectively explore the effects of what-if decisions.•We combine graphs and time series into multi-dimensional models (Many-Worlds Graphs).•These are able to update thousands of parallel worlds composed of millions of nodes.•We benchmark our Many-Worlds Graph open source implementation, called GreyCat.

论文关键词：What-if analysis,Time-evolving graphs,Predictive analytics,Graph processing

论文评审过程：Received 20 June 2017, Revised 21 December 2018, Accepted 7 March 2019, Available online 12 March 2019, Version of Record 19 March 2019.

论文官网地址：https://doi.org/10.1016/j.is.2019.03.004