Fast processing method of massive time-series data based on aggregated edge and time-series aggregated edge

Abstract

A fast processing method of massive time-series data based on aggregated edges and time-series aggregated edges implements fast and real-time processing based on an association of a graph data structure under a massive data pattern. In terms of incremental flow calculation based on time windows, the present disclosure proposes an innovative data structure of 'aggregated edge' and “time-series aggregated edge”, which is suitable for data modeling of real-time dynamic graph. The present disclosure introduces a time-series graph query language, which adds descriptive semantics of time series information, and not only supports basic queries based on vertices, edges, and attributes, but also can implement users' graph queries for index calculation results in a time window. The graph query includes graph matching and graph filtering. The present disclosure is especially suitable for fields such as marketing and real-time risk control based on massive data mining and has good timeliness control and high scalability.