Abstract: Provided is an aggregation index structure and an aggregation index method for improving aggregation query efficiency. The aggregation index partitions streaming data through two dimensions of grouping and slicing, and then aggregates the partitioned data. The structure includes index metadata, a sliced data list and a detailed data store; the aggregation index method includes three parts: aggregation index definition, aggregation index creation and aggregation index query. The aggregation index structure and method provided by the present disclosure can greatly improve the efficiency of aggregation query, support the ad hoc aggregation query of PB-level data, complete the aggregation query of a large amount of data in seconds, support the insertion of new data at any time, and return the query results in minutes or even seconds latency in the event of changes of the query range conditions.

Abstract: Provided is a method for reverse real-time matching based on event-driven graph patterns. The method includes definition of a graph pattern, establishment of reverse matching architecture and a reverse matching method; and the graph pattern is used for describing a spatial connection relationship among vertices and edges in a graph in a reverse order and attribute constraints thereof, and includes a linked list of matching steps and matching contexts. Reverse real-time matching of the graph pattern is realized according to the reverse matching architecture, and the problem of event-driven subgraph matching in a real-time scene is solved.

Abstract: Provided is a method for reverse real-time matching based on event-driven graph patterns. The method includes definition of a graph pattern, establishment of reverse matching architecture and a reverse matching method; and the graph pattern is used for describing a spatial connection relationship among vertices and edges in a graph in a reverse order and attribute constraints thereof, and includes a linked list of matching steps and matching contexts. Reverse real-time matching of the graph pattern is realized according to the reverse matching architecture, and the problem of event-driven subgraph matching in a real-time scene is solved.

Abstract: Provided is an aggregation index structure and an aggregation index method for improving aggregation query efficiency. The aggregation index partitions streaming data through two dimensions of grouping and slicing, and then aggregates the partitioned data. The structure includes index metadata, a sliced data list and a detailed data store; the aggregation index method includes three parts: aggregation index definition, aggregation index creation and aggregation index query. The aggregation index structure and method provided by the present disclosure can greatly improve the efficiency of aggregation query, support the ad hoc aggregation query of PB-level data, complete the aggregation query of a large amount of data in seconds, support the insertion of new data at any time, and return the query results in minutes or even seconds latency in the event of changes of the query range conditions.

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.