关键词: GraphOLAP, 数据立方体, 元路径, Spark

Abstract: With the rapid development of the Internet and the increasing of computer applications, a large number of graph data especially social networks are generated. Multi-dimensional information networks have become a common way to represent these data. However in the multi-dimensional information networks there are multiple types of nodes and attributes. How to process the analysis of multi-view and multi-granularity and mine the hidden information has become the focus of current research. Graph online analytical processing (GraphOLAP) can process a quick online analysis and query operation of graph data. With the existing achievement of GraphOLAP, this paper proposes a new Graph-Cube framework according to the characteristics of multi-dimensional information network. This paper introduces the concept of meta-path and uses main node to guide the aggregation of the meta-path. Then this paper uses meta-path to guide the roll-up/drill-down operation of the network and proposes attributes transform and homogeneous transform operation of the Graph-Cube. Finally, this paper discusses the materialization strategy and implements the framework in Spark. The experimental results on real and simulation datasets prove the efficiency and effectiveness of the proposed framework.

Key words:  GraphOLAP, Graph-Cube, meta-path, Spark