P&D Graph Cube:Model and Parallel Materialization for Multidimensional Heterogeneous Network
We consider extending decision support facilities toward large-scale sophisticated networks,upon which multidimensional attributes are associated variety of entities,forming the so-called Multidimensional heterogeneous network.Multidimensional heterogeneous network has become important tool for modelling information networks,meanwhile,OLAP(Online Analytical Processing)has proven to be effective tool on relational data,however,it is computationally an enormous challenge to manage and analyse Multidimensional heterogeneous network to support effective decision making and OLAP operations.In this paper,we enrich the semantics of the traditional OLAP and propose a P&D(Path and Dimension)Graph Cube Model framework,which can support multi-type queries and graph OLAP operations.Furthermore,on the basis of P&D Graph Cube Model,we divide the graph cube materialization into two parts described as Path related Materialization and Dimension related Materialization.On Path Materialization,by taking account of structure summarization,we design the Pathrelated Materialization Algorithm based on the definition of relation path set,thus resulting in a more insightful and structure-enriched network.On Dimension Materialization,we provide a Graph Frag-shell Algorithm to compute graph shell fragments for fast high-dimension Graph OLAP.Finally,we implement the related algorithms on Spark.The results of experiments on real data set confirm the effectiveness and scalable of P&D Graph Cube Model with materialization algorithms.
graph cube materialization OLAP relation path
Xinyu Wu Bin Wu Bai Wang
Beijing Key Laboratory of Intelligent Telecommunications software and Multimedia Beijing University of Posts and Telecommunications Beijing,China
国际会议
南京
英文
95-104
2017-10-12（万方平台首次上网日期，不代表论文的发表时间）