Graph cube

TLDR

Decision support on relational data has benefited from OLAP and data warehouses, but these tools are ill‑suited for the emerging class of multidimensional networks that combine attributes with complex network structure. This paper proposes Graph Cube, a data‑warehousing model that enables efficient OLAP queries over large multidimensional networks. Graph Cube extends the traditional data cube by aggregating attributes and summarizing network structure, introduces a novel cross‑boid query type, and is implemented by integrating network‑specific characteristics with established cube techniques. Experimental evaluation on real‑world datasets demonstrates that Graph Cube delivers powerful and efficient decision support for large multidimensional networks.

Abstract

We consider extending decision support facilities toward large sophisticated networks, upon which multidimensional attributes are associated with network entities, thereby forming the so-called multidimensional networks. Data warehouses and OLAP (Online Analytical Processing) technology have proven to be effective tools for decision support on relational data. However, they are not well-equipped to handle the new yet important multidimensional networks. In this paper, we introduce Graph Cube, a new data warehousing model that supports OLAP queries effectively on large multidimensional networks. By taking account of both attribute aggregation and structure summarization of the networks, Graph Cube goes beyond the traditional data cube model involved solely with numeric value based group-by's, thus resulting in a more insightful and structure-enriched aggregate network within every possible multidimensional space. Besides traditional cuboid queries, a new class of OLAP queries, crossboid, is introduced that is uniquely useful in multidimensional networks and has not been studied before. We implement Graph Cube by combining special characteristics of multidimensional networks with the existing well-studied data cube techniques. We perform extensive experimental studies on a series of real world data sets and Graph Cube is shown to be a powerful and efficient tool for decision support on large multidimensional networks.