Graph cube: on warehousing and OLAP multidimensional networks
Venue
SIGMOD - Proceedings of the 2011 International Conference on Management of Data, ACM, New York, NY
Publication Year
2011
Authors
Peixiang Zhao, Xialolei Li, Dong Xin, Jiawei Han
BibTeX
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.
