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.
- C. C. Aggarwal and H. Wang. Managing and Mining Graph Data. Springer, 2010. Google ScholarDigital Library
- T. Baird. The Truth About Facebook. Emereo Pty Limited, 2009.Google Scholar
- D. Burdick, A. Doan, R. Ramakrishnan, and S. Vaithyanathan. OLAP over imprecise data with domain constraints. In VLDB, pages 39--50, 2007. Google ScholarDigital Library
- S. Chaudhuri and U. Dayal. An overview of data warehousing and OLAP technology. SIGMOD Rec., 26(1):65--74, 1997. Google ScholarDigital Library
- C. Chen, X. Yan, F. Zhu, J. Han, and P. S. Yu. Graph OLAP: Towards online analytical processing on graphs. In ICDM, pages 103--112, 2008. Google ScholarDigital Library
- D. Gibson, R. Kumar, and A. Tomkins. Discovering large dense subgraphs in massive graphs. In VLDB, pages 721--732, 2005. Google ScholarDigital Library
- J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, and H. Pirahesh. Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data Min. Knowl. Discov., 1(1):29--53, 1997. Google ScholarDigital Library
- P. J. Haas, J. F. Naughton, S. Seshadri, and L. Stokes. Sampling-based estimation of the number of distinct values of an attribute. In VLDB, pages 311--322, 1995. Google ScholarDigital Library
- J. Han, Y. Chen, G. Dong, J. Pei, B. W. Wah, J. Wang, and Y. D. Cai. Stream cube: An architecture for multi-dimensional analysis of data streams. Distrib. Parallel Databases, 18(2):173--197, 2005. Google ScholarDigital Library
- V. Harinarayan, A. Rajaraman, and J. D. Ullman. Implementing data cubes efficiently. In SIGMOD, pages 205--216, 1996. Google ScholarDigital Library
- H. Karloff and M. Mihail. On the complexity of the view-selection problem. In PODS, pages 167--173, 1999. Google ScholarDigital Library
- K. LeFevre and E. Terzi. GraSS: Graph structure summarization. In SDM, pages 454--465, 2010.Google ScholarCross Ref
- J. Leskovec and C. Faloutsos. Sampling from large graphs. In KDD, pages 631--636, 2006. Google ScholarDigital Library
- X. Li, J. Han, and H. Gonzalez. High-dimensional OLAP: a minimal cubing approach. In VLDB, pages 528--539, 2004. Google ScholarDigital Library
- C. X. Lin, B. Ding, J. Han, F. Zhu, and B. Zhao. Text cube: Computing IR measures for multidimensional text database analysis. In ICDM, pages 905--910, 2008. Google ScholarDigital Library
- E. Lo, B. Kao, W.-S. Ho, S. D. Lee, C. K. Chui, and D. W. Cheung. OLAP on sequence data. In SIGMOD, pages 649--660, 2008. Google ScholarDigital Library
- K. Morfonios, S. Konakas, Y. Ioannidis, and N. Kotsis. ROLAP implementations of the data cube. ACM Comput. Surv., 39(4):12, 2007. Google ScholarDigital Library
- S. Navlakha, R. Rastogi, and N. Shrivastava. Graph summarization with bounded error. In SIGMOD, pages 419--432, 2008. Google ScholarDigital Library
- S. Navlakha, M. C. Schatz, and C. Kingsford. Revealing biological modules via graph summarization. Journal of Computational Biology, 16:253--264, 2009.Google ScholarCross Ref
- M. Newman. Networks: An Introduction. Oxford University Press, 2010. Google ScholarDigital Library
- Y. Qi, K. S. Candan, J. Tatemura, S. Chen, and F. Liao. Supporting OLAP operations over imperfectly integrated taxonomies. In SIGMOD, pages 875--888, 2008. Google ScholarDigital Library
- H. Thomases. Twitter Marketing: An Hour a Day. Wiley, 2010. Google ScholarDigital Library
- Y. Tian, R. A. Hankins, and J. Patel. Efficient aggregation for graph summarization. In SIGMOD, pages 567--580, 2008. Google ScholarDigital Library
- M. Wattenberg. Visual exploration of multivariate graphs. In CHI, pages 811--819, 2006. Google ScholarDigital Library
- N. Zhang, Y. Tian, and J. M. Patel. Discovery-driven graph summarization. In ICDE, pages 880--891, 2010.Google ScholarCross Ref
- Y. Zhou, H. Cheng, and J. X. Yu. Graph clustering based on structural/attribute similarities. PVLDB, 2(1):718--729, 2009. Google ScholarDigital Library
Index Terms
- Graph cube: on warehousing and OLAP multidimensional networks
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