A Framework and a Language for On-Line Analytical Processing on Graphs

Graphs are essential modeling and analytical objects for representing information networks. Existing approaches, in on-line analytical processing on graphs, took the first step by supporting multi-level and multi-dimensional queries on graphs, but they do not provide a semantic-driven framework and a language to support n-dimensional computations, which are frequent in OLAP environments. The major challenge here is how to extend decision support on multidimensional networks considering both data objects and the relationships among them. Moreover, one of the critical deficiencies of graph query languages, e.g. SPARQL, is the lack of support for n-dimensional computations. In this paper, we propose a graph data model, GOLAP, for online analytical processing on graphs. This data model enables extending decision support on multidimensional networks considering both data objects and the relationships among them. Moreover, we extend SPARQL to support n-dimensional computations. The approaches presented in this paper have been implemented on top of FPSPARQL, Folder-Path enabled extension of SPARQL, and experimentally validated on synthetic and real-world datasets.

[1]  Abhinav Gupta,et al.  Spreadsheets in RDBMS for OLAP , 2003, SIGMOD '03.

[2]  Alberto Abelló,et al.  A Survey of Multidimensional Modeling Methodologies , 2009, Int. J. Data Warehous. Min..

[3]  Yannis Papakonstantinou,et al.  Hypothetical Queries in an OLAP Environment , 2000, VLDB.

[4]  Charu C. Aggarwal,et al.  Relation Strength-Aware Clustering of Heterogeneous Information Networks with Incomplete Attributes , 2012, Proc. VLDB Endow..

[5]  Philip S. Yu,et al.  Mining Knowledge from Data: An Information Network Analysis Approach , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[6]  Sherif Sakr,et al.  A Query Language for Analyzing Business Processes Execution , 2011, BPM.

[7]  Jiawei Han,et al.  Graph cube: on warehousing and OLAP multidimensional networks , 2011, SIGMOD '11.

[8]  Torsten Suel,et al.  Web Information Systems Engineering - WISE 2010 - 11th International Conference, Hong Kong, China, December 12-14, 2010. Proceedings , 2010, WISE.

[9]  Hongyan Liu,et al.  C-Cubing: Efficient Computation of Closed Cubes by Aggregation-Based Checking , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[10]  Alberto Abelló,et al.  On-Line Analytical Processing , 2009, Encyclopedia of Database Systems.

[11]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[12]  Andreas Harth,et al.  Transforming statistical linked data for use in OLAP systems , 2011, I-Semantics '11.

[13]  Yizhou Sun,et al.  RankClus: integrating clustering with ranking for heterogeneous information network analysis , 2009, EDBT '09.

[14]  Erik Thomsen,et al.  OLAP Solutions - Building Multidimensional Information Systems , 1997 .

[15]  Surajit Chaudhuri,et al.  An overview of data warehousing and OLAP technology , 1997, SGMD.

[16]  Fabio Casati,et al.  Event correlation for process discovery from web service interaction logs , 2011, The VLDB Journal.

[17]  Luc De Raedt,et al.  A query language for analyzing networks , 2009, CIKM.

[18]  Philip S. Yu,et al.  Efficient Topological OLAP on Information Networks , 2011, DASFAA.

[19]  Philip S. Yu,et al.  Graph OLAP: Towards Online Analytical Processing on Graphs , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[20]  Marta Mattoso,et al.  Adaptive Virtual Partitioning for OLAP Query Processing in a Database Cluster , 2004, J. Inf. Data Manag..

[21]  Philip S. Yu,et al.  Scalable OLAP and mining of information networks , 2009, EDBT '09.

[22]  Amarnath Gupta,et al.  Tolkien: An Event Based Storytelling System , 2009, Proc. VLDB Endow..

[23]  Lorena Etcheverry,et al.  Enhancing OLAP Analysis with Web Cubes , 2012, ESWC.

[24]  Peter A. Flach,et al.  Evaluation Measures for Multi-class Subgroup Discovery , 2009, ECML/PKDD.

[25]  Alberto Abelló,et al.  Online Analytical Processing , 2011, Encyclopedia of Cryptography and Security.

[26]  Daniele Braga,et al.  C-SPARQL: SPARQL for continuous querying , 2009, WWW '09.

[27]  Jignesh M. Patel,et al.  Efficient aggregation for graph summarization , 2008, SIGMOD Conference.

[28]  Charu C. Aggarwal,et al.  Managing and Mining Graph Data , 2010, Managing and Mining Graph Data.

[29]  Shuo Wang,et al.  Refining Graph Partitioning for Social Network Clustering , 2010, WISE.

[30]  Lora Aroyo,et al.  The Semantic Web: Research and Applications , 2009, Lecture Notes in Computer Science.

[31]  Philip S. Yu,et al.  Graph indexing: a frequent structure-based approach , 2004, SIGMOD '04.

[32]  Yizhou Sun,et al.  Graph Regularized Transductive Classification on Heterogeneous Information Networks , 2010, ECML/PKDD.