BIIIG: Enabling business intelligence with integrated instance graphs

We propose a new graph-based framework for business intelligence called BIIIG supporting the flexible evaluation of relationships between data instances. It builds on the broad availability of interconnected objects in existing business information systems. Our approach extracts such interconnected data from multiple sources and integrates them into an integrated instance graph. To support specific analytic goals, we extract subgraphs from this integrated instance graph representing executed business activities with all their data traces and involved master data. We provide an overview of the BIIIG approach and describe its main steps. We also present initial results from an evaluation with real ERP data.

[1]  Yannis Kotidis,et al.  Extending the data warehouse for service provisioning data , 2006, Data Knowl. Eng..

[2]  Charu C. Aggarwal,et al.  When will it happen?: relationship prediction in heterogeneous information networks , 2012, WSDM '12.

[3]  Wolfgang Lehner,et al.  SynopSys: large graph analytics in the SAP HANA database through summarization , 2013, GRADES.

[4]  Falko Menge.  Enterprise Service Bus , 2007 .

[5]  David A Chappell,et al.  Enterprise Service Bus , 2004 .

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

[7]  Sherif Sakr,et al.  Graph Data Management: Techniques and Applications , 2011, Graph Data Management.

[8]  Yves Lechevallier,et al.  DB2SNA: An All-in-One Tool for Extraction and Aggregation of Underlying Social Networks from Relational Databases , 2013, The Influence of Technology on Social Network Analysis and Mining.

[9]  Dirk Fahland,et al.  Automatic Discovery of Data-Centric and Artifact-Centric Processes , 2012, Business Process Management Workshops.

[10]  Wolfgang Lehner,et al.  The Graph Story of the SAP HANA Database , 2013, BTW.

[11]  Guillaume Blin,et al.  A survey of RDF storage approaches , 2012, ARIMA J..

[12]  Dung N. Lam,et al.  Graph-Based Data Warehousing Using the Core-Facets Model , 2011, ICDM.

[13]  Yannis Kotidis,et al.  Business intelligence on complex graph data , 2012, EDBT-ICDT '12.

[14]  Jeremy J. Carroll,et al.  Resource description framework (rdf) concepts and abstract syntax , 2003 .

[15]  Erhard Rahm,et al.  Schema Matching and Mapping , 2013, Schema Matching and Mapping.

[16]  Bin Wu,et al.  HMGraph OLAP: a novel framework for multi-dimensional heterogeneous network analysis , 2012, DOLAP '12.

[17]  Marko A. Rodriguez,et al.  Constructions from Dots and Lines , 2010, ArXiv.

[18]  Dirk Fahland,et al.  Many-to-Many: Some Observations on Interactions in Artifact Choreographies , 2011, ZEUS.

[19]  Cláudio T. Silva,et al.  Visual summaries for graph collections , 2013, 2013 IEEE Pacific Visualization Symposium (PacificVis).

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

[21]  Renzo Angles,et al.  A Comparison of Current Graph Database Models , 2012, 2012 IEEE 28th International Conference on Data Engineering Workshops.

[22]  Jiawei Han,et al.  Mining Graph Patterns , 2014, Frequent Pattern Mining.