Abstract
Using graph data models for business intelligence applications is a novel and promising approach. In contrast to traditional data warehouse models, graph models enable the mining of relationship patterns. In our prior work, we introduced an approach to graph-based data integration and analytics called BIIIG (Business Intelligence with Integrated Instance Graphs). In this work, we compare state-of-the-art systems for graph data management and analytics with regard to the support for our approach in Big Data scenarios. To exemplify the analytical value of graph models for business intelligence, we propose an analytical workflow to extract knowledge from graph-integrated business data. Finally, we show how we use Gradoop, a novel framework for distributed graph analytics, to implement our approach.
About the authors
André Petermann studied Information Technology at the University of the West of Scotland (BSc 2009) and Multimedia Technology at the Leipzig University of Applied Sciences (Dipl.-Ing.(FH) 2011). He gained in-depth experience in design and development of business intelligence solutions with COMPAREX AG (2009–2012). In 2013, Petermann became a researcher in the field of graph analytics for business intelligence at Leipzig University Database Research Group.
Universität Leipzig, Institut für Informatik, D-04109 Leipzig, Germany
Martin Junghanns studied Computer Science at the Leipzig University (MSc 2014). While working for the NoSQL database vendor sones GmbH (2009–2011) and SAP (2013–2014) he gained in-depth knowledge about database systems, software development and graph data management. Since 2014, Junghanns is working as a researcher in the field of distributed graph analytics and management at Leipzig University Database Research Group. He is an active contributor in Big Data related Open Source projects.
Universität Leipzig, Institut für Informatik, D-04109 Leipzig, Germany
Acknowledgement
This work is partially funded by the German Federal Ministry of Education and Research under project ScaDS Dresden/Leipzig (BMBF 01IS14014B).
©2016 Walter de Gruyter Berlin/Boston
