In this section you will find EURA NOVA's scientific publications and reports.
Our paper “Graph BI & Analytics: Current State and Future Challenges” has been accepted for publication at the 20th International Conference on Big Data Analytics and Knowledge Discovery, taking place in Regensburg, Germany.
The paper presents the state of the art of graph BI & analytics, with a focus on graph warehousing. We survey the topics of graph modelling, management, querying, and processing in graph warehouses. Then we conclude by discussing future research directions for solving complex graph problems, building native graph components and intelligent techniques to assist end-users in building and analysing the graph.
More importantly, the paper calls for the development of intelligent, efficient and industry-grade graph data warehousing systems to support the structure-driven management and analytics of data efficiently. While adopting a template that is similar to the traditional BI systems, the graph BI that is presented here extends current systems with graph analytics capabilities that deliver graph-derived insights. The paper will be presented in September at DaWak 2018, but you can already read the abstract bellow. If you wish to go deeper into the subject, don’t hesitate to contact our research department at firstname.lastname@example.org.
Abstract. In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI & analytics framework.