Graph BI & Analytics: Current State and Future Challenges

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 and analytics framework.

Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications

“…These big data applications are focused towards improving decision making and operational efficiency of organizations. Applications associated with this domain include data management [218,96,280], problemspecific applications for areas like financial services [232] and service performance management [233], and general applications [69,286,150,167,181] for improving the operational efficiency of the system. It is important to mention that applications related to industries and company-specific applications like logs analysis for IT companies are not included in business intelligence.…”

TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes

“…Li et al (Li, Yu, Zhao, Xie, & Lin, 2011), proposed conceptual models for designing and querying graph data warehouse systems. In (Skhiri & Jouili, 2013;Ghrab et al, 2018), authors suggested novel architecture for graph BI systems that leverages large graph mining and warehousing. This paper goes in-line with these research directions, and attempts to provide a foundation for extending decision-making systems, and particularly OLAP, with graph analytics capabilities, while paying particular attention to the few cases of possible correspondence between graph and ROLAP cubes.…”

“…Many approaches were proposed to address the graph data warehousing challenge (Ghrab, Romero, Jouili, & Skhiri, 2018;Queiroz-Sousa & Salgado, 2019). These efforts laid the foundation for multidimensional modeling and analysis of graphs.…”

Advances on Data Management and Information Systems

“…Such data not only need to be efficiently stored but also efficiently analyzed. Therefore, some OLAP-like analysis approaches from graph data have been recently proposed, e.g., Chen et al (2020), Ghrab et al (2018), Ghrab et al (2021), and Schuetz et al (2021). Thus, combining graph and OLAP technologies offers ways of analyzing graphs in a manner already well accepted by the industry (Richardson et al, 2021).…”