Skip to content

Analytics-aware graph database modeling

Graphs are a fundamental structure for modeling many real world domains and applications. They have emerged in various fields such as social, informational and transportation networks. The hetero geneity and dynamicity of these networks pose challenges to traditional techniques for data modeling, storage and analysis of data.

Managing graph-structured data using native graph structures and algorithms is the key for its efficient analysis. Therefore, the graph should be modeled using nodes and edges, and explored using graph algorithms, such as pattern matching and k-neighborhood.

In this paper, we introduce a novel model for management of graph data. The aim of our model is to provide analysts with a set of simple, well-defined, and adaptable components to perform complex graph modeling and analysis tasks.

Amine Ghrab, Oscar Romero, Sabri Skhiri, and Esteban Zimanyi, Analytics-Aware Graph Database Modeling, EURA NOVA technical series.

Click here to access the paper.

Releated Posts

IEEE Big Data 2023 – A Summary

Our CTO, Sabri Skhiri, recently travelled to Sorrento for IEEE Big Data 2023. In this article, Sabri explores for you the various keynotes and talks that took place during the
Read More

Robust ML Approach for Screening MET Drug Candidates in Combination with Immune Checkpoint Inhibitors

Present study highlights the significance of dataset size in ICI microbiota models and presents a methodology to enhance the performances of a multi-cohort-based ML approach.
Read More