Graph data temporal evolutions: From conceptual modelling to implementation

Edited by Renata Guizzardi, Anna Perini, Samira CherfiInternational audienceGraph data management systems are designed for managing highly interconnected data. However, most of the existing work on the topic does not take into account the temporal dimension of such data, even though they may change over time: new interconnections, new internal characteristics of data (etc.). For decision makers, these data changes provide additional insights to explain the underlying behaviour of a business domain. The objective of this paper is to propose a complete solution to manage temporal interconnected data. To do so, we propose a new conceptual model of temporal graphs. It has the advantage of being generic as it captures the different kinds of changes that may occur in interconnected data. We define a set of translation rules to convert our conceptual model into the logical property graph. Based on the translation rules, we implement several temporal graphs according to benchmark and real-world datasets in the Neo4j data store. These implementations allow us to carry out a comprehensive study of the feasibility and usability (through business analyses), the efficiency (saving up to 99% query execution times comparing to classic approaches) and the scalability of our solution