Integrating Graph Database and Complex Network for Space–Time Evolution Analysis in Airline Industry

Analyzing transport networks in the airline industry is challenging. This industry is data-driven since it requires robustand efficient analytics tools to manage Big data, which are often missing, and thus get a quick response to stay moreattractive in the market. Graph databases coupled with network theory offer the possibility to analyze the structureof the network to understand complex relationships and inter-dependencies between different data after modeling andstoring the data as a graph in the graph database. As a result, they offer an efficient and flexible way to captureand follow the network’s dynamic topology and mobility pattern, potentially allowing us to extract interesting newknowledge in the decision-making process. This study proposes a complete framework to model, store and analyze airtransport network data based on graph theory, graph database, and network theory. A significant part of this work is tomodel a large-scale network in terms of structural complexity and evolution in space and time of the network while alsoexploiting its benefits in network analysis. The data provided are stored in the NoSQL database: MongoDB. The graphdatabase-oriented solution assesses the scalability compared to MongoDB. Moreover, it helps visualize network changesin various case scenarios to facilitate searching, tracking, and updating data that change over time