Analytical queries on semantic trajectories using graph databases

This article studies the analysis of moving object data collected by location-aware devices, such as GPS, using graph databases. Such raw trajectories can be transformed into so-called semantic trajectories, which are sequences of stops that occur at "places of interest." Trajectory data analysis can be enriched if spatial and non-spatial contextual data associated with the moving objects are taken into account, and aggregation of trajectory data can reveal hidden patterns within such data. When trajectory data are stored in relational databases, there is an "impedance mismatch" between the representation and storage models. Graphs in which the nodes and edges are annotated with properties are gaining increasing interest to model a variety of networks. Therefore, this article proposes the use of graph databases (Neo4j in this case) to represent and store trajectory data, which can thus be analyzed at different aggregation levels using graph query languages (Cypher, for Neo4j). Through a real-world public data case study, the article shows that trajectory queries are expressed more naturally on the graph-based representation than over the relational alternative, and perform better in many typical cases.