Graph Database Solutions for Managing and Analysing Spatio-temporalData in Transportation Networks. (R-11730)

Graphs are everywhere. Graph databases are one of the most significant categories of NoSQL databases (big data). While most NoSQL databases move away from relationships, relationships are key concepts in a graph database. To date, they are most often used for applications like social network analytics, recommender systems, fraud detection etc. They are likely to play an important role in the IoT world, but for that purpose, they lack extensive spatial and temporal support. This research will focus on creating a system that is able to store and analyse spatial-temporal data with the main focus on graph-like data. More specifically adding temporal support to graph-based data models. The possibilities of using relational databases and graph databases is studied in order to obtain a generic usable Database Management System. The research also includes adding analytical capabilities to the system, for example enabling OLAP or data-mining algorithms.

Keywords:Graph databases, SPATIO-TEMPORAL DATA, Transportation networks

Disciplines:Data mining, Knowledge representation and reasoning, Database systems and architectures, Workflow, process and database management, Other information and computing sciences not elsewhere classified