Several modern day problems need to deal with large amounts of spatio-temporal data. As such, in order to meet the application requirements, more and more systems are adapting to the specificities of those data. The most prominent case is perhaps the data storage systems, that have developed a large number of functionalities to efficiently support spatio-temporal data operations. This work is motivated by the question of which of those data storage systems is better suited to address the needs of industrial applications. In particular, the work conducted, set to identify the most efficient data store system in terms of response times, comparing two of the most representative of the two categories (NoSQL and relational), i.e. MongoDB and PostgreSQL. The evaluation is based upon real, business scenarios and their subsequent queries as well as their underlying infrastructures and concludes in confirming the superiority of PostgreSQL in almost all cases with the exception of the polygon intersection queries. Furthermore, the average response time is radically reduced with the use of indexes, especially in the case of MongoDB.
This work was supported by the MASTER Project through the European Union’s Hori-zon 2020 research and innovation program under Marie-Slodowska Curie under Grant 777695. The work reflects only the author’s view and that the EU Agency is not responsible for any use that may be made of the information it contains.
This work has been also developed in the frame of the SmartShip project, which have received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Slodowska-Curie grant agreement No 823916 respectively.
This work was supported in part by MarineTraffic which provided data access for research purposes.
