If you want to learn more about Citus on Microsoft Azure, read this post about Hyperscale (Citus) on Azure Database for PostgreSQL.
GPS has become part of our daily life. GPS is in cars for navigation, in smartphones helping us to find places, and more recently GPS has been helping us to avoid getting infected by COVID-19. Managing and analyzing mobility tracks is the core of my work. My group in Université libre de Bruxelles specializes in mobility data management. We build an open source database system for spatiotemporal trajectories, called MobilityDB. MobilityDB adds support for temporal and spatiotemporal objects to the Postgres database and its spatial extension, PostGIS. If you’re not yet familiar with spatiotemporal trajectories, not to worry, we’ll walk through some movement trajectories for a public transport bus in just a bit.
One of my team’s projects is to develop a distributed version of MobilityDB. This is where we came in touch with the Citus extension to Postgres and the Citus engineering team. This post presents issues and solutions for distributed query processing of movement trajectory data. GPS is the most common source of trajectory data, but the ideas in this post also apply to movement trajectories collected by other location tracking sensors, such as radar systems for aircraft, and AIS systems for sea vessels.Keep reading