Distributed Spatiotemporal Trajectory Query Processing in SQL

Abstract

Nowadays, the collection of moving object data is significantly increasing due to the ubiquity of GPS-enabled devices. Managing and analyzing this kind of data is crucial in many application domains, including social mobility, pandemics, and transportation. In previous work, we have proposed the MobilityDB moving object database system. It is a production-ready system, that is built on top of PostgreSQL and PostGIS. It accepts SQL queries and offers most of the common spatiotemporal types and operations. In this paper, to address the scalability requirement of big data, we provide an architecture and an implementation of a distributed moving object database system based on MobilityDB. More specifically, we define: (1) an architecture for deploying a distributed MobilityDB database on a cluster using readily available tools, (2) two alternative trajectory data partitioning and index partitioning methods, and (3) a query optimizer that is capable of distributing spatiotemporal SQL queries over multiple MobilityDB instances. The overall outcome is that the cluster is managed in SQL at the run-time and that the user queries are transparently distributed and executed. This is validated with experiments using a real dataset, which also compares MobilityDB with other relevant systems. CCS CONCEPTS • Information systems → Data management systems.

A Survey on Big Data Processing Frameworks for Mobility Analytics

“…(global: two R-trees built similarity join use of STR for partitioning on MBR of first and last points respectively, local: trie-like index on selected points) MobilityDB Hierarchical partitioning, Local indexing Range, k-NN, not [10,11,67] multidimensional partitioning based on PostGIS distributed joins Table 2: Overview of spatial and spatio-temporal parallel processing frameworks.…”

“…MobilityDB [10,11,67] provides a distributed system that scales PostgreSQL horizontally over multiple workers. It uses two partitioning schemes: hierarchical (first based on time, then space) and multidimensional (where the 3D space is partitioned to cells) to distribute the data to workers in a load-balanced way.…”

MobilityDB

“…https://anitagraser.com/projects/time-manager/ cover:(1) Visually explore individual moving point and moving regions objects, (2) Visualize the spatial trajectories of moving points, and the traversed areas of moving regions, (3) Visually analyze the topological relationships of mixture of moving and spatial objects, (4) Create aggregate visualization such as flow maps.2.4 Part III: DB Distribution (20 minutes)MobilityDB integrates with Citus[7] for distributing moving object data and queries[4,5]. Citus has capabilities to partition tables over a cluster of PostgreSQL nodes.…”