Spatiotemporal data represent the real-world objects that move in geographic space over time. The enormous numbers of mobile sensors and location tracking devices continuously produce massive amounts of such data. This leads to the need for scalable spatiotemporal data management systems. Such systems shall be capable of representing spatiotemporal data in persistent storage and in memory. They shall also provide a range of query processing operators that may scale out in a cloud setting. Currently, very few researches have been conducted to meet this requirement. This paper proposes a Hadoop extension with a spatiotemporal algebra. The algebra consists of moving object types added as Hadoop native types, and operators on top of them. The Hadoop file system has been extended to support parameter passing for files that contain spatiotemporal data, and for operators that can be unary or binary. Both the types and operators are accessible for the MapReduce jobs. Such an extension allows users to write Hadoop programs that can perform spatiotemporal analysis. Certain queries may call more than one operator for different jobs and keep these operators running in parallel. This paper describes the design and implementation of this algebra, and evaluates it using a benchmark that is specific to moving object databases.;
The availability of moving object data that is being collected nowadays, and the demand of using them in applications, have generated the need for spatiotemporal data management systems. MobilityDB is an open source moving object database system. Its core function is to eiciently store and query moving object trajectories. It is engineered up from PostgreSQL and PostGIS, providing spatiotemporal data management via SQL. In order to store and analyze the massive datasets of trajectories, a scalable version is required. In this paper, we present a solution to distribute MobilityDB using Citus. Citus is a PostgreSQL extension for distributed query processing. We report on the integration architecture, and the types of queries that can be distributed out of the box. The experiments prove the feasibility of the solution, and show a signiicant speed up in queries. CCS CONCEPTS• Information systems → Database management system engines.
Mobility applications involve large amounts of data that must be managed and queried in a scalable way. MobilityDB 1 is an SQL moving object database system. It is an extension of PostgreSQL and PostGIS that supports storing and querying mobility data. This paper demonstrates the distributed query management capabilities in MobilityDB using a cluster that contains 2 billion real AIS ship trajectory points obtained from the Danish Maritime Authority.
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.
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