TrajMesa: A Distributed NoSQL Storage Engine for Big Trajectory Data

Li, Ruiyuan; He, Huajun; Wang, Rubin; Ruan, Sijie; Sui, Yuan; Bao, Jie; Zheng, Yu

doi:10.1109/icde48307.2020.00224

Cited by 40 publications

(16 citation statements)

References 10 publications

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“…A number of big spatio-temporal data processing systems have been developed by utilizing NoSQL databases in the last couple of years, such as MD-HBase [175], Distributed SECONDO [170], GeoMesa [94,124,152], BBoxDB [172,173], THBase [202], TrajMesa [150], and JUST [148]. The detailed feature matrix of these systems is presented in Table 5.…”

Section: Nosql-based Big Spatio-temporal Infrastructuresmentioning

confidence: 99%

A Survey on Spatio-temporal Data Analytics Systems

2022

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Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

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Section: Nosql-based Big Spatio-temporal Infrastructuresmentioning

confidence: 99%

A Survey on Spatio-temporal Data Analytics Systems

2022

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“…The research in moving object data management has been active since early 2000, covering all aspects of modelling, indexing, query operations, analysis, visualization, data uncertainty, etc. Multiple research prototypes exist, including SECONDO [7], UlTraMan [8] on spark, HadoopTrajectory [9], and TrajMesa [10]. There is also MobilityDB [4], an industrialstrength moving object database system.…”

Section: B Moving Object Data Managementmentioning

confidence: 99%

“…A distinction is made in the literature between continuous trajectories and discrete points. TrajMesa [10], for instance, represents a trajectory as a sequence of timestamped points without assuming interpolation between points. In this sense, the problem of managing temporal geometries reduces to managing discrete polygon objects.…”

Section: B Moving Object Data Managementmentioning

confidence: 99%

Implementing Rigid Temporal Geometries in Moving Object Databases

Schoemans

Sakr

Zimányi

2021

2021 IEEE 37th International Conference on Data Engineering (ICDE)

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Various applications process geospatial trajectories of moving objects, such as cars, ships and robots. There is thus a need for a common conceptual framework to model and manage these objects, as well as to enable data interoperability across tools. The International Organization for Standardization ISO® has responded to this need and created the standard ISO 19141-Schema for moving features. Among its types, it defines a schema for rigid temporal geometries, which represent the movement of spatial objects translating and rotating over time, while preserving a fixed shape. Despite the abundance of these objects in real-world, there exists no reference implementation of this type of data in a common system, which causes them to usually be represented as temporal points without taking into account their spatial extents and shapes. In this paper, we aim to provide an implementation of rigid temporal geometries into MobilityDB, an open-source moving object database, that extends PostgreSQL and PostGIS. We provide a data model for rigid temporal geometries and propose efficient algorithms for the operations defined in ISO 19141. A use case on real AIS ship trajectories is illustrated to validate the proposed implementation. A synthetic data generator for temporal geometries is also proposed. Finally, we review the standard from an implementation point of view and provide insights on possible improvements.

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“…NoSQL-based Systems. TrajMesa [6] proposes a horizontal storage schema in H-store, where each trajectory is stored in one row. Additional columns are added to store the trajectory identifier and its spatiotemporal box.…”

Section: Related Workmentioning

confidence: 99%

Distributed Spatiotemporal Trajectory Query Processing in SQL

Bakli

Sakr

Zimányi

2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

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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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TrajMesa: A Distributed NoSQL Storage Engine for Big Trajectory Data

Cited by 40 publications

References 10 publications

A Survey on Spatio-temporal Data Analytics Systems

A Survey on Spatio-temporal Data Analytics Systems

Implementing Rigid Temporal Geometries in Moving Object Databases

Distributed Spatiotemporal Trajectory Query Processing in SQL

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