Pg-Trajectory: A PostgreSQL/PostGIS Based Data Model for Spatiotemporal Trajectories

Küçük, Ahmet; Hamdi, Shah Muhammad; Aydin, Berkay; Schuh, Michael A.; Angryk, Rafal A.

doi:10.1109/bdcloud-socialcom-sustaincom.2016.23

Cited by 7 publications

(11 citation statements)

References 25 publications

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“…Our goal is to quantify the differences in performance between PostGIS default trajectory support, dedicated trajectory data models, and commonly used point-based data models. Specifically, we compare the trajectory data type PG-Trajectory by Kucuk et al (2016) to default PostGIS without custom data types or functions. The comparison methodology is based on query run times as well as query complexity, from the perspectives of both the user writing the query (number of subqueries that have to be written) and the database system (steps in the execution plan).…”

Section: Moving Point Objects In Postgismentioning

confidence: 99%

“…Section 2 introduces approaches for modelling moving point objects in PostGIS. Section 3 presents experiments comparing the custom PG-Trajectory data model (Kucuk et al, 2016) to default PostGIS trajectory support and commonly used point-based data models. Section 4 discusses advantages and shortcomings of these approaches and concludes with an outlook on moving area objects, as well as on massive movement data.…”

Section: Introductionmentioning

confidence: 99%

“…28-29) have already confirmed that spatial databases can handle moving objects, but cautioned that, in classical spatial databases, time is not managed intrinsically. An example of a PostGIS-based data model using a custom trajectory object is presented by Kucuk et al (2016). Their model handles moving point and polygon features as an open source extension (DMLAB, 2016) for PostGIS.…”

Section: Introductionmentioning

confidence: 99%

“…Their model handles moving point and polygon features as an open source extension (DMLAB, 2016) for PostGIS. Kucuk et al (2016) argue that '[w]hen the trajectories are represented [...] using three columns (traj id, timestamp, geometry), users are required to write complex queries and applications for implementing trajectory manipulating functions ' (p. 83). They therefore propose a custom data type that 'makes these queries easier by wrapping up widely used trajectory functions' (ibid.)…”

Section: Introductionmentioning

confidence: 99%

“…(see Table 1). Kucuk et al (2016) indeed handle time explicitly. However, we find that this is not necessary -at least for moving point objects -in current versions of PostGIS, thanks to its built-in temporal support (version 2.2.0, released in October 2015, onwards (PostGIS, 2015)), as we will demonstrate in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Spatio-temporal Data Models for Trajectories in PostGIS Databases

Gräser¹

2018

giforum

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Research in transport, ecology, health and other fields stands to profit from an improved understanding of movement. As movement data availability improves, the need for appropriate movement data analysis increases. However, the limited support for modelling moving objects in GIS hampers data exploration and analysis. This paper discusses trajectory data models and their implementation in the open-source spatial database system PostGIS. We quantify the difference in performance between PostGIS default trajectory support, dedicated trajectory data models, and commonly used point-based data models. To the best of our knowledge, this is the first paper to evaluate PostGIS default trajectory support and compare it to a proposed dedicated trajectory data type from the literature. Our experiments include computing trajectory duration and length, temporal and spatial filters, extracting positions at a certain time, and visualizing trajectories in desktop GIS. We also discuss the limitations of, and potential for, contextual trajectories and moving area object trajectories. Our results show that PostGIS functions for moving point object trajectories are fast, reduce query complexity, and provide good indexing integration, especially concerning multi-dimensional indices; the results also reveal that trajectory data models outperform commonly used point-based data models.

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Section: Moving Point Objects In Postgismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating Spatio-temporal Data Models for Trajectories in PostGIS Databases

Gräser¹

2018

giforum

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Maritime Data Processing in Relational Databases

Emmanuel¹,

Ray²,

Camossi³

et al. 2021

Guide to Maritime Informatics

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Maritime data processing research has long used spatio-temporal relational databases. This model suits well the requirements of off-line applications dealing with average-size and known in advance geographic data that can be represented in tabular form. This chapter explores off-line maritime data processing in such relational databases and provides a step-by-step guide to build a maritime database for investigating maritime traffic and vessel behaviour. Along the chapter, examples and exercises are proposed to build a maritime database using the data available in the open, heterogeneous, integrated dataset for maritime intelligence, surveillance, and reconnaissance that is described in [41]. The dataset exemplifies the variety of data that are nowadays available for monitoring the activities at sea, mainly the Automatic Identification System (AIS), which is openly broadcast and provides worldwide information on the maritime traffic. All the examples and the exercises refer to the syntax of the widespread relational database management system PostgreSQL and its spatial extension PostGIS, which are an established and standard-based combination for spatial data representation and querying. Along the chapter, the reader is guided to experience the spatio-temporal features offered by the database management system, including spatial and temporal data types, indexes, queries and functions, to incrementally investigate vessel behaviours and the resulting maritime traffic.

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