MobilityDB

Zimányi, Esteban; Sakr, Mahmoud; Lesuisse, Arthur; Bakli, Mohamed

doi:10.1145/3340964.3340991

Cited by 11 publications

(4 citation statements)

References 5 publications

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“…For the purpose of storing massive trajectory data in a single-computer environment, researchers have explored this issue and proposed corresponding solutions. They extended the traditional RDBMS to provide efficient data storage management and complex query processing support by optimizing the system's native indexing and querying mechanisms [22,23]. Additionally, TrajStore [24] and SharkDB [25], which are designed for trajectory data storage, have also appeared accordingly.…”

Section: Storage and Querying Of Trajectorymentioning

confidence: 99%

Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

Yang,

Zuo,

Zhao

et al. 2024

IJGI

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The presence of abundant spatio-temporal information based on the location of mobile objects in publicly accessible GPS mobile devices makes it crucial to collect, analyze, and mine such information. Therefore, it is necessary to index a large volume of trajectory data to facilitate efficient trajectory retrieval and access. It is difficult for existing indexing methods that primarily rely on data-driven indexing structures (such as R-Tree) or space-driven indexing structures (such as Quadtree) to support efficient analysis and computation of data based on spatio-temporal range queries as a service basis, especially when applied to massive trajectory data. In this study, we propose a massive GPS data storage and indexing method based on uneven spatial segmentation and trajectory optimization segmentation. Primarily, the method divides GPS trajectories in a large spatio-temporal data space into multiple MBR sequences by greedy algorithm. Then, a hybrid indexing model for segmented trajectories is constructed to form a global spatio-temporal segmentation scheme, called HHBITS index, to achieve hierarchical organization of trajectory data. Eventually, a spatio-temporal range query processing method is proposed based on this index. This paper implements and evaluates the index in MongoDB and compares it with two other spatio-temporal composite indexes for performing spatio-temporal range queries efficiently. The experimental results show that the method in this paper has high performance in responding to spatio-temporal queries on large-scale trajectory data.

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Section: Storage and Querying Of Trajectorymentioning

confidence: 99%

Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

Yang,

Zuo,

Zhao

et al. 2024

IJGI

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“…Given that the indicators are computed using the MobilityDB MO database, this section presents a brief overview of this database to make the article self-contained. Further details can be found in the system's documentation (Zimányi, 2020). type provided by PostgreSQL.…”

Section: Mobilitydbmentioning

confidence: 99%

“…To produce the kinds of indicators introduced above, data processing tools that can efficiently handle enormous amounts of mobility data are needed. In this article, we use MobilityDB (Zimányi et al, 2019, 2020), a novel database built upon PostGIS (the spatial extension of PostgreSQL), that extends the type system of PostgreSQL and PostGIS with abstract data types (ADTs) for representing Moving Object (MO) data. Moving objects (Güting & Schneider, 2005) are objects (e.g., cars, trucks, pedestrians) whose spatial features change continuously in time.…”

Section: Introductionmentioning

confidence: 99%

User‐centered road network traffic analysis with MobilityDB

Sakr

Zimányi

Vaisman

et al. 2022

Transactions in GIS

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Performance indicators of road networks are a long-lasting topic of research. Existing schemes assess network properties such as the average speed on road segments and the queuing time at intersections. The increasing availability of user trajectories, collected mainly using mobile phones with a variety of applications, creates opportunities for developing user-centered performance indicators. Performing such an analysis on big trajectory data sets remains a challenge for the existing data management systems, because they lack support for spatiotemporal trajectory data. This article presents an end-to-end solution, based on MobilityDB, a novel moving object database system that extends Post-greSQL with spatiotemporal data types and functions. A new class of indicators is proposed, focused on the users' experience. The indicators address the network design, the traffic flow, and the driving comfort of the motorists. Furthermore, these indicators are expressed as analytical MobilityDB queries over a big set of real vehicle trajectories.

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“…This makes outlier or noise detection a crucial step in trajectory cleaning Magdy et al (2017). As such, outlier detection is an essential function in mobility data management systems Zimányi et al (2020a); Zimányi et al (2019).…”

Section: Introductionmentioning

confidence: 99%

A Benchmark of Existing Tools for Outlier Detection and Cleaning in Trajectories

Duarte,

Sakr

2023

Preprint

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Outlier detection and cleaning is an essential step in data preprocessing to ensure the integrity and validity of data analyses. This paper focuses on outlier points within a individual trajectories, i.e., points that deviate significantly inside a single trajectory. We benchmark ten open-source libraries to comprehensively evaluate available tools, comparing their efficiency and accuracy in identifying and cleaning outliers. This benchmarking considers the libraries as they are offered to end users, with real-world applicability. We compare existing outlier detection libraries, introduce a method for establishing ground-truth, and aim to guide users in choosing the most appropriate tool for their specific outlier detection needs. Furthermore, we survey the state-of-the-art algorithms for outlier detection and classify them into seven types: Statistic-based methods, Sliding window algorithms, Clustering-based methods, Graph-based methods, Ensemble-based methods, Learning-based methods, and Heuristic-based methods. Our research provides insights into these libraries' performance and contributes to developing data preprocessing and outlier detection methodologies.

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MobilityDB

Cited by 11 publications

References 5 publications

Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data

User‐centered road network traffic analysis with MobilityDB

A Benchmark of Existing Tools for Outlier Detection and Cleaning in Trajectories

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