HadoopTrajectory: a Hadoop spatiotemporal data processing extension

The recent advances in location tracking technologies and the widespread use of location-aware applications have resulted in big datasets of moving object trajectories. While there exists a couple of research prototypes for moving object databases, there is a lack of systems that can process big spa...

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Vydané v:Journal of geographical systems Ročník 21; číslo 2; s. 211 - 235
Hlavní autori: Bakli, Mohamed, Sakr, Mahmoud, Soliman, Taysir Hassan A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2019
Springer
Springer Nature B.V
Predmet:
Analytics
Computer Appl. in Social and Behavioral Sciences
Data processing
Distributed processing
Econometrics
Economics
Economics and Finance
Electronic data processing
Geographical Information Systems/Cartography
Landscape/Regional and Urban Planning
Measuring instruments
Moving object recognition
Nodes
Operators
Original Article
Regional/Spatial Science
Spatiotemporal data
Tracking
Trajectories
Urban Economics
R53
R4
C6
O3
C8
Hadoop
Trajectory data management
Big data
Spatiotemporal
3DR-tree
L86
ISSN:1435-5930, 1435-5949
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Popis
Shrnutí:The recent advances in location tracking technologies and the widespread use of location-aware applications have resulted in big datasets of moving object trajectories. While there exists a couple of research prototypes for moving object databases, there is a lack of systems that can process big spatiotemporal data. This work proposes HadoopTrajectory, a Hadoop extension for spatiotemporal data processing. The extension adds spatiotemporal types and operators to the Hadoop core. These types and operators can be directly used in MapReduce programs, which gives the Hadoop user the possibility to write spatiotemporal data analytics programs. The storage layer of Hadoop, the HDFS, is extended by types to represent trajectory data and their corresponding input and output functions. It is also extended by file splitters and record readers. This enables Hadoop to read big files of moving object trajectories such as vehicle GPS tracks and split them over worker nodes for distributed processing. The storage layer is also extended by spatiotemporal indexes that help filtering the data before splitting it over the worker nodes. Several data access functions are provided so that the MapReduce layer can deal with this data. The MapReduce layer is extended with trajectory processing operators, to compute for instance the length of a trajectory in meters. This paper describes the extension and evaluates it using a synthetic dataset and a real dataset. Comparisons with non-Hadoop systems and with standard Hadoop are given. The extension accounts for about 11,601 lines of Java code.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1435-5930
1435-5949
DOI:10.1007/s10109-019-00292-4