Special Issue "Distributed and Parallel Architectures for Spatial Data"

Special Issue Editors

Guest Editor
Assoc. Prof. Alberto Belussi

Department of Computer Science, University of Verona, Verona, Italy
Website | E-Mail
Interests: spatial big data systems; spatio-temporal data analysis; spatial query processing; conceptual design of spatial databases; spatial constraints; spatial data validation
Guest Editor
Dr. Sara Migliorini

Department of Computer Science, University of Verona, Verona, Italy
Website | E-Mail
Interests: spatial big data systems; spatio-temporal data analysis; spatial data modeling for cultural heritage; scientific workflows for geographical applications
Guest Editor
Dr. Damiano Carra

Department of Computer Science, University of Verona, Verona, Italy
Website | E-Mail
Interests: big data systems: Design, analysis and evaluation of large scale data processing systems; distributed systems: Analysis of the Content Delivery Networks (CDNs), with a focus on cache management policies
Guest Editor
Assoc. Prof. Eliseo Clementini

Dipartimento di Ingegneria industriale e dell'informazione e di economia, Università dell’Aquila, L’Aquila, Italy
Website | E-Mail
Interests: spatial databases; spatial query languages; mathematical modeling of spatial information; computational geometry; spatio-temporal reasoning; wualitative modeling of geographical information; indoor and outdoor navigation; volunteered geographic information

Special Issue Information

Dear Colleagues,

In recent years, an increasing amount of spatial data have been collected by different types of devices, such as mobile phones, sensors, satellites, space telescope, medical tools for analysis, or are generated by social networks, such as geotagged tweets. The processing of this huge amount of information, including spatial properties, which are frequently represented in heterogeneous ways, is a challenging task that has boosted research in the big data area to investigate the case and propose new solutions for dealing with its peculiarities.

Many different proposals and approaches for facing the problem have been proposed in the literature, addressing different goals and different types of users. However, most of them are obtained by customizing existing approaches, which were originally developed for the processing of big data of the alphanumeric type, without any specific support for spatial or spatio-temporal properties. Thus, the proposed solutions can exploit the parallelism provided by these kinds of systems, but without taking into account, in a proficient way, the space and time dimensions that intrinsically characterize the analyzed datasets. As described in the literature, current solutions includes: (i) the on-top approach, where an underlying system for traditional big datasets is used as a black box while spatial processing is added through the definition of user-defined functions that are specified on top of the underlying system; (ii) the from-scratch approach, where a completely new system is implemented for a specific application context; and (iii) the built-in approach, where an existing solution is extended by injecting spatial data functions into its core.

This Special Issue aims at promoting new and innovative studies, proposing new architectures or innovative evolutions of existing ones, or illustrating experiments on current technologies in order to improve the efficiency and effectiveness of distributed and cluster systems when they deal with spatio-temporal data. We invite submissions of either original technical papers or high-quality survey papers that shed new light on a particular perspective on spatial big data systems.

Assoc. Prof. Alberto Belussi
Dr Sara Migliorini
Dr Damiano Carra
Assoc. Prof. Eliseo Clementini
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big spatial (or spatio-temporal) data processing
  • Optimized MapReduce implementation of spatial analysis tools
  • Novel indexing methods for massive spatial (or spatio-temporal) data
  • Performance studies for spatial (or spatio-temporal) analytics
  • Processing of geo-crowdsourced datasets
  • Visualization of massive geo-spatial datasets
  • Smart City analytics

Published Papers (5 papers)

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Research

Open AccessArticle
Mobility Data Warehouses
ISPRS Int. J. Geo-Inf. 2019, 8(4), 170; https://doi.org/10.3390/ijgi8040170
Received: 9 January 2019 / Revised: 12 March 2019 / Accepted: 29 March 2019 / Published: 2 April 2019
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Abstract
The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be [...] Read more.
The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be used. Trajectory data warehouses typically include, as measures, segments of trajectories, linked to spatial and non-spatial contextual dimensions. This paper goes beyond this concept, by including, as measures, the trajectories of moving objects at any point in time. In this way, online analytical processing (OLAP) queries, typically including aggregation, can be combined with moving object queries, to express queries like “List the total number of trucks running at less than 2 km from each other more than 50% of its route in the province of Antwerp” in a concise and elegant way. Existing proposals for trajectory data warehouses do not support queries like this, since they are based on either the segmentation of the trajectories, or a pre-aggregation of measures. The solution presented here is implemented using MobilityDB, a moving object database that extends the PostgresSQL database with temporal data types, allowing seamless integration with relational spatial and non-spatial data. This integration leads to the concept of mobility data warehouses. This paper discusses modeling and querying mobility data warehouses, providing a comprehensive collection of queries implemented using PostgresSQL and PostGIS as database backend, extended with the libraries provided by MobilityDB. Full article
(This article belongs to the Special Issue Distributed and Parallel Architectures for Spatial Data)
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Open AccessArticle
Mr4Soil: A MapReduce-Based Framework Integrated with GIS for Soil Erosion Modelling
ISPRS Int. J. Geo-Inf. 2019, 8(3), 103; https://doi.org/10.3390/ijgi8030103
Received: 10 January 2019 / Revised: 8 February 2019 / Accepted: 22 February 2019 / Published: 27 February 2019
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Abstract
A soil erosion model is used to evaluate the conditions of soil erosion and guide agricultural production. Recently, high spatial resolution data have been collected in new ways, such as three-dimensional laser scanning, providing the foundation for refined soil erosion modelling. However, serial [...] Read more.
A soil erosion model is used to evaluate the conditions of soil erosion and guide agricultural production. Recently, high spatial resolution data have been collected in new ways, such as three-dimensional laser scanning, providing the foundation for refined soil erosion modelling. However, serial computing cannot fully meet the computational requirements of massive data sets. Therefore, it is necessary to perform soil erosion modelling under a parallel computing framework. This paper focuses on a parallel computing framework for soil erosion modelling based on the Hadoop platform. The framework includes three layers: the methodology, algorithm, and application layers. In the methodology layer, two types of parallel strategies for data splitting are defined as row-oriented and sub-basin-oriented methods. The algorithms for six parallel calculation operators for local, focal and zonal computing tasks are designed in detail. These operators can be called to calculate the model factors and perform model calculations. We defined the key-value data structure of GeoCSV format for vector, row-based and cell-based rasters as the inputs for the algorithms. A geoprocessing toolbox is developed and integrated with the geographic information system (GIS) platform in the application layer. The performance of the framework is examined by taking the Gushanchuan basin as an example. The results show that the framework can perform calculations involving large data sets with high computational efficiency and GIS integration. This approach is easy to extend and use and provides essential support for applying high-precision data to refine soil erosion modelling. Full article
(This article belongs to the Special Issue Distributed and Parallel Architectures for Spatial Data)
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Open AccessArticle
HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time
ISPRS Int. J. Geo-Inf. 2018, 7(12), 467; https://doi.org/10.3390/ijgi7120467
Received: 30 October 2018 / Revised: 24 November 2018 / Accepted: 27 November 2018 / Published: 30 November 2018
Cited by 1 | PDF Full-text (3904 KB) | HTML Full-text | XML Full-text
Abstract
Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly [...] Read more.
Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data. Full article
(This article belongs to the Special Issue Distributed and Parallel Architectures for Spatial Data)
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Open AccessArticle
High-Performance Geospatial Big Data Processing System Based on MapReduce
ISPRS Int. J. Geo-Inf. 2018, 7(10), 399; https://doi.org/10.3390/ijgi7100399
Received: 21 August 2018 / Revised: 30 September 2018 / Accepted: 4 October 2018 / Published: 6 October 2018
Cited by 1 | PDF Full-text (3294 KB) | HTML Full-text | XML Full-text
Abstract
With the rapid development of Internet of Things (IoT) technologies, the increasing volume and diversity of sources of geospatial big data have created challenges in storing, managing, and processing data. In addition to the general characteristics of big data, the unique properties of [...] Read more.
With the rapid development of Internet of Things (IoT) technologies, the increasing volume and diversity of sources of geospatial big data have created challenges in storing, managing, and processing data. In addition to the general characteristics of big data, the unique properties of spatial data make the handling of geospatial big data even more complicated. To facilitate users implementing geospatial big data applications in a MapReduce framework, several big data processing systems have extended the original Hadoop to support spatial properties. Most of those platforms, however, have included spatial functionalities by embedding them as a form of plug-in. Although offering a convenient way to add new features to an existing system, the plug-in has several limitations. In particular, while executing spatial and nonspatial operations by alternating between the existing system and the plug-in, additional read and write overheads have to be added to the workflow, significantly reducing performance efficiency. To address this issue, we have developed Marmot, a high-performance, geospatial big data processing system based on MapReduce. Marmot extends Hadoop at a low level to support seamless integration between spatial and nonspatial operations of a solid framework, allowing improved performance of geoprocessing workflow. This paper explains the overall architecture and data model of Marmot as well as the main algorithm for automatic construction of MapReduce jobs from a given spatial analysis task. To illustrate how Marmot transforms a sequence of operators for spatial analysis to map and reduce functions in a way to achieve better performance, this paper presents an example of spatial analysis retrieving the number of subway stations per city in Korea. This paper also experimentally demonstrates that Marmot generally outperforms SpatialHadoop, one of the top plug-in based spatial big data frameworks, particularly in dealing with complex and time-intensive queries involving spatial index. Full article
(This article belongs to the Special Issue Distributed and Parallel Architectures for Spatial Data)
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Open AccessFeature PaperArticle
LandQv2: A MapReduce-Based System for Processing Arable Land Quality Big Data
ISPRS Int. J. Geo-Inf. 2018, 7(7), 271; https://doi.org/10.3390/ijgi7070271
Received: 25 May 2018 / Revised: 24 June 2018 / Accepted: 6 July 2018 / Published: 10 July 2018
Cited by 3 | PDF Full-text (8401 KB) | HTML Full-text | XML Full-text
Abstract
Arable land quality (ALQ) data are a foundational resource for national food security. With the rapid development of spatial information technologies, the annual acquisition and update of ALQ data covering the country have become more accurate and faster. ALQ data are mainly vector-based [...] Read more.
Arable land quality (ALQ) data are a foundational resource for national food security. With the rapid development of spatial information technologies, the annual acquisition and update of ALQ data covering the country have become more accurate and faster. ALQ data are mainly vector-based spatial big data in the ESRI (Environmental Systems Research Institute) shapefile format. Although the shapefile is the most common GIS vector data format, unfortunately, the usage of ALQ data is very constrained due to its massive size and the limited capabilities of traditional applications. To tackle the above issues, this paper introduces LandQv2, which is a MapReduce-based parallel processing system for ALQ big data. The core content of LandQv2 is composed of four key technologies including data preprocessing, the distributed R-tree index, the spatial range query, and the map tile pyramid model-based visualization. According to the functions in LandQv2, firstly, ALQ big data are transformed by a MapReduce-based parallel algorithm from the ESRI Shapefile format to the GeoCSV file format in HDFS (Hadoop Distributed File System), and then, the spatial coding-based partition and R-tree index are executed for the spatial range query operation. In addition, the visualization of ALQ big data with a GIS (Geographic Information System) web API (Application Programming Interface) uses the MapReduce program to generate a single image or pyramid tiles for big data display. Finally, a set of experiments running on a live system deployed on a cluster of machines shows the efficiency and scalability of the proposed system. All of these functions supported by LandQv2 are integrated into SpatialHadoop, and it is also able to efficiently support any other distributed spatial big data systems. Full article
(This article belongs to the Special Issue Distributed and Parallel Architectures for Spatial Data)
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