Spatial Data Management in IoT Systems: Solutions and Evaluation

2021 ◽  
Vol 15 (01) ◽  
pp. 117-139
Author(s):  
Maria Krommyda ◽  
Verena Kantere
Keyword(s):  
Big Data ◽  
Spatial Data ◽  
Spatial Information ◽  
Efficient Solutions ◽  
Data Sources ◽  
The Internet ◽  
Multiple Sources ◽  
Spatial Data Management ◽  
The Internet Of Things ◽  
Integrate Data

As the Internet of Things (IoT) systems gain in popularity, an increasing number of Big Data sources are available. Ranging from small sensor networks designed for household use to large fully automated industrial environments, the IoT systems create billions of measurements each second making traditional storage and indexing solutions obsolete. While research around Big Data has focused on scalable solutions that can support the datasets produced by these systems, the focus has been mainly on managing the volume and velocity of these data, rather than providing efficient solutions for their retrieval and analysis. A key characteristic of these data, which is, more often than not, overlooked, is the spatial information that can be used to integrate data from multiple sources and conduct multi-dimensional analysis of the collected information. We present here the solutions currently available for the storage and indexing of spatial datasets produced by the IoT systems and we discuss their applicability in real-world scenarios.

scholarly journals Development of framework for aggregation and visualization of three-dimensional (3D) spatial data

2021 ◽  
Author(s):  
Mihal Miu ◽  
Xiaokun Zhang ◽  
M. Ali Akber Dewan ◽  
Junye Wang
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Spatial Information ◽  
Three Dimensional ◽  
Geospatial Data ◽  
Third Party ◽  
Data Sources ◽  
Multidimensional Data ◽  
Multiple Sources ◽  
Geospatial Information

Geospatial information plays an important role in environmental modelling, resource management, business operations, and government policy. However, very little or no commonality between formats of various geospatial data has led to difficulties in utilizing the available geospatial information. These disparate data sources must be aggregated before further extraction and analysis may be performed. The objective of this paper is to develop a framework called PlaniSphere, which aggregates various geospatial datasets, synthesizes raw data, and allows for third party customizations of the software. PlaniSphere uses NASA World Wind to access remote data and map servers using Web Map Service (WMS) as the underlying protocol that supports service-oriented architecture (SOA). The results show that PlaniSphere can aggregate and parses files that reside in local storage and conforms to the following formats: GeoTIFF, ESRI shape files, and KML. Spatial data retrieved using WMS from the Internet can create geospatial data sets (map data) from multiple sources, regardless of who the data providers are. The plug-in function of this framework can be expanded for wider uses, such as aggregating and fusing geospatial data from different data sources, by providing customizations to serve future uses, which the capacity of the commercial ESRI ArcGIS software is limited to add libraries and tools due to its closed-source architectures and proprietary data structures. Analysis and increasing availability of geo-referenced data may provide an effective way to manage spatial information by using large-scale storage, multidimensional data management, and Online Analytical Processing (OLAP) capabilities in one system.

Geographic Information System and Big Spatial Data

2020 ◽  
Vol 16 (4) ◽  
pp. 101-145
Author(s):  
Raja Sher Afgun Usmani ◽  
Ibrahim Abaker Targio Hashem ◽  
Thulasyammal Ramiah Pillai ◽  
Anum Saeed ◽  
Akibu Mahmoud Abdullahi
Keyword(s):  
Information System ◽  
Big Data ◽  
Geographic Information System ◽  
Spatial Data ◽  
Spatial Information ◽  
Geographic Information ◽  
Data Sources ◽  
Spatial Datasets ◽  
Big Data Technologies ◽  
The Impact

Geographic information system (GIS) is designed to generate maps, manage spatial datasets, perform sophisticated “what if” spatial analyses, visualize multiple spatial datasets simultaneously, and solve location-based queries. The impact of big data is in every industry, including the GIS. The location-based big data also known as big spatial data has significant implications as it forces the industry to contemplate how to acquire and leverage spatial information. In this study, a comprehensive taxonomy is created to provide a better understanding of the uses of GIS and big spatial data. The taxonomy is made up of big data technologies, GIS data sources, tools, analytics, and applications. The authors look into the importance of big spatial data and its implications, review the data sources, and GIS analytics, applications, and GIS tools. Furthermore, in order to guide researchers interested in GIS, the challenges that require substantial research efforts are taken into account. Lastly, open issues in GIS that require further observation are summarized.

scholarly journals Development of framework for aggregation and visualization of three-dimensional (3D) spatial data

2021 ◽  
Author(s):  
Mihal Miu ◽  
Xiaokun Zhang ◽  
M. Ali Akber Dewan ◽  
Junye Wang
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Spatial Information ◽  
Three Dimensional ◽  
Geospatial Data ◽  
Third Party ◽  
Data Sources ◽  
Multidimensional Data ◽  
Multiple Sources ◽  
Geospatial Information

Geospatial information plays an important role in environmental modelling, resource management, business operations, and government policy. However, very little or no commonality between formats of various geospatial data has led to difficulties in utilizing the available geospatial information. These disparate data sources must be aggregated before further extraction and analysis may be performed. The objective of this paper is to develop a framework called PlaniSphere, which aggregates various geospatial datasets, synthesizes raw data, and allows for third party customizations of the software. PlaniSphere uses NASA World Wind to access remote data and map servers using Web Map Service (WMS) as the underlying protocol that supports service-oriented architecture (SOA). The results show that PlaniSphere can aggregate and parses files that reside in local storage and conforms to the following formats: GeoTIFF, ESRI shape files, and KML. Spatial data retrieved using WMS from the Internet can create geospatial data sets (map data) from multiple sources, regardless of who the data providers are. The plug-in function of this framework can be expanded for wider uses, such as aggregating and fusing geospatial data from different data sources, by providing customizations to serve future uses, which the capacity of the commercial ESRI ArcGIS software is limited to add libraries and tools due to its closed-source architectures and proprietary data structures. Analysis and increasing availability of geo-referenced data may provide an effective way to manage spatial information by using large-scale storage, multidimensional data management, and Online Analytical Processing (OLAP) capabilities in one system.

Big Spatial Data Management for the Internet of Things: A Survey

2020 ◽  
Vol 28 (4) ◽  
pp. 990-1035
Author(s):  
Isam Mashhour Al Jawarneh ◽  
Paolo Bellavista ◽  
Antonio Corradi ◽  
Luca Foschini ◽  
Rebecca Montanari
Keyword(s):  
Internet Of Things ◽  
Data Management ◽  
Spatial Data ◽  
The Internet ◽  
Spatial Data Management ◽  
The Internet Of Things

An Optimal Framework for Spatial Query Optimization Using Hadoop in Big Data Analytics

2019 ◽  
Vol 12 ◽  
Author(s):  
Pankaj Dadheech ◽  
Dinesh Goyal ◽  
Sumit Srivastava ◽  
Ankit Kumar
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Spatial Data ◽  
Spatial Information ◽  
Big Data Analytics ◽  
Spatial Query ◽  
Data Process ◽  
Boolean Queries ◽  
Spatial Query Optimization ◽  
Hadoop System

Spatial queries frequently used in Hadoop for significant data process. However, vast and massive size of spatial information makes it difficult to process the spatial inquiries proficiently, so they utilized the Hadoop system for process Big Data. We have used Boolean Queries & Geometry Boolean Spatial Data for Query Optimization using Hadoop System. In this paper, we show a lightweight and adaptable spatial data index for big data which will process in Hadoop frameworks. Results demonstrate the proficiency and adequacy of our spatial ordering system for various spatial inquiries.

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