Review on Integrating Geospatial Big Datasets and Open Research Issues

Abstract

Big data and geographic information systems (GIS) are two technologies that have increasingly influenced many areas in the last 10 years and will continue to improve and help solve serious global problems, such as consequences of climate change or global pandemics. A wide spectrum of GIS applications interacts with the continuous growth of geospatial big data sources to drive precise and informed decisions. Geospatial big data integration is designed to accomplish the compatibility of distinct geospatial datasets regardless of their spatial coverage. The large number of geospatial big data sources demand effective data integration for storing and handling such datasets, which will be used for geospatial data analysis and visualization. For instance, risk management datasets related to healthcare and the environment are heterogeneous and disparate. Obtaining a unified view of such geospatial big datasets is complicated and challenging, especially if we consider problems related to healthcare pandemics and environmental disasters. Hence, before we can attempt to predict and mitigate processes occurring in these domains, we must realize that geospatial big data integration is crucial in consolidating datasets. We explore and discuss issues involved in integrating geospatial big datasets in this study. We then classify big data integration processes into three categories, namely, data warehousing, data transformation and integration methods. Furthermore, several research challenges focused on geospatial big data, big earth data, data warehousing, data transformation and linked data are presented. Lastly, open research issues and emerging trends that require in-depth investigations in the near future are highlighted in this study.INDEX TERMS Big data integration, geographic information system (GIS), geospatial big data.

Applications of geospatial big data in the Internet of Things

“…• Variety. Geospatial data can be collected using diverse technologies, such as GPS, RFID, social networks with location features, and VGI (Al-Yadumi, Xion, Wei, & Boursier, 2021). Their types, structures, semantics, and formats are heterogeneous due to differences between the multiple data sources, purposes, and collection approaches (Işikdağ, 2020).…”

“…It can also help understand the causes and conditions of their occurrence and infer their consequences and relationship with other entities. The availability of computing resources has also stimulated studies on artificial intelligence (AI) and machine learning (ML) techniques to analyze and obtain insights into complex and massive geospatial data in different application domains (Al-Yadumi et al, 2021). Another resource to explore geospatial big data is spatial data science, a branch of data science that provides scalable data access, analysis, and synthesis for spatial data from several sources.…”

“…Nowadays, with a need to deal with high volumes of data transfer and data-intensive applications, the geospatial industry and research community have also intensified their pursuit of cloud computing solutions (Al-Yadumi et al, 2021;Breunig et al, 2020). The distribution of computer resources can improve reliability and elasticity, offering high-end computing capacity and new cost-effective possibilities for the storage and operation of massive geospatial data if processing, memory, and interconnection resources are effectively used (Al-Yadumi et al, 2021;Huang, Li, & Li, 2018;Xu & Yang, 2014). Some examples of cloud-based technologies for geospatial data management are GeoRocket, Google Earth Engine and SEPAL (Al-Yadumi et al, 2021).…”

“…The distribution of computer resources can improve reliability and elasticity, offering high-end computing capacity and new cost-effective possibilities for the storage and operation of massive geospatial data if processing, memory, and interconnection resources are effectively used (Al-Yadumi et al, 2021;Huang, Li, & Li, 2018;Xu & Yang, 2014). Some examples of cloud-based technologies for geospatial data management are GeoRocket, Google Earth Engine and SEPAL (Al-Yadumi et al, 2021).…”

Multimatcher Model to Enhance Ontology Matching Using Background Knowledge

“…The evolution of semantic web technologies and the growth of big data volumes maintained by various database models have resulted in many disparate and independent data sources [1]. However, data growth will pose many issues if we cannot keep pace with these improvements.…”