Related papers: Geospatial Big Data Handling Theory and Methods: A…
This paper addresses the interesting problem of processing and analyzing data in geographic information systems (GIS) to achieve a clear perspective on urban sprawl. The term urban sprawl refers to overgrowth and expansion of low-density…
Geospatial analysis is crucial for addressing many of the world's most pressing challenges. Given this, there is immense value in improving and expanding the visualization techniques used to communicate geospatial data. In this work, we…
Astronomy produces extremely large data sets from ground-based telescopes, space missions, and simulation. The volume and complexity of these rich data sets require new approaches and advanced tools to understand the information contained…
With continued advances in Geographic Information Systems and related computational technologies, statisticians are often required to analyze very large spatial datasets. This has generated substantial interest over the last decade, already…
Many datasets nowadays contain links between geographic locations and natural language texts. These links can be geotags, such as geotagged tweets or geotagged Wikipedia pages, in which location coordinates are explicitly attached to texts.…
Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models have been proposed, the majority of them have been solely…
Big data has ushered in a new wave of predictive power using machine learning models. In this work, we assess what {\it big} means in the context of typical materials-science machine-learning problems. This concerns not only data volume,…
Advances in information technology and its widespread growth in several areas of business, engineering, medical and scientific studies are resulting in information/data explosion. Knowledge discovery and decision making from such rapidly…
Astrophysics has become a domain extremely rich of scientific data. Data mining tools are needed for information extraction from such large datasets. This asks for an approach to data management emphasizing the efficiency and simplicity of…
Big data has become a great asset for many organizations, promising improved operations and new business opportunities. However, big data has increased access to sensitive information that when processed can directly jeopardize the privacy…
The modelling, analysis, and visualisation of dynamic geospatial phenomena has been identified as a key developmental challenge for next-generation Geographic Information Systems (GIS). In this context, the envisaged paradigmatic extensions…
In this digitalised world where every information is stored, the data a are growing exponentially. It is estimated that data are doubles itself every two years. Geospatial data are one of the prime contributors to the big data scenario.…
Understanding the principles of geophysical phenomena is an essential and challenging task. "Model-driven" approaches have supported the development of geophysics for a long time; however, such methods suffer from the curse of…
The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world {via} creating new services and applications in a variety of sectors such as environmental monitoring,…
Spatial big data have the "velocity," "volume," and "variety" of big data sources and additional geographic information about the record. Digital data sources, such as medical claims, mobile phone call data records, and geo-tagged tweets,…
This paper explores the essential areas of cybersecurity management for big data systems. Big data platform stems its complexity from being a collection of interrelated non-standardized systems that interact with each other to process large…
People are facing a flood of data today. Data are being collected at unprecedented scale in many areas, such as networking, image processing, virtualization, scientific computation, and algorithms. The huge data nowadays are called Big…
Modern geosciences have to deal with large quantities and a wide variety of data, including 2-D, 3-D and 4-D seismic surveys, well logs generated by sensors, detailed lithological records, satellite images and meteorological records. These…
While manufacturers have been generating highly distributed data from various systems, devices and applications, a number of challenges in both data management and data analysis require new approaches to support the big data era. These…
Geospatial sciences include a wide range of applications, from environmental monitoring transportation to infrastructure planning, as well as location-based analysis and services. Graph theory algorithms in mathematics have emerged as…