This proceedings volume introduces recent work on the storage, retrieval and visualization of spatial Big Data, data-intensive geospatial computing and related data quality issues. Further, it addresses traditional topics such as multi-scale spatial data representations, knowledge discovery, space-time modeling, and geological applications. Spatial analysis and data mining are increasingly facing the challenges of Big Data as more and more types of crowd sourcing spatial data are used in GIScience, such as movement trajectories, cellular phone calls, and social networks. In order to effectively manage these massive data collections, new methods and algorithms are called for. The book highlights state-of-the-art advances in the handling and application of spatial data, especially spatial Big Data, offering a cutting-edge reference guide for graduate students, researchers and practitioners in the field of GIScience.

Summarizes the state-of-the-art in this area. It classifies the existing work by considering six aspects of big spatial data systems: approach, architecture, language, indexing, querying, and visualization. It also provides the reader with case studies of real applications that make use of these systems to provide services for end users.

Big data has always been a major challenge in geoinformatics as geospatial data come in various types and formats, new geospatial data are acquired very fast, and geospatial databases are inherently very large. And while there have been advances in hardware and software for handling big data, they often fall short of handling geospatial big data efficiently and effectively. Big Data: Techniques and Technologies in Geoinformatics tackles these challenges head on, integrating coverage of techniques and technologies for storing, managing, and computing geospatial big data. Providing a perspective based on analysis of time, applications, and resources, this book familiarizes readers with geospatial applications that fall under the category of big data. It explores new trends in geospatial data collection, such as geo-crowdsourcing and advanced data collection technologies such as LiDAR point clouds. The book features a range of topics on big data techniques and technologies in geoinformatics including distributed computing, geospatial data analytics, social media, and volunteered geographic information. With chapters contributed by experts in geoinformatics and in domains such as computing and engineering, the book provides an understanding of the challenges and issues of big data in geoinformatics applications. The book is a single collection of current and emerging techniques, technologies, and tools that are needed to collect, analyze, manage, process, and visualize geospatial big data.

· This book is an updated version of a well-received book previously published in Chinese by Science Press of China (the first edition in 2006 and the second in 2013). It offers a systematic and practical overview of spatial data mining, which combines computer science and geo-spatial information science, allowing each field to profit from the knowledge and techniques of the other. To address the spatiotemporal specialties of spatial data, the authors introduce the key concepts and algorithms of the data field, cloud model, mining view, and Deren Li methods. The data field method captures the interactions between spatial objects by diffusing the data contribution from a universe of samples to a universe of population, thereby bridging the gap between the data model and the recognition model. The cloud model is a qualitative method that utilizes quantitative numerical characters to bridge the gap between pure data and linguistic concepts. The mining view method discriminates the different requirements by using scale, hierarchy, and granularity in order to uncover the anisotropy of spatial data mining. The Deren Li method performs data preprocessing to prepare it for further knowledge discovery by selecting a weight for iteration in order to clean the observed spatial data as much as possible. In addition to the essential algorithms and techniques, the book provides application examples of spatial data mining in geographic information science and remote sensing. The practical projects include spatiotemporal video data mining for protecting public security, serial image mining on nighttime lights for assessing the severity of the Syrian Crisis, and the applications in the government project ‘the Belt and Road Initiatives’.

This book covers selected research works presented at the fifth International Conference on Networking, Information Systems and Security (NISS 2022), organized by the Research Center for Data and Information Sciences at the National Research and Innovation Agency (BRIN), Republic of Indonesia, and Moroccan Mediterranean Association of Sciences and Sustainable Development, Morocco, during March 30–31, 2022, hosted in online mode in Bandung, Indonesia. Building on the successful history of the conference series in the recent four years, this book aims to present the paramount role of connecting researchers around the world to disseminate and share new ideas in intelligent information systems, cyber-security, and networking technologies. The 49 chapters presented in this book were carefully reviewed and selected from 115 submissions. They focus on delivering intelligent solutions through leveraging advanced information systems, networking, and security for competitive advantage and cost savings in modern industrial sectors as well as public, business, and education sectors. Authors are eminent academicians, scientists, researchers, and scholars in their respective fields from across the world.

This text integrates different mobility data handling processes, from database management to multi-dimensional analysis and mining, into a unified presentation driven by the spectrum of requirements raised by real-world applications. It presents a step-by-step methodology to understand and exploit mobility data: collecting and cleansing data, storage in Moving Object Database (MOD) engines, indexing, processing, analyzing and mining mobility data. Emerging issues, such as semantic and privacy-aware querying and mining as well as distributed data processing, are also covered. Theoretical presentation is smoothly interchanged with hands-on exercises and case studies involving an actual MOD engine. The authors are established experts who address both theoretical and practical dimensions of the field but also present valuable prototype software. The background context, clear explanations and sample exercises make this an ideal textbook for graduate students studying database management, data mining and geographic information systems.

Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques for the detection, mitigation, treatment and elimination of the COVID-19 virus. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques, including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes is now possible. - Provides a leading-edge survey of Data Science techniques and methods for research, mitigation and the treatment of the COVID-19 virus - Integrates various Data Science techniques to provide a resource for COVID-19 researchers and clinicians around the world, including the wide variety of impacts the virus is having on societies and medical practice - Presents insights into innovative, data-oriented modeling and predictive techniques from COVID-19 researchers around the world, including geoprocessing and tracking, lab data analysis, and theoretical views on a variety of technical applications - Includes real-world feedback and user experiences from physicians and medical staff from around the world for medical treatment perspectives, public safety policies and impacts, sociological and psychological perspectives, the effects of COVID-19 in agriculture, economies, and education, and insights on future pandemics