"Preface What is big data? Due to increased interest in this phenomenon, many recent papers and reports have focused on defining and discussing this subject. A review of these publications would point to a consensus about how big data is perceived and explained. It is widely agreed that big data has three specific characteristics: volume, in terms of large-scale data storage and processing; variety, or the availability of data in different types and formats; and velocity, which refers to the fast rate ofnew data acquisition. These characteristics are widely referred to as the three Vs of big data, and while projects involving datasets that only feature one of these Vs are considered to be big, most datasets from such fields as science, engineering, and social media feature all three Vs. To better understand the recent spurt of interest in big data, I provide here a new and different perspective on it. I argue that the answer to the question of "What is big data?" depends on when the question is asked, what application is involved, and what computing resources are available. In other words, understanding what big data is requires an analysis of time, applications, and resources. In light of this, I categorize the time element into three groups: past (since the introduction of computing several decades ago), near-past (within the last few years), and present (now). One way of looking at the time element is that, in general, big data in the past meant dealing with gigabyte-sized datasets, in the near-past,terabyte-sized datasets, and in the present, petabyte-sized datasets. I also categorize the application element into three groups: scientific (data used for complex modeling, analysis, and simulation), business (data used for business analysis and modeling), and general"--
The growth of significance of big data for social and economic research is a relatively new phenomenon, but some fields have struggled with the data deluge for some time now and have amassed a plethora of methods and solutions to the problems presented by it. Geosciences have long had to deal with the rapidity of data acquisition, a variety of data formats, and the immense size of the databases containing relevant information. By standards of big data, geoinformatics is an old, venerable, and well-developed discipline. In this volume, several international experts in the field present a state-of-the-art snapshot of currently employed methods and technologies. Subjects covered include major projects such as GEOSS Clearinghouse, data integration, the use of cloud computing and open environments, online visualization and analysis services, interaction with the social media, and many others. Case studies of particular technologies (for example, NASA global precipitation products) are discussed throughout. Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)
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.
