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This book is a printed edition of the Special Issue "Scalable Interactive Visualization" that was published in Informatics
Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. - Addresses both astronomy and geosciences in parallel, from a big data perspective - Includes introductory information, key principles, applications and the latest techniques - Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields
With the onset of massive cosmological data collection through media such as the Sloan Digital Sky Survey (SDSS), galaxy classification has been accomplished for the most part with the help of citizen science communities like Galaxy Zoo. Seeking the wisdom of the crowd for such Big Data processing has proved extremely beneficial. However, an analysis of one of the Galaxy Zoo morphological classification data sets has shown that a significant majority of all classified galaxies are labelled as “Uncertain”. This book reports on how to use data mining, more specifically clustering, to identify galaxies that the public has shown some degree of uncertainty for as to whether they belong to one morphology type or another. The book shows the importance of transitions between different data mining techniques in an insightful workflow. It demonstrates that Clustering enables to identify discriminating features in the analysed data sets, adopting a novel feature selection algorithms called Incremental Feature Selection (IFS). The book shows the use of state-of-the-art classification techniques, Random Forests and Support Vector Machines to validate the acquired results. It is concluded that a vast majority of these galaxies are, in fact, of spiral morphology with a small subset potentially consisting of stars, elliptical galaxies or galaxies of other morphological variants.
Grid architectures, which are viewed as tools for the integration of distributed resources, play a significant role as managers of computational resources, but also as aggregators of measurement instrumentation and pervasive large-scale data acquisition platforms. The functionality of a grid architecture allows managing, maintaining, and exploiting hetereogeneous instrumentation and acquisition devices in a unifed way by providing standardized interfaces and common work environments to their users. This result is achieved through the properties of isolation from the physical network and from the peculiarites of the instrumentation granted by standard middleware together with secure and flexibile mechanisms which seek, access, and aggregate distributed resources. This book focuses on a number of aspects related to the effective exploitation of remote instrumentation on the grid. These include middleware architecture, high speed networking in support of grid applications, wireless grid for acquisition devices and sensor networks, quality of service provisioning for real time control, and measurement instrumentation.
Drawn from the US National Science Foundation's Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field.Gathering perspectives from top experts across different di
The ability of storing, managing, and giving access to the huge quantity of data collected by astronomical observatories is one of the major challenges of modern astronomy. At the same time, the growing complexity of data systems implies a change of concepts: the scientist has to manipulate data as well as information. Recent developments of the `WorldWideWeb' bring interesting answers to these problems. The book presents a wide selection of databases, archives, data centers, and information systems. Clear and up-to-date descriptions are included, together with their scientific context and motivations. Audience: This volume provides an essential tool for astronomers, librarians, data specialists and computer engineers.