Elsevier Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics

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. Review Offers insight into the common workflows and data science tools used for big data in astronomy and geoscience From the Back Cover Knowledge Discovery in Big Data from Astronomy and Earth Observation 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 the similarity in the data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Both the software and algorithms aspect of big data as well as the hardware view are addressed as well. 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. About the Author Petr Škoda has been involved in astroinformatics and has a long-term experience in using and lecturing the astronomical Virtual observatory. One of the proposers of COST BigSkyEarth Action and its MC member. BigSkyEarth is the working group behind the idea of the book and a conference about the same topic. Fathalrahman Adam has good understanding of classical machine learning and new concepts, along with hands-on experience and published papers. He is involved in large scale applications using satellite data for earth observation, mainly multi-spectral data. He is a member of BigSkyEarth COST Action.