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
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vii | |
| A Word From The Big-Sky-Earth Chair |
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xi | |
| Preface |
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xiii | |
| Acknowledgments |
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xvii | |
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1 Methodologies for Knowledge Discovery Processes in Context of AstroGeolnformatics |
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7 | (20) |
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2 Historical Background of Big Data in Astro and Geo Context |
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27 | (10) |
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3 AstroGeoInformatics: From Data Acquisition to Further Application |
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37 | (2) |
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4 Synergy in Astronomy and Geosciences |
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39 | (18) |
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5 Surveys, Catalogues, Databases, and Archives of Astronomical Data |
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57 | (46) |
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6 Surveys, Catalogues, Databases/Archives, and State-of-the-Art Methods for Geoscience Data Processing |
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103 | |
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7 High-Performance Techniques for Big Data Processing |
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137 | (22) |
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8 Query Processing and Access Methods for Big Astro and Geo Databases |
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159 | (14) |
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9 Real-Time Stream Processing in Astronomy |
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173 | (10) |
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183 | (14) |
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11 Advanced Time Series Analysis of Generally Irregularly Spaced Signals: Beyond the Oversimplified Methods |
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197 | (28) |
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12 Learning in Big Data: Introduction to Machine Learning |
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225 | (26) |
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13 Deep Learning -- an Opportunity and a Challenge for Geo- and Astrophysics |
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251 | (16) |
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14 Astro- and Geoinformatics -- visually Guided Classification of Time Series Data |
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267 | (16) |
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15 When Evolutionary Computing Meets Astro- and Geoinformatics |
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283 | (24) |
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16 Multiwavelength Extragalactic Surveys: Examples of Data Mining |
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307 | (18) |
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17 Applications of Big Data in Astronomy and Geosciences: Algorithms for Photographic Images Processing and Error Elimination |
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325 | (6) |
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18 Big Astronomical Datasets and Discovery of New Celestial Bodies in the Solar System in Automated Mode by the CoLiTec Software |
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331 | (16) |
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19 Big Data for the Magnetic Field Variations in Solar-Terrestrial Physics and Their Wavelet Analysis |
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347 | (24) |
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20 International Database of Neutron Monitor Measurements: Development and Applications |
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371 | (14) |
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21 Monitoring the Earth Ionosphere by Listening to GPS Satellites |
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385 | (20) |
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22 Exploitation of Big Real-Time GNSS Databases for Weather Prediction |
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405 | (14) |
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23 Application of Databases Collected in Ionospheric Observations by VLF/LF Radio Signals |
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419 | (16) |
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24 Influence on Life Applications of a Federated Astro-Geo Database |
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435 | (10) |
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| Index |
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445 | |
Petr koda 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.
Lisainfo e-raamatute kohta