Advances In Machine Learning And Data Mining For Astronomy

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Advances in Machine Learning and Data Mining for Astronomy

Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Advances in Machine Learning and Data Mining for Astronomy Book PDF

Author : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Publisher : CRC Press
Page : 746 pages
File Size : 55,9 Mb
Release : 2012-03-29
Category : Computers
ISBN : 9781439841730

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Advances in Machine Learning and Data Mining for Astronomy by Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava Pdf

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Statistics, Data Mining, and Machine Learning in Astronomy

Željko Ivezić,Andrew J. Connolly,Jacob T. VanderPlas,Alexander Gray
Statistics Data Mining and Machine Learning in Astronomy Book PDF

Author : Željko Ivezić,Andrew J. Connolly,Jacob T. VanderPlas,Alexander Gray
Publisher : Princeton University Press
Page : 550 pages
File Size : 48,6 Mb
Release : 2014-01-12
Category : Science
ISBN : 9780691151687

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Statistics, Data Mining, and Machine Learning in Astronomy by Željko Ivezić,Andrew J. Connolly,Jacob T. VanderPlas,Alexander Gray Pdf

As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers

Statistics, Data Mining, and Machine Learning in Astronomy

Željko Ivezić,Andrew J. Connolly,Jacob T. VanderPlas,Alexander Gray
Statistics Data Mining and Machine Learning in Astronomy Book PDF

Author : Željko Ivezić,Andrew J. Connolly,Jacob T. VanderPlas,Alexander Gray
Publisher : Princeton University Press
Page : 552 pages
File Size : 41,6 Mb
Release : 2019-12-03
Category : Science
ISBN : 9780691197050

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Statistics, Data Mining, and Machine Learning in Astronomy by Željko Ivezić,Andrew J. Connolly,Jacob T. VanderPlas,Alexander Gray Pdf

Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest. An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date. Fully revised and expanded Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from astronomical surveys Uses a freely available Python codebase throughout Ideal for graduate students, advanced undergraduates, and working astronomers

Statistics, Data Mining, and Machine Learning in Astronomy

Željko Ivezić,Andrew Connolly,Jacob VanderPlas,Alexander Gray
Statistics Data Mining and Machine Learning in Astronomy Book PDF

Author : Željko Ivezić,Andrew Connolly,Jacob VanderPlas,Alexander Gray
Publisher : Unknown
Page : 552 pages
File Size : 52,5 Mb
Release : 2014
Category : Electronic
ISBN : OCLC:1107417483

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Statistics, Data Mining, and Machine Learning in Astronomy by Željko Ivezić,Andrew Connolly,Jacob VanderPlas,Alexander Gray Pdf

As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers.

Advanced Data Mining Tools and Methods for Social Computing

Sourav De,Sandip Dey,Siddhartha Bhattacharyya,Surbhi Bhatia Khan
Advanced Data Mining Tools and Methods for Social Computing Book PDF

Author : Sourav De,Sandip Dey,Siddhartha Bhattacharyya,Surbhi Bhatia Khan
Publisher : Academic Press
Page : 294 pages
File Size : 52,7 Mb
Release : 2022-01-14
Category : Computers
ISBN : 9780323857093

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Advanced Data Mining Tools and Methods for Social Computing by Sourav De,Sandip Dey,Siddhartha Bhattacharyya,Surbhi Bhatia Khan Pdf

Advanced Data Mining Tools and Methods for Social Computing explores advances in the latest data mining tools, methods, algorithms and the architectures being developed specifically for social computing and social network analysis. The book reviews major emerging trends in technology that are supporting current advancements in social networks, including data mining techniques and tools. It also aims to highlight the advancement of conventional approaches in the field of social networking. Chapter coverage includes reviews of novel techniques and state-of-the-art advances in the area of data mining, machine learning, soft computing techniques, and their applications in the field of social network analysis. Provides insights into the latest research trends in social network analysis Covers a broad range of data mining tools and methods for social computing and analysis Includes practical examples and case studies across a range of tools and methods Features coding examples and supplementary data sets in every chapter

Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems

Ivan Zelinka,Guanrong Chen,Otto E. Rössler,Vaclav Snasel,Ajith Abraham
Nostradamus 2013 Prediction Modeling and Analysis of Complex Systems Book PDF

Author : Ivan Zelinka,Guanrong Chen,Otto E. Rössler,Vaclav Snasel,Ajith Abraham
Publisher : Springer Science & Business Media
Page : 529 pages
File Size : 41,6 Mb
Release : 2013-11-13
Category : Technology & Engineering
ISBN : 9783319005423

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Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems by Ivan Zelinka,Guanrong Chen,Otto E. Rössler,Vaclav Snasel,Ajith Abraham Pdf

Prediction of behavior of the dynamical systems, analysis and modeling of its structure is vitally important problem in engineering, economy and science today. Examples of such systems can be seen in the world around us and of course in almost every scientific discipline including such “exotic” domains like the earth’s atmosphere, turbulent fluids, economies (exchange rate and stock markets), population growth, physics (control of plasma), information flow in social networks and its dynamics, chemistry and complex networks. To understand such dynamics and to use it in research or industrial applications, it is important to create its models. For this purpose there is rich spectra of methods, from classical like ARMA models or Box Jenkins method to such modern ones like evolutionary computation, neural networks, fuzzy logic, fractal geometry, deterministic chaos and more. This proceeding book is a collection of the accepted papers to conference Nostradamus that has been held in Ostrava, Czech Republic. Proceeding also comprises of outstanding keynote speeches by distinguished guest speakers: Guanrong Chen (Hong Kong), Miguel A. F. Sanjuan (Spain), Gennady Leonov and Nikolay Kuznetsov (Russia), Petr Škoda (Czech Republic). The main aim of the conference is to create periodical possibility for students, academics and researchers to exchange their ideas and novel methods. This conference will establish forum for presentation and discussion of recent trends in the area of applications of various predictive methods for researchers, students and academics.

Data Mining: Know It All

Soumen Chakrabarti,Richard E. Neapolitan,Dorian Pyle,Mamdouh Refaat,Markus Schneider,Toby J. Teorey,Ian H. Witten,Earl Cox,Eibe Frank,Ralf Hartmut Güting,Jiawei Han,Xia Jiang,Micheline Kamber,Sam S. Lightstone,Thomas P. Nadeau
Data Mining Know It All Book PDF

Author : Soumen Chakrabarti,Richard E. Neapolitan,Dorian Pyle,Mamdouh Refaat,Markus Schneider,Toby J. Teorey,Ian H. Witten,Earl Cox,Eibe Frank,Ralf Hartmut Güting,Jiawei Han,Xia Jiang,Micheline Kamber,Sam S. Lightstone,Thomas P. Nadeau
Publisher : Morgan Kaufmann
Page : 477 pages
File Size : 44,7 Mb
Release : 2008-10-31
Category : Computers
ISBN : 9780080877884

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Data Mining: Know It All by Soumen Chakrabarti,Richard E. Neapolitan,Dorian Pyle,Mamdouh Refaat,Markus Schneider,Toby J. Teorey,Ian H. Witten,Earl Cox,Eibe Frank,Ralf Hartmut Güting,Jiawei Han,Xia Jiang,Micheline Kamber,Sam S. Lightstone,Thomas P. Nadeau Pdf

This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining. This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources. Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints. Presents multiple methods of analysis and algorithmic problem-solving techniques, enhancing the reader’s technical expertise and ability to implement practical solutions. Coverage of both theory and practice brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases.

Large-Scale Machine Learning in the Earth Sciences

Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser
Large Scale Machine Learning in the Earth Sciences Book PDF

Author : Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser
Publisher : CRC Press
Page : 238 pages
File Size : 45,7 Mb
Release : 2017-08-01
Category : Computers
ISBN : 9781498703888

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Large-Scale Machine Learning in the Earth Sciences by Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser Pdf

From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire more computer scientists to focus on environmental applications, and Earth scientists to seek collaborations with researchers in machine learning and data mining to advance the frontiers in Earth sciences." --Vipin Kumar, University of Minnesota Large-Scale Machine Learning in the Earth Sciences provides researchers and practitioners with a broad overview of some of the key challenges in the intersection of Earth science, computer science, statistics, and related fields. It explores a wide range of topics and provides a compilation of recent research in the application of machine learning in the field of Earth Science. Making predictions based on observational data is a theme of the book, and the book includes chapters on the use of network science to understand and discover teleconnections in extreme climate and weather events, as well as using structured estimation in high dimensions. The use of ensemble machine learning models to combine predictions of global climate models using information from spatial and temporal patterns is also explored. The second part of the book features a discussion on statistical downscaling in climate with state-of-the-art scalable machine learning, as well as an overview of methods to understand and predict the proliferation of biological species due to changes in environmental conditions. The problem of using large-scale machine learning to study the formation of tornadoes is also explored in depth. The last part of the book covers the use of deep learning algorithms to classify images that have very high resolution, as well as the unmixing of spectral signals in remote sensing images of land cover. The authors also apply long-tail distributions to geoscience resources, in the final chapter of the book.

Astroinformatics (IAU S325)

Massimo Brescia,S. G. Djorgovski,Eric D. Feigelson,Giuseppe Longo,Stefano Cavuoti
Astroinformatics IAU S325 Book PDF

Author : Massimo Brescia,S. G. Djorgovski,Eric D. Feigelson,Giuseppe Longo,Stefano Cavuoti
Publisher : Cambridge University Press
Page : 0 pages
File Size : 41,7 Mb
Release : 2017-06-15
Category : Science
ISBN : 110716995X

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Astroinformatics (IAU S325) by Massimo Brescia,S. G. Djorgovski,Eric D. Feigelson,Giuseppe Longo,Stefano Cavuoti Pdf

Astronomy has become data-driven in ways that are both quantitatively and qualitatively different from the past: data structures are not simple; procedures to gain astrophysical insights are not obvious; and the informational content of the data sets is so high that archival research and data mining are not merely convenient, but obligatory, as researchers who obtain the data can only extract a small fraction of the science enabled by it. IAU Symposium 325 took place at a crucial stage in the development of the field, when many efforts have carried significant achievements, but the widespread groups have just begun to effectively communicate across specialties, to gather and assimilate their achievements, and to consult cross-disciplinary experts. Bringing together astronomers involved in surveys and large simulation projects, computer scientists, data scientists, and companies, this volume showcases their fruitful exchange of ideas, methods, software, and technical capabilities.

Technology and Innovation in Learning, Teaching and Education

Meni Tsitouridou,José A. Diniz,Tassos A. Mikropoulos
Technology and Innovation in Learning Teaching and Education Book PDF

Author : Meni Tsitouridou,José A. Diniz,Tassos A. Mikropoulos
Publisher : Springer
Page : 634 pages
File Size : 40,9 Mb
Release : 2019-05-28
Category : Education
ISBN : 9783030209544

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Technology and Innovation in Learning, Teaching and Education by Meni Tsitouridou,José A. Diniz,Tassos A. Mikropoulos Pdf

This book constitutes the thoroughly refereed post-conference proceedings of the First International Conference on Technology and Innovation in Learning, Teaching and Education, TECH-EDU 2018, held in Thessaloniki, Greece, on June 20-22, 2018. The 30 revised full papers along with 18 short papers presented were carefully reviewed and selected from 80 submissions.The papers are organized in topical sections on new technologies and teaching approaches to promote the strategies of self and co-regulation learning (new-TECH to SCRL); eLearning 2.0: trends, challenges and innovative perspectives; building critical thinking in higher education: meeting the challenge; digital tools in S and T learning; exploratory potentialities of emerging technologies in education; learning technologies; digital technologies and instructional design; big data in education and learning analytics.

Support Vector Machines

Naiyang Deng,Yingjie Tian,Chunhua Zhang
Support Vector Machines Book PDF

Author : Naiyang Deng,Yingjie Tian,Chunhua Zhang
Publisher : CRC Press
Page : 345 pages
File Size : 53,8 Mb
Release : 2012-12-17
Category : Business & Economics
ISBN : 9781439857939

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Support Vector Machines by Naiyang Deng,Yingjie Tian,Chunhua Zhang Pdf

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which

Integrated Vehicle Health Management

Ian K Jennions
Integrated Vehicle Health Management Book PDF

Author : Ian K Jennions
Publisher : SAE International
Page : 190 pages
File Size : 45,7 Mb
Release : 2011-09-27
Category : Technology & Engineering
ISBN : 9780768064322

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Integrated Vehicle Health Management by Ian K Jennions Pdf

Unique and groundbreaking—this highly-anticipated book addresses both basic and advanced concepts critical for the understanding and support of the developing field of Integrated Vehicle Health Management (IVHM). From an initial idea by the SAE IVHM Steering Group, collaboratively written by experts from academia, research and industry, the thirteen chapters within this book represent the collective voice of the most qualified authorities in the field. Highlights of the book include: -a single definition and taxonomy of IVHM, as well as basic principles -the identification of how and where IVHM should be implemented -the commercial value of IVHM -vehicle health management systems engineering -algorithms and their impact on IVHM -IVHM future directions and issues -Case study on IHUMS This book serves as the perfect introduction to IVHM for engineers, executives, academic instructors, and students.

Big Data in Astronomy

Linghe Kong,Tian Huang,Yongxin Zhu,Shenghua Yu
Big Data in Astronomy Book PDF

Author : Linghe Kong,Tian Huang,Yongxin Zhu,Shenghua Yu
Publisher : Elsevier
Page : 440 pages
File Size : 44,8 Mb
Release : 2020-06-13
Category : Science
ISBN : 9780128190852

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Big Data in Astronomy by Linghe Kong,Tian Huang,Yongxin Zhu,Shenghua Yu Pdf

Big Data in Radio Astronomy: Scientific Data Processing for Advanced Radio Telescopes provides the latest research developments in big data methods and techniques for radio astronomy. Providing examples from such projects as the Square Kilometer Array (SKA), the world’s largest radio telescope that generates over an Exabyte of data every day, the book offers solutions for coping with the challenges and opportunities presented by the exponential growth of astronomical data. Presenting state-of-the-art results and research, this book is a timely reference for both practitioners and researchers working in radio astronomy, as well as students looking for a basic understanding of big data in astronomy. Bridges the gap between radio astronomy and computer science Includes coverage of the observation lifecycle as well as data collection, processing and analysis Presents state-of-the-art research and techniques in big data related to radio astronomy Utilizes real-world examples, such as Square Kilometer Array (SKA) and Five-hundred-meter Aperture Spherical radio Telescope (FAST)

Data Mining

Ian H. Witten,Eibe Frank
Data Mining Book PDF

Author : Ian H. Witten,Eibe Frank
Publisher : Elsevier
Page : 558 pages
File Size : 46,8 Mb
Release : 2005-07-13
Category : Computers
ISBN : 9780080477022

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Data Mining by Ian H. Witten,Eibe Frank Pdf

Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more. This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses. Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods Performance improvement techniques that work by transforming the input or output

Knowledge Discovery in Big Data from Astronomy and Earth Observation

Petr Skoda,Fathalrahman Adam
Knowledge Discovery in Big Data from Astronomy and Earth Observation Book PDF

Author : Petr Skoda,Fathalrahman Adam
Publisher : Elsevier
Page : 474 pages
File Size : 40,9 Mb
Release : 2020-04-10
Category : Science
ISBN : 9780128191552

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Knowledge Discovery in Big Data from Astronomy and Earth Observation by Petr Skoda,Fathalrahman Adam Pdf

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