Training Students To Extract Value From Big Data

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Training Students to Extract Value from Big Data

Author : National Research Council
Publisher : National Academies Press
ISBN 13 : 0309314402
Total Pages : 96 pages
Book Rating : 4.3/5 (93 download)

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Book Synopsis Training Students to Extract Value from Big Data by : National Research Council

Download or read book Training Students to Extract Value from Big Data written by National Research Council and published by National Academies Press. This book was released on 2015-01-16 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the availability of high-throughput data-collection technologies, such as information-sensing mobile devices, remote sensing, internet log records, and wireless sensor networks has grown, science, engineering, and business have rapidly transitioned from striving to develop information from scant data to a situation in which the challenge is now that the amount of information exceeds a human's ability to examine, let alone absorb, it. Data sets are increasingly complex, and this potentially increases the problems associated with such concerns as missing information and other quality concerns, data heterogeneity, and differing data formats. The nation's ability to make use of data depends heavily on the availability of a workforce that is properly trained and ready to tackle high-need areas. Training students to be capable in exploiting big data requires experience with statistical analysis, machine learning, and computational infrastructure that permits the real problems associated with massive data to be revealed and, ultimately, addressed. Analysis of big data requires cross-disciplinary skills, including the ability to make modeling decisions while balancing trade-offs between optimization and approximation, all while being attentive to useful metrics and system robustness. To develop those skills in students, it is important to identify whom to teach, that is, the educational background, experience, and characteristics of a prospective data-science student; what to teach, that is, the technical and practical content that should be taught to the student; and how to teach, that is, the structure and organization of a data-science program. Training Students to Extract Value from Big Data summarizes a workshop convened in April 2014 by the National Research Council's Committee on Applied and Theoretical Statistics to explore how best to train students to use big data. The workshop explored the need for training and curricula and coursework that should be included. One impetus for the workshop was the current fragmented view of what is meant by analysis of big data, data analytics, or data science. New graduate programs are introduced regularly, and they have their own notions of what is meant by those terms and, most important, of what students need to know to be proficient in data-intensive work. This report provides a variety of perspectives about those elements and about their integration into courses and curricula.

Deep Learning: Convergence to Big Data Analytics

Author : Murad Khan
Publisher : Springer
ISBN 13 : 9811334595
Total Pages : 79 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Deep Learning: Convergence to Big Data Analytics by : Murad Khan

Download or read book Deep Learning: Convergence to Big Data Analytics written by Murad Khan and published by Springer. This book was released on 2018-12-30 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.

Big Data and Health Analytics

Author : Katherine Marconi
Publisher : CRC Press
ISBN 13 : 1482229250
Total Pages : 374 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Big Data and Health Analytics by : Katherine Marconi

Download or read book Big Data and Health Analytics written by Katherine Marconi and published by CRC Press. This book was released on 2014-12-20 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides frameworks, use cases, and examples that illustrate the role of big data and analytics in modern health care, including how public health information can inform health delivery. Written for health care professionals and executives, this book presents the current thinking of academic and industry researchers and leaders from around the world. Using non-technical language, it includes case studies that illustrate the business processes that underlie the use of big data and health analytics to improve health care delivery.

E-Portfolios in Higher Education

Author : Tushar Chaudhuri
Publisher : Springer
ISBN 13 : 9811038031
Total Pages : 217 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis E-Portfolios in Higher Education by : Tushar Chaudhuri

Download or read book E-Portfolios in Higher Education written by Tushar Chaudhuri and published by Springer. This book was released on 2017-03-30 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shares the collective experience of integrating electronic portfolios as assessment tools and as instruments for life-long learning in courses across various disciplines in higher education. It enables readers to trace the evolution of e-portfolios over the last ten years and to deal with the challenges faced by instructors and students when implementing e-portfolios in their respective courses. Further, the book suggests flexible ways of dealing with those challenges. It also highlights the relevance of electronic portfolios for the needs and demands of contemporary societies. As such, it speaks to a large target audience from a range of disciplines, roles and geographical contexts within the wider context of higher education in Asia and around the globe.

Big Data Systems

Author : Jawwad Ahmed Shamsi
Publisher : CRC Press
ISBN 13 : 1498752713
Total Pages : 341 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Big Data Systems by : Jawwad Ahmed Shamsi

Download or read book Big Data Systems written by Jawwad Ahmed Shamsi and published by CRC Press. This book was released on 2021-05-10 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Systems encompass massive challenges related to data diversity, storage mechanisms, and requirements of massive computational power. Further, capabilities of big data systems also vary with respect to type of problems. For instance, distributed memory systems are not recommended for iterative algorithms. Similarly, variations in big data systems also exist related to consistency and fault tolerance. The purpose of this book is to provide a detailed explanation of big data systems. The book covers various topics including Networking, Security, Privacy, Storage, Computation, Cloud Computing, NoSQL and NewSQL systems, High Performance Computing, and Deep Learning. An illustrative and practical approach has been adopted in which theoretical topics have been aided by well-explained programming and illustrative examples. Key Features: Introduces concepts and evolution of Big Data technology. Illustrates examples for thorough understanding. Contains programming examples for hands on development. Explains a variety of topics including NoSQL Systems, NewSQL systems, Security, Privacy, Networking, Cloud, High Performance Computing, and Deep Learning. Exemplifies widely used big data technologies such as Hadoop and Spark. Includes discussion on case studies and open issues. Provides end of chapter questions for enhanced learning.

Critical Thinking for Strategic Intelligence

Author : Katherine Hibbs Pherson
Publisher : CQ Press
ISBN 13 : 1506316875
Total Pages : 404 pages
Book Rating : 4.5/5 (63 download)

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Book Synopsis Critical Thinking for Strategic Intelligence by : Katherine Hibbs Pherson

Download or read book Critical Thinking for Strategic Intelligence written by Katherine Hibbs Pherson and published by CQ Press. This book was released on 2016-10-14 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Second Edition of Critical Thinking for Strategic Intelligence provides a basic introduction to the critical thinking skills employed within the intelligence community. This easy-to-use handbook is framed around twenty key questions that all analysts must ask themselves as they prepare to conduct research, generate hypotheses, evaluate sources of information, draft papers, and ultimately present analysis. Drawing upon their decades of teaching and analytic experience, Katherine Hibbs Pherson and Randolph H. Pherson have updated the book with useful graphics that diagram and display the processes and structured analytic techniques used to arrive at the best possible analytical product.

Signal Processing and Machine Learning for Biomedical Big Data

Author : Ervin Sejdic
Publisher : CRC Press
ISBN 13 : 149877346X
Total Pages : 624 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Signal Processing and Machine Learning for Biomedical Big Data by : Ervin Sejdic

Download or read book Signal Processing and Machine Learning for Biomedical Big Data written by Ervin Sejdic and published by CRC Press. This book was released on 2018-07-04 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This book’s material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.

Demystifying Big Data and Machine Learning for Healthcare

Author : Prashant Natarajan
Publisher : CRC Press
ISBN 13 : 1315389312
Total Pages : 210 pages
Book Rating : 4.3/5 (153 download)

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Book Synopsis Demystifying Big Data and Machine Learning for Healthcare by : Prashant Natarajan

Download or read book Demystifying Big Data and Machine Learning for Healthcare written by Prashant Natarajan and published by CRC Press. This book was released on 2017-02-15 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.

Learning and Collaboration Technologies. Novel Learning Ecosystems

Author : Panayiotis Zaphiris
Publisher : Springer
ISBN 13 : 3319585096
Total Pages : 516 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Learning and Collaboration Technologies. Novel Learning Ecosystems by : Panayiotis Zaphiris

Download or read book Learning and Collaboration Technologies. Novel Learning Ecosystems written by Panayiotis Zaphiris and published by Springer. This book was released on 2017-06-28 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 10295 and 10296 constitute the refereed proceedings of the 4th International Conference on Learning and Collaboration Technologies, LCT 2017, held as part of the 19th International Conference on Human-Computer Interaction, HCII 2017, in Vancouver, BC, Canada, in July 2017, in conjunction with 15 thematically similar conferences. The 1228 papers presented at the HCII 2017 conferences were carefully reviewed and selected from 4340 submissions. The papers cover the entire field of human-computer interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The papers included in this volume are organized in the following topical sections: multimodal and natural interaction for learning; learning and teaching ecosystems; e-learning, social media and MOOCs; beyond the classroom; and games and gamification for learning.

Research Anthology on Developing Effective Online Learning Courses

Author : Management Association, Information Resources
Publisher : IGI Global
ISBN 13 : 1799880974
Total Pages : 2104 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Research Anthology on Developing Effective Online Learning Courses by : Management Association, Information Resources

Download or read book Research Anthology on Developing Effective Online Learning Courses written by Management Association, Information Resources and published by IGI Global. This book was released on 2020-12-18 with total page 2104 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the current educational environment, there has been a shift towards online learning as a replacement for the traditional in-person classroom experience. With this new environment comes new technologies, benefits, and challenges for providing courses to students through an entirely digital environment. With this shift comes the necessary research on how to utilize these online courses and how to develop effective online educational materials that fit student needs and encourage student learning, motivation, and success. The optimization of these online tools requires a deeper look into curriculum, instructional design, teaching techniques, and new models for student assessment and evaluation. Information on how to create valuable online course content, engaging lesson plans for the digital space, and meaningful student activities online are only a few of many current topics of interest for promoting student achievement through online learning. The Research Anthology on Developing Effective Online Learning Courses provides multiple perspectives on how to develop engaging and effective online learning courses in the wake of the rapid digitalization of education. This book includes topics focused on online learners, online course content, effective online instruction strategies, and instructional design for the online environment. This reference work is ideal for curriculum developers, instructional designers, IT consultants, deans, chairs, teachers, administrators, academicians, researchers, and students interested in the latest research on how to create online learning courses that promote student success.

Big Data and Global Trade Law

Author : Mira Burri
Publisher : Cambridge University Press
ISBN 13 : 1108911463
Total Pages : 407 pages
Book Rating : 4.1/5 (89 download)

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Book Synopsis Big Data and Global Trade Law by : Mira Burri

Download or read book Big Data and Global Trade Law written by Mira Burri and published by Cambridge University Press. This book was released on 2021-07-29 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection explores the relevance of global trade law for data, big data and cross-border data flows. Contributing authors from different disciplines including law, economics and political science analyze developments at the World Trade Organization and in preferential trade venues by asking what future-oriented models for data governance are available and viable in the area of trade law and policy. The collection paints the broad picture of the interaction between digital technologies and trade regulation as well as provides in-depth analyses of critical to the data-driven economy issues, such as privacy and AI, and different countries' perspectives. This title is also available as Open Access on Cambridge Core.

Data Science for Undergraduates

Author : National Academies of Sciences, Engineering, and Medicine
Publisher : National Academies Press
ISBN 13 : 0309475597
Total Pages : 139 pages
Book Rating : 4.3/5 (94 download)

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Book Synopsis Data Science for Undergraduates by : National Academies of Sciences, Engineering, and Medicine

Download or read book Data Science for Undergraduates written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2018-11-11 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.

Big Data Is Not a Monolith

Author : Cassidy R. Sugimoto
Publisher : MIT Press
ISBN 13 : 0262529483
Total Pages : 308 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Big Data Is Not a Monolith by : Cassidy R. Sugimoto

Download or read book Big Data Is Not a Monolith written by Cassidy R. Sugimoto and published by MIT Press. This book was released on 2016-10-21 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Perspectives on the varied challenges posed by big data for health, science, law, commerce, and politics. Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identify linguistic correlations in large corpuses of texts. This book examines big data not as an undifferentiated whole but contextually, investigating the varied challenges posed by big data for health, science, law, commerce, and politics. Taken together, the chapters reveal a complex set of problems, practices, and policies. The advent of big data methodologies has challenged the theory-driven approach to scientific knowledge in favor of a data-driven one. Social media platforms and self-tracking tools change the way we see ourselves and others. The collection of data by corporations and government threatens privacy while promoting transparency. Meanwhile, politicians, policy makers, and ethicists are ill-prepared to deal with big data's ramifications. The contributors look at big data's effect on individuals as it exerts social control through monitoring, mining, and manipulation; big data and society, examining both its empowering and its constraining effects; big data and science, considering issues of data governance, provenance, reuse, and trust; and big data and organizations, discussing data responsibility, “data harm,” and decision making. Contributors Ryan Abbott, Cristina Alaimo, Kent R. Anderson, Mark Andrejevic, Diane E. Bailey, Mike Bailey, Mark Burdon, Fred H. Cate, Jorge L. Contreras, Simon DeDeo, Hamid R. Ekbia, Allison Goodwell, Jannis Kallinikos, Inna Kouper, M. Lynne Markus, Michael Mattioli, Paul Ohm, Scott Peppet, Beth Plale, Jason Portenoy, Julie Rennecker, Katie Shilton, Dan Sholler, Cassidy R. Sugimoto, Isuru Suriarachchi, Jevin D. West

Refining the Concept of Scientific Inference When Working with Big Data

Author : National Academies of Sciences, Engineering, and Medicine
Publisher : National Academies Press
ISBN 13 : 0309454476
Total Pages : 115 pages
Book Rating : 4.3/5 (94 download)

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Book Synopsis Refining the Concept of Scientific Inference When Working with Big Data by : National Academies of Sciences, Engineering, and Medicine

Download or read book Refining the Concept of Scientific Inference When Working with Big Data written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2017-02-24 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: The concept of utilizing big data to enable scientific discovery has generated tremendous excitement and investment from both private and public sectors over the past decade, and expectations continue to grow. Using big data analytics to identify complex patterns hidden inside volumes of data that have never been combined could accelerate the rate of scientific discovery and lead to the development of beneficial technologies and products. However, producing actionable scientific knowledge from such large, complex data sets requires statistical models that produce reliable inferences (NRC, 2013). Without careful consideration of the suitability of both available data and the statistical models applied, analysis of big data may result in misleading correlations and false discoveries, which can potentially undermine confidence in scientific research if the results are not reproducible. In June 2016 the National Academies of Sciences, Engineering, and Medicine convened a workshop to examine critical challenges and opportunities in performing scientific inference reliably when working with big data. Participants explored new methodologic developments that hold significant promise and potential research program areas for the future. This publication summarizes the presentations and discussions from the workshop.

Machine Learning for Decision Makers

Author : Patanjali Kashyap
Publisher : Apress
ISBN 13 : 1484229886
Total Pages : 381 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Machine Learning for Decision Makers by : Patanjali Kashyap

Download or read book Machine Learning for Decision Makers written by Patanjali Kashyap and published by Apress. This book was released on 2018-01-04 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.

Big Data

Author : Maribel Yasmina Santos
Publisher : CRC Press
ISBN 13 : 1000794032
Total Pages : 315 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Big Data by : Maribel Yasmina Santos

Download or read book Big Data written by Maribel Yasmina Santos and published by CRC Press. This book was released on 2022-09-01 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data is a concept of major relevance in today’s world, sometimes highlighted as a key asset for productivity growth, innovation, and customer relationship, whose popularity has increased considerably during the last years. Areas like smart cities, manufacturing, retail, finance, software development, environment, digital media, among others, can benefit from the collection, storage, processing, and analysis of Big Data, leveraging unprecedented data-driven workflows and considerably improved decision-making processes. The concept of a Big Data Warehouse (BDW) is emerging as either an augmentation or a replacement of the traditional Data Warehouse (DW), a concept that has a long history as one of the most valuable enterprise data assets. Nevertheless, research in Big Data Warehousing is still in its infancy, lacking an integrated and validated approach for designing and implementing both the logical layer (data models, data flows, and interoperability between components) and the physical layer (technological infrastructure) of these complex systems. This book addresses models and methods for designing and implementing Big Data Systems to support mixed and complex decision processes, giving special attention to BDWs as a way of efficiently storing and processing batch or streaming data for structured or semi-structured analytical problems.

Artificial Intelligence For Science: A Deep Learning Revolution

Author : Alok Choudhary
Publisher : World Scientific
ISBN 13 : 9811265682
Total Pages : 803 pages
Book Rating : 4.8/5 (112 download)

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Book Synopsis Artificial Intelligence For Science: A Deep Learning Revolution by : Alok Choudhary

Download or read book Artificial Intelligence For Science: A Deep Learning Revolution written by Alok Choudhary and published by World Scientific. This book was released on 2023-03-21 with total page 803 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique collection introduces AI, Machine Learning (ML), and deep neural network technologies leading to scientific discovery from the datasets generated both by supercomputer simulation and by modern experimental facilities.Huge quantities of experimental data come from many sources — telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities such as the Large Hadron Collider (LHC) at CERN in Geneva and the ITER Tokamak in France. These sources generate many petabytes moving to exabytes of data per year. Extracting scientific insights from these data is a major challenge for scientists, for whom the latest AI developments will be essential.The timely handbook benefits professionals, researchers, academics, and students in all fields of science and engineering as well as AI, ML, and neural networks. Further, the vision evident in this book inspires all those who influence or are influenced by scientific progress.