Applications of Big Data Analytics
Applications of Big Data Analytics

Author: Mohammed M. Alani

Publisher: Springer

Published: 2019-02-09

Total Pages: 0

ISBN-13: 9783030094973

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This timely text/reference reviews the state of the art of big data analytics, with a particular focus on practical applications. An authoritative selection of leading international researchers present detailed analyses of existing trends for storing and analyzing big data, together with valuable insights into the challenges inherent in current approaches and systems. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, education, and disaster management. The text also covers, typically from an application-oriented perspective, advances in data science in such areas as big data collection, searching, analysis, and knowledge discovery. Topics and features: Discusses a model for data traffic aggregation in 5G cellular networks, and a novel scheme for resource allocation in 5G networks with network slicing Explores methods that use big data in the assessment of flood risks, and apply neural networks techniques to monitor the safety of nuclear power plants Describes a system which leverages big data analytics and the Internet of Things in the application of drones to aid victims in disaster scenarios Proposes a novel deep learning-based health data analytics application for sleep apnea detection, and a novel pathway for diagnostic models of headache disorders Reviews techniques for educational data mining and learning analytics, and introduces a scalable MapReduce graph partitioning approach for high degree vertices Presents a multivariate and dynamic data representation model for the visualization of healthcare data, and big data analytics methods for software reliability assessment This practically-focused volume is an invaluable resource for all researchers, academics, data scientists and business professionals involved in the planning, designing, and implementation of big data analytics projects. Dr. Mohammed M. Alani is an Associate Professor in Computer Engineering and currently is the Provost at Al Khawarizmi International College, Abu Dhabi, UAE. Dr. Hissam Tawfik is a Professor of Computer Science in the School of Computing, Creative Technologies & Engineering at Leeds Beckett University, UK. Dr. Mohammed Saeed is a Professor in Computing and currently is the Vice President for Academic Affairs and Research at the University of Modern Sciences, Dubai, UAE. Dr. Obinna Anya is a Research Staff Member at IBM Research – Almaden, San Jose, CA, USA.

Data Mining and Learning Analytics
Data Mining and Learning Analytics

Author: Samira ElAtia

Publisher: John Wiley & Sons

Published: 2016-09-20

Total Pages: 351

ISBN-13: 1118998219

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Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.

Smart Universities
Smart Universities

Author: Vladimir L. Uskov

Publisher: Springer

Published: 2017-05-18

Total Pages: 435

ISBN-13: 3319594540

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This book presents peer-reviewed contributions on smart universities by various international research, design and development teams. Smart university is an emerging and rapidly evolving area that creatively integrates innovative concepts; smart software and hardware systems; smart classrooms with state-of-the-art technologies and technical platforms; smart pedagogy based on modern teaching and learning strategies; smart learning and academic analytics; as well as various branches of computer science and computer engineering. The contributions are grouped into several parts: Part 1—Smart Universities: Literature Review and Creative Analysis, Part 2—Smart Universities: Concepts, Systems and Technologies, Part 3—Smart Education: Approaches and Best Practices, and Part 4—Smart Universities: Smart Long Life Learning. The book is a valuable source of research data and findings, design and development outcomes, and best practices for faculty, scholars, Ph.D students, administrators, practitioners and anyone interested in the rapidly growing areas of smart university and smart education.

Muscle Relaxants
Muscle Relaxants

Author: Kazuaki Fukushima

Publisher: Springer Science & Business Media

Published: 2013-06-29

Total Pages: 412

ISBN-13: 4431668969

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At the Fifth International Neuromuscular Meeting held in Tokyo in 1994, leading experts in the field came together to discuss the physiology and pharmacology of neuromuscular receptor sites and neuromuscular blocking agents (NBAs). The proceedings of the meeting present a review of the history of muscle relaxants and a comprehensive examination of recent research, with a primary focus on clinical considerations. Among the topics covered by specific chapters are aspects of the neuromuscular junction (NMJ), pharmacokinetics, metabolism and metabolites of neuromuscular blocking agents, and drug interaction. Presenting the most up-to-date knowledge of the physiology and pharmacology of the NMJ and NBAs, this volume will be highly valuable to clinicians and researchers in anesthesiology, physiology, and pharmacology.

Comparative Stylistics of French and English
Comparative Stylistics of French and English

Author: Jean-Paul Vinay

Publisher: John Benjamins Publishing

Published: 1995

Total Pages: 383

ISBN-13: 902721610X

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The Stylistique comparée du français et de l'anglais has become a standard text in the French-speaking world for the study of comparative stylistics and the training of translators. This updated, first English edition makes Vinay & Darbelnet's classic methodology of translation available to a wider readership. The translation-oriented contrastive grammatical and stylistic analyses of the two languages are extensively exemplified by expressions, phrases and texts. Combining description with methodological guidelines for translation, this volume serves both as a course book and ­ through its detailed index and glossary ­ as a reference manual for specific translation problems.

bookdown
bookdown

Author: Yihui Xie

Publisher: CRC Press

Published: 2016-12-12

Total Pages: 140

ISBN-13: 1351792601

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bookdown: Authoring Books and Technical Documents with R Markdown presents a much easier way to write books and technical publications than traditional tools such as LaTeX and Word. The bookdown package inherits the simplicity of syntax and flexibility for data analysis from R Markdown, and extends R Markdown for technical writing, so that you can make better use of document elements such as figures, tables, equations, theorems, citations, and references. Similar to LaTeX, you can number and cross-reference these elements with bookdown. Your document can even include live examples so readers can interact with them while reading the book. The book can be rendered to multiple output formats, including LaTeX/PDF, HTML, EPUB, and Word, thus making it easy to put your documents online. The style and theme of these output formats can be customized. We used books and R primarily for examples in this book, but bookdown is not only for books or R. Most features introduced in this book also apply to other types of publications: journal papers, reports, dissertations, course handouts, study notes, and even novels. You do not have to use R, either. Other choices of computing languages include Python, C, C++, SQL, Bash, Stan, JavaScript, and so on, although R is best supported. You can also leave out computing, for example, to write a fiction. This book itself is an example of publishing with bookdown and R Markdown, and its source is fully available on GitHub.

Big Data on Campus
Big Data on Campus

Author: Karen L. Webber

Publisher: Johns Hopkins University Press

Published: 2020-11-03

Total Pages: 337

ISBN-13: 1421439034

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How data-informed decision making can make colleges and universities more effective institutions. The continuing importance of data analytics is not lost on higher education leaders, who face a multitude of challenges, including increasing operating costs, dwindling state support, limits to tuition increases, and increased competition from the for-profit sector. To navigate these challenges, savvy leaders must leverage data to make sound decisions. In Big Data on Campus, leading data analytics experts and higher ed leaders show the role that analytics can play in the better administration of colleges and universities. Aimed at senior administrative leaders, practitioners of institutional research, technology professionals, and graduate students in higher education, the book opens with a conceptual discussion of the roles that data analytics can play in higher education administration. Subsequent chapters address recent developments in technology, the rapid accumulation of data assets, organizational maturity in building analytical capabilities, and methodological advancements in developing predictive and prescriptive analytics. Each chapter includes a literature review of the research and application of analytics developments in their respective functional areas, a discussion of industry trends, examples of the application of data analytics in their decision process, and other related issues that readers may wish to consider in their own organizational environment to find opportunities for building robust data analytics capabilities. Using a series of focused discussions and case studies, Big Data on Campus helps readers understand how analytics can support major organizational functions in higher education, including admission decisions, retention and enrollment management, student life and engagement, academic and career advising, student learning and assessment, and academic program planning. The final section of the book addresses major issues and human factors involved in using analytics to support decision making; the ethical, cultural, and managerial implications of its use; the role of university leaders in promoting analytics in decision making; and the need for a strong campus community to embrace the analytics revolution. Contributors: Rana Glasgal, J. Michael Gower, Tom Gutman, Brian P. Hinote, Braden J. Hosch, Aditya Johri, Christine M. Keller, Carrie Klein, Jaime Lester, Carrie Hancock Marcinkevage, Gail B. Marsh, Susan M. Menditto, Jillian N. Morn, Valentina Nestor, Cathy O'Bryan, Huzefa Rangwala, Timothy Renick, Charles Tegen, Rachit Thariani, Chris Tompkins, Lindsay K. Wayt, Karen L. Webber, Henry Y. Zheng, Ying Zhou

Adoption and impact of OER in the Global South
Adoption and impact of OER in the Global South

Author: Hodgkinson-Williams, Cheryl

Publisher: African Minds

Published: 2018-01-05

Total Pages: 610

ISBN-13: 1928331483

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Education in the Global South faces several key interrelated challenges, for which Open Educational Resources (OER) are seen to be part of the solution. These challenges include: unequal access to education; variable quality of educational resources, teaching, and student performance; and increasing cost and concern about the sustainability of education. The Research on Open Educational Resources for Development (ROER4D) project seeks to build on and contribute to the body of research on how OER can help to improve access, enhance quality and reduce the cost of education in the Global South. This volume examines aspects of educator and student adoption of OER and engagement in Open Educational Practices (OEP) in secondary and tertiary education as well as teacher professional development in 21 countries in South America, Sub-Saharan Africa and South and Southeast Asia. The ROER4D studies and syntheses presented here aim to help inform Open Education advocacy, policy, practice and research in developing countries.

Life Cycle Management
Life Cycle Management

Author: Arne Remmen

Publisher: UNEP/Earthprint

Published: 2007

Total Pages: 56

ISBN-13: 9789280727722

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The journey towards sustainability requires that companies must find innovative ways to make profits and at the same time extend the traditional boundaries of business to include the environmental and social dimensions, a process known as Life Cycle Thinking. This Guide contains many examples illustrating how business organizations are putting Life Cycle Thinking into practice all over the world.

Data Warehousing in the Age of Big Data
Data Warehousing in the Age of Big Data

Author: Krish Krishnan

Publisher: Newnes

Published: 2013-05-02

Total Pages: 371

ISBN-13: 0124059201

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Data Warehousing in the Age of the Big Data will help you and your organization make the most of unstructured data with your existing data warehouse. As Big Data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how Big Data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses Big Data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture options, workloads, and integration techniques for Big Data and the data warehouse. Part 3 deals with data governance, data visualization, information life-cycle management, data scientists, and implementing a Big Data–ready data warehouse. Extensive appendixes include case studies from vendor implementations and a special segment on how we can build a healthcare information factory. Ultimately, this book will help you navigate through the complex layers of Big Data and data warehousing while providing you information on how to effectively think about using all these technologies and the architectures to design the next-generation data warehouse. - Learn how to leverage Big Data by effectively integrating it into your data warehouse. - Includes real-world examples and use cases that clearly demonstrate Hadoop, NoSQL, HBASE, Hive, and other Big Data technologies - Understand how to optimize and tune your current data warehouse infrastructure and integrate newer infrastructure matching data processing workloads and requirements