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HPCC Systems is an Open Source Big Data supercomputing platform that is an alternative to the Hadoop and Spark worlds. The Mastering HPCC Systems series introduces the HPCC Systems platform to anyone interested in evaluating it for use on their own big data projects. It also expands the ECL programming knowledge of anyone already working with the platform. This Fundamentals of ETL Processing volume provides an introduction to the ECL language through hands-on working through the standard data ingest process common to all Big Data projects. It starts with acquiring data and importing it into the HPCC Systems platform. It then takes you through data exploration, cleaning, and standardization processes. It ends by using that transformed data to create a data product ready for delivery to end-users.
HPCC Systems is an Open Source Big Data supercomputing platform that is an alternative to the Hadoop and Spark worlds. The Mastering HPCC Systems series introduces the HPCC Systems platform to anyone interested in evaluating it for use on their own big data projects. It also expands the ECL programming knowledge of anyone already working with the platform. This ECL Cookbook volume provides a large number of discrete "tips and tricks" articles that describe various useful ECL programming techniques not already covered in the other volumes of this series. The articles are organized into sections that cover working with data files, index files, strings, dates, sets, and math functions. The code described provides a very useful set of library functions that can be used as-is, or as the basis for your extrapolation to solve related problems using the same techniques.
HPCC Systems is an Open Source Big Data supercomputing platform that is an alternative to the Hadoop and Spark worlds. The Mastering HPCC Systems series introduces the HPCC Systems platform to anyone interested in evaluating it for use on their own big data projects. It also expands the ECL programming knowledge of anyone already working with the platform. This Platform Overview and History volume provides an overview of the platform's infrastructure, design, history, and terminology. It introduces the components to new users and provides a high-level overview for executives whose organization is implementing the HPCC Systems platform.
Great POSSIBILITIES and high future prospects to become ten times folds in the near FUTUREKey features Comprehensively gives clear picture of current state-of-the-art aspect of cloud computing by elaborating terminologies, models and other related terms. Enlightens all major players in Cloud Computing industry providing services in terms of SaaS, PaaS and IaaS. Highlights Cloud Computing Simulators, Security Aspect and Resource Allocation. In-depth presentation with well-illustrated diagrams and simple to understand technical concepts of cloud. Description The book "e;Handbook of Cloud Computing"e; provides the latest and in-depth information of this relatively new and another platform for scientific computing which has great possibilities and high future prospects to become ten folds in near future. The book covers in comprehensive manner all aspects and terminologies associated with cloud computing like SaaS, PaaS and IaaS and also elaborates almost every cloud computing service model.The book highlights several other aspects of cloud computing like Security, Resource allocation, Simulation Platforms and futuristic trend i.e. Mobile cloud computing. The book will benefit all the readers with all in-depth technical information which is required to understand current and futuristic concepts of cloud computing. No prior knowledge of cloud computing or any of its related technology is required in reading this book. What will you learn Cloud Computing, Virtualisation Software as a Service, Platform as a Service, Infrastructure as a Service Data in Cloud and its Security Cloud Computing - Simulation, Mobile Cloud Computing Specific Cloud Service Models Resource Allocation in Cloud Computing Who this book is for Students of Polytechnic Diploma Classes- Computer Science/ Information Technology Graduate Students- Computer Science/ CSE / IT/ Computer Applications Master Class Students-Msc (CS/IT)/ MCA/ M.Phil, M.Tech, M.S. Researcher's-Ph.D Research Scholars doing work in Virtualization, Cloud Computing and Cloud Security Industry Professionals- Preparing for Certifications, Implementing Cloud Computing and even working on Cloud Security Table of contents1. Introduction to Cloud Computing2. Virtualisation3. Software as a Service4. Platform as a Service5. Infrastructure as a Service6. Data in Cloud7. Cloud Security 8. Cloud Computing - Simulation9. Specific Cloud Service Models10. Resource Allocation in Cloud Computing11. Mobile Cloud Computing About the authorDr. Anand Nayyar received Ph.D (Computer Science) in Wireless Sensor Networks and Swarm Intelligence. Presently he is working in Graduate School, Duy Tan University, Da Nang, Vietnam. He has total of fourteen Years of Teaching, Research and Consultancy experience with more than 250 Research Papers in various International Conferences and highly reputed journals. He is certified Professional with more than 75 certificates and member of 50 Professional Organizations. He is acting as "e;ACM DISTINGUISHED SPEAKER"e;
The primary purpose of this book is to capture the state-of-the-art in Cloud Computing technologies and applications. The book will also aim to identify potential research directions and technologies that will facilitate creation a global market-place of cloud computing services supporting scientific, industrial, business, and consumer applications. We expect the book to serve as a reference for larger audience such as systems architects, practitioners, developers, new researchers and graduate level students. This area of research is relatively recent, and as such has no existing reference book that addresses it. This book will be a timely contribution to a field that is gaining considerable research interest, momentum, and is expected to be of increasing interest to commercial developers. The book is targeted for professional computer science developers and graduate students especially at Masters level. As Cloud Computing is recognized as one of the top five emerging technologies that will have a major impact on the quality of science and society over the next 20 years, its knowledge will help position our readers at the forefront of the field.
Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications. To help realize Big Data's full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues. - Covers computational platforms supporting Big Data applications - Addresses key principles underlying Big Data computing - Examines key developments supporting next generation Big Data platforms - Explores the challenges in Big Data computing and ways to overcome them - Contains expert contributors from both academia and industry
This book includes selected papers from the International Conference on Data Science and Intelligent Applications (ICDSIA 2020), hosted by Gandhinagar Institute of Technology (GIT), Gujarat, India, on January 24–25, 2020. The proceedings present original and high-quality contributions on theory and practice concerning emerging technologies in the areas of data science and intelligent applications. The conference provides a forum for researchers from academia and industry to present and share their ideas, views and results, while also helping them approach the challenges of technological advancements from different viewpoints. The contributions cover a broad range of topics, including: collective intelligence, intelligent systems, IoT, fuzzy systems, Bayesian networks, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, speech processing, machine learning and deep learning, and intelligent applications and systems. Helping strengthen the links between academia and industry, the book offers a valuable resource for instructors, students, industry practitioners, engineers, managers, researchers, and scientists alike.
Blockchain technology is defined as a decentralized system of distributed registers that are used to record data transactions on multiple computers. The reason this technology has gained popularity is that you can put any digital asset or transaction in the blocking chain, the industry does not matter. Blockchain technology has infiltrated all areas of our lives, from manufacturing to healthcare and beyond. Cybersecurity is an industry that has been significantly affected by this technology and may be more so in the future. Blockchain for Cybersecurity and Privacy: Architectures, Challenges, and Applications is an invaluable resource to discover the blockchain applications for cybersecurity and privacy. The purpose of this book is to improve the awareness of readers about blockchain technology applications for cybersecurity and privacy. This book focuses on the fundamentals, architectures, and challenges of adopting blockchain for cybersecurity. Readers will discover different applications of blockchain for cybersecurity in IoT and healthcare. The book also includes some case studies of the blockchain for e-commerce online payment, retention payment system, and digital forensics. The book offers comprehensive coverage of the most essential topics, including: Blockchain architectures and challenges Blockchain threats and vulnerabilities Blockchain security and potential future use cases Blockchain for securing Internet of Things Blockchain for cybersecurity in healthcare Blockchain in facilitating payment system security and privacy This book comprises a number of state-of-the-art contributions from both scientists and practitioners working in the fields of blockchain technology and cybersecurity. It aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this particular area or those interested in grasping its diverse facets and exploring the latest advances on the blockchain for cybersecurity and privacy.
This book includes high-quality papers presented at the International Conference on Data Science and Management (ICDSM 2019), organised by the Gandhi Institute for Education and Technology, Bhubaneswar, from 22 to 23 February 2019. It features research in which data science is used to facilitate the decision-making process in various application areas, and also covers a wide range of learning methods and their applications in a number of learning problems. The empirical studies, theoretical analyses and comparisons to psychological phenomena described contribute to the development of products to meet market demands.
This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning – including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.