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Big Data Analytics: A Social Network Approach (Studies in Big Data #30)
by Ajith Abraham Mrutyunjaya Panda Aboul HassanienSocial networking has increased drastically in recent years, resulting in an increased amount of data being created daily. Furthermore, diversity of issues and complexity of the social networks pose a challenge in social network mining. Traditional algorithm software cannot deal with such complex and vast amounts of data, necessitating the development of novel analytic approaches and tools. This reference work deals with social network aspects ofbig data analytics. It covers theory, practices and challenges in social networking. The book spans numerous disciplines like neural networking, deep learning, artificial intelligence, visualization, e-learning in higher education, e-healthcare, security and intrusion detection.
Big Data Analytics: Applications in Business and Marketing
by Mansaf Alam Kiran ChaudharyBig Data Analytics: Applications in Business and Marketing explores the concepts and applications related to marketing and business as well as future research directions. It also examines how this emerging field could be extended to performance management and decision-making. Investment in business and marketing analytics can create value through proper allocation of resources and resource orchestration process. The use of data analytics tools can be used to diagnose and improve performance. The book is divided into five parts. The first part introduces data science, big data, and data analytics. The second part focuses on applications of business analytics including: Big data analytics and algorithm Market basket analysis Anticipating consumer purchase behavior Variation in shopping patterns Big data analytics for market intelligence The third part looks at business intelligence and features an evaluation study of churn prediction models for business Intelligence. The fourth part of the book examines analytics for marketing decision-making and the roles of big data analytics for market intelligence and of consumer behavior. The book concludes with digital marketing, marketing by consumer analytics, web analytics for digital marketing, and smart retailing. This book covers the concepts, applications and research trends of marketing and business analytics with the aim of helping organizations increase profitability by improving decision-making through data analytics.
Big Data Analytics: Digital Marketing and Decision-Making
by Mansaf Alam Kiran ChaudharyBig Data Analytics: Digital Marketing and Decision-Making covers the advances related to marketing and business analytics. Investment marketing analytics can create value through proper allocation of resources and resource orchestration processes. The use of data analytics tools can be used to improve and speed decision-making processes. Chapters examining analytics for decision-making cover such topics as: Big data analytics for gathering business intelligence Data analytics and consumer behavior The role of big data analytics in organizational decision-making This book also looks at digital marketing and focuses on such areas as: The prediction of marketing by consumer analytics Web analytics for digital marketing Smart retailing Leveraging web analytics for optimizing digital marketing strategies Big Data Analytics: Digital Marketing and Decision-Making aims to help organizations increase their profits by making better decisions on time through the use of data analytics. It is written for students, practitioners, industry professionals, researchers, and faculty working in the field of commerce and marketing, big data analytics, and organizational decision-making.
Big Data Analytics: Grundlagen, Fallbeispiele und Nutzungspotenziale (Edition HMD)
by Andreas Meier Sara D'OnofrioMit diesem Herausgeberwerk führen die Autoren den Begriff „Big Data Analytics“ ein und geben Fallstudien aus unterschiedlichen Anwendungsgebieten. Unter Big Data Analytics wird das Aufbereiten, Analysieren und Interpretieren von großen, oft heterogenen Datenbeständen verstanden, mit dem Ziel, Muster und Zusammenhänge in den Daten aufzudecken und Entscheidungsgrundlagen für wissenschaftliche, betriebliche oder gesellschaftliche Fragestellungen zu erhalten. Nebst den theoretischen Grundlagen widmet sich das Herausgeberwerk der Vielfalt verschiedener Anwendungsmöglichkeiten. Fallbeispiele geben Einblick in die Anwendung von Big Data Analytics und dessen Nutzenpotenziale. Das Werk richtet sich gleichermaßen an Studierende, Fachleute aller Fachrichtungen als auch an interessierte Anwender. Es hilft den Leserinnen und Leser, die Bedeutungsvielfalt des Begriffs Big Data Analytics zu verstehen und verschiedene Einsatzmöglichkeiten im eigenen Umfeld zu erkennen und zu bewerten.
Big Data Analytics: Methods and Applications
by Saumyadipta Pyne B. L. S. Prakasa Rao S. B. RaoThis book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.
Big Data Analytics: Proceedings of CSI 2015 (Advances in Intelligent Systems and Computing #654)
by Vasudha Bhatnagar Durgesh Kumar Mishra V. B. AggarwalThis volume comprises the select proceedings of the annual convention of the Computer Society of India. Divided into 10 topical volumes, the proceedings present papers on state-of-the-art research, surveys, and succinct reviews. The volumes cover diverse topics ranging from communications networks to big data analytics, and from system architecture to cyber security. This volume focuses on Big Data Analytics. The contents of this book will be useful to researchers and students alike.
Big Data Analytics: Systems, Algorithms, Applications
by Aneesh Sreevallabh Chivukula Aditya Mogadala Rohit Ghosh C.S.R. Prabhu L.M. Jenila LivingstonThis 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.
Big Data Analytics: Theory, Techniques, Platforms, and Applications
by Gagangeet Singh Aujla Anish Jindal Ümit Demirbaga Oğuzhan KalyonThis book introduces readers to big data analytics. It covers the background to and the concepts of big data, big data analytics, and cloud computing, along with the process of setting up, configuring, and getting familiar with the big data analytics working environments in the first two chapters. The third chapter provides comprehensive information on big data processing systems - from installing these systems to implementing real-world data applications, along with the necessary codes. The next chapter dives into the details of big data storage technologies, including their types, essentiality, durability, and availability, and reveals their differences in their properties. The fifth and sixth chapters guide the reader through understanding, configuring, and performing the monitoring and debugging of big data systems and present the available commercial and open-source tools for this purpose. Chapter seven gives information about a trending machine learning, Bayesian network: a probabilistic graphical model, by presenting a real-world probabilistic application to understand causal, complex, and hidden relationships for diagnosis and forecasting in a scalable manner for big data. Special sections throughout the eighth chapter present different case studies and applications to help the readers to develop their big data analytics skills using various big data analytics frameworks.The book will be of interest to business executives and IT managers as well as university students and their course leaders, in fact all those who want to get involved in the big data world.
Big Data Analytics: Tools and Technology for Effective Planning (Chapman & Hall/CRC Big Data Series)
by Ganesh Chandra Deka Arun K. SomaniThe proposed book will discuss various aspects of big data Analytics. It will deliberate upon the tools, technology, applications, use cases and research directions in the field. Chapters would be contributed by researchers, scientist and practitioners from various reputed universities and organizations for the benefit of readers.
Big Data Application Architecture Q & A: A Problem - Solution Approach
by Nitin Sawant Himanshu ShahBig Data Application Architecture Pattern Recipes provides an insight into heterogeneous infrastructures, databases, and visualization and analytics tools used for realizing the architectures of big data solutions. Its problem-solution approach helps in selecting the right architecture to solve the problem at hand. In the process of reading through these problems, you will learn harness the power of new big data opportunities which various enterprises use to attain real-time profits. Big Data Application Architecture Pattern Recipes answers one of the most critical questions of this time 'how do you select the best end-to-end architecture to solve your big data problem?'. The book deals with various mission critical problems encountered by solution architects, consultants, and software architects while dealing with the myriad options available for implementing a typical solution, trying to extract insight from huge volumes of data in real-time and across multiple relational and non-relational data types for clients from industries like retail, telecommunication, banking, and insurance. The patterns in this book provide the strong architectural foundation required to launch your next big data application. The architectures for realizing these opportunities are based on relatively less expensive and heterogeneous infrastructures compared to the traditional monolithic and hugely expensive options that exist currently. This book describes and evaluates the benefits of heterogeneity which brings with it multiple options of solving the same problem, evaluation of trade-offs and validation of 'fitness-for-purpose' of the solution.
Big Data Applications and Services 2017: The 4th International Conference on Big Data Applications and Services (Advances in Intelligent Systems and Computing #770)
by Wookey Lee Carson K. LeungThis proceedings volume contains selected papers from the Fourth International Conference on Big Data Applications and Services (BigDAS 2017), held in Tashkent, Uzbekistan on August 15-18, 2017. Big data has become a core technology providing innovative solutions in many fields including social media, healthcare and manufacturing. The Fourth International Conference on Big Data Applications and Services (BigDAS 2017) presented innovative results, encouraged academic and industrial interaction, and promoted collaborative research in the field of big data worldwide. The conference was organized by the Korea Big Data Services Society and National University of Uzbekistan.
Big Data Applications and Use Cases (International Series on Computer Entertainment and Media Technology)
by Patrick C. K. HungThis book presents different use cases in big data applications and related practical experiences. Many businesses today are increasingly interested in utilizing big data technologies for supporting their business intelligence so that it is becoming more and more important to understand the various practical issues from different practical use cases. This book provides clear proof that big data technologies are playing an ever increasing important and critical role in a new cross-discipline research between computer science and business.
Big Data Applications in Industry 4.0
by P. KalirajIndustry 4.0 is the latest technological innovation in manufacturing with the goal to increase productivity in a flexible and efficient manner. Changing the way in which manufacturers operate, this revolutionary transformation is powered by various technology advances including Big Data analytics, Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing. Big Data analytics has been identified as one of the significant components of Industry 4.0, as it provides valuable insights for smart factory management. Big Data and Industry 4.0 have the potential to reduce resource consumption and optimize processes, thereby playing a key role in achieving sustainable development. Big Data Applications in Industry 4.0 covers the recent advancements that have emerged in the field of Big Data and its applications. The book introduces the concepts and advanced tools and technologies for representing and processing Big Data. It also covers applications of Big Data in such domains as financial services, education, healthcare, biomedical research, logistics, and warehouse management. Researchers, students, scientists, engineers, and statisticians can turn to this book to learn about concepts, technologies, and applications that solve real-world problems. Features An introduction to data science and the types of data analytics methods accessible today An overview of data integration concepts, methodologies, and solutions A general framework of forecasting principles and applications, as well as basic forecasting models including naïve, moving average, and exponential smoothing models A detailed roadmap of the Big Data evolution and its related technological transformation in computing, along with a brief description of related terminologies The application of Industry 4.0 and Big Data in the field of education The features, prospects, and significant role of Big Data in the banking industry, as well as various use cases of Big Data in banking, finance services, and insurance Implementing a Data Lake (DL) in the cloud and the significance of a data lake in decision making
Big Data Architect’s Handbook: A guide to building proficiency in tools and systems used by leading big data experts
by Syed Muhammad AkhtarA comprehensive end-to-end guide that gives hands-on practice in big data and Artificial IntelligenceKey FeaturesLearn to build and run a big data application with sample codeExplore examples to implement activities that a big data architect performsUse Machine Learning and AI for structured and unstructured dataBook DescriptionThe big data architects are the “masters” of data, and hold high value in today’s market. Handling big data, be it of good or bad quality, is not an easy task. The prime job for any big data architect is to build an end-to-end big data solution that integrates data from different sources and analyzes it to find useful, hidden insights.Big Data Architect’s Handbook takes you through developing a complete, end-to-end big data pipeline, which will lay the foundation for you and provide the necessary knowledge required to be an architect in big data. Right from understanding the design considerations to implementing a solid, efficient, and scalable data pipeline, this book walks you through all the essential aspects of big data. It also gives you an overview of how you can leverage the power of various big data tools such as Apache Hadoop and ElasticSearch in order to bring them together and build an efficient big data solution.By the end of this book, you will be able to build your own design system which integrates, maintains, visualizes, and monitors your data. In addition, you will have a smooth design flow in each process, putting insights in action.What you will learn Learn Hadoop Ecosystem and Apache projects Understand, compare NoSQL database and essential software architecture Cloud infrastructure design considerations for big data Explore application scenario of big data tools for daily activities Learn to analyze and visualize results to uncover valuable insights Build and run a big data application with sample code from end to end Apply Machine Learning and AI to perform big data intelligence Practice the daily activities performed by big data architectsWho this book is forBig Data Architect’s Handbook is for you if you are an aspiring data professional, developer, or IT enthusiast who aims to be an all-round architect in big data. This book is your one-stop solution to enhance your knowledge and carry out easy to complex activities required to become a big data architect.
Big Data Benchmarking: 5th International Workshop, WBDB 2014, Potsdam, Germany, August 5-6- 2014, Revised Selected Papers (Lecture Notes in Computer Science #8991)
by Chaitanya Baru Tilmann Rabl Kai Sachs Meikel Poess Hans-Arno JacobsonThis book constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Big Data Benchmarking, WBDB 2014, held in Potsdam, Germany, in August 2014. The 13 papers presented in this book were carefully reviewed and selected from numerous submissions and cover topics such as benchmarks specifications and proposals, Hadoop and MapReduce - in the different context such as virtualization and cloud - as well as in-memory, data generation, and graphs.
Big Data Benchmarking: 6th International Workshop, WBDB 2015, Toronto, ON, Canada, June 16-17, 2015 and 7th International Workshop, WBDB 2015, New Delhi, India, December 14-15, 2015, Revised Selected Papers (Lecture Notes in Computer Science #10044)
by Tilmann Rabl, Raghunath Nambiar, Chaitanya Baru, Milind Bhandarkar, Meikel Poess and Saumyadipta PyneThis book constitutes the thoroughly refereed post-workshop proceedings of the 6th International Workshop on Big Data Benchmarking, WBDB 2015, held in Toronto, ON, Canada, in June 2015 and the 7th International Workshop, WBDB 2015, held in New Delhi, India, in December 2015. The 8 full papers presented in this book were carefully reviewed and selected from 22 submissions. They deal with recent trends in big data and HPC convergence, new proposals for big data benchmarking, as well as tooling and performance results.
Big Data Benchmarks, Performance Optimization, and Emerging Hardware: 4th and 5th Workshops, BPOE 2014, Salt Lake City, USA, March 1, 2014 and Hangzhou, China, September 5, 2014, Revised Selected Papers (Lecture Notes in Computer Science #8807)
by Jianfeng Zhan Rui Han Chuliang WengThis book constitutes the thoroughly revised selected papers of the 4th and 5th workshops on Big Data Benchmarks, Performance Optimization, and Emerging Hardware, BPOE 4 and BPOE 5, held respectively in Salt Lake City, in March 2014, and in Hangzhou, in September 2014. The 16 papers presented were carefully reviewed and selected from 30 submissions. Both workshops focus on architecture and system support for big data systems, such as benchmarking; workload characterization; performance optimization and evaluation; emerging hardware.
Big Data Benchmarks, Performance Optimization, and Emerging Hardware: 6th Workshop, BPOE 2015, Kohala, HI, USA, August 31 - September 4, 2015. Revised Selected Papers (Lecture Notes in Computer Science #9495)
by Jianfeng Zhan Rui Han Roberto V. ZicariThis book constitutes the thoroughly revised selectedpapers of the 6th workshop on Big Data Benchmarks, Performance Optimization,and Emerging Hardware, BPOE 2015, held in Kohala Coast, HI, USA, inAugust/September 2015 as satellite event of VLDB 2015, the 41st InternationalConference on Very Large Data Bases. The 8 papers presented were carefully reviewedand selected from 10 submissions. The workshop focuses on architecture andsystem support for big data systems, aiming at bringing researchers andpractitioners from data management, architecture, and systems researchcommunities together to discuss the research issues at the intersection ofthese areas. This book also invites three papers from several industrialpartners, including two papers describing tools used in system benchmarking andmonitoring and one paper discussing principles and methodologies in existingbig data benchmarks.
Big Data Bibliography
by Safari Books Online Content TeamThe exponentially decreasing costs of data storage combined with the soaring volume of data being captured presents challenges and opportunities to those who work in the new frontiers of data science. Businesses, government agencies, and scientists leveraging data-based decisions are more successful than those relying on decades of trial-and-error. But taming and harnessing big data can be a herculean undertaking. The data must be collected, processed and distilled, analyzed, and presented in a manner humans can understand. Because there are no degrees in data science, data scientists must grow into their roles. If you are looking for resources to help you better understand big data and analytics, this is your guide. Tap into books and videos that cover everything from business intelligence to data mining and machine learning, to cloud services and NoSql Data Stores to data visualization.
Big Data Bootcamp: What Managers Need to Know to Profit from the Big Data Revolution
by David FeinleibInvestors and technology gurus have called big data one of the most important trends to come along in decades. Big Data Bootcamp explains what big data is and how you can use it in your company to become one of tomorrow's market leaders. Along the way, it explains the very latest technologies, companies, and advancements. Big data holds the keys to delivering better customer service, offering more attractive products, and unlocking innovation. That's why, to remain competitive, every organization should become a big data company. It's also why every manager and technology professional should become knowledgeable about big data and how it is transforming not just their own industries but the global economy. And that knowledge is just what this book delivers. It explains components of big data like Hadoop and NoSQL databases; how big data is compiled, queried, and analyzed; how to create a big data application; and the business sectors ripe for big data-inspired products and services like retail, healthcare, finance, and education. Best of all, your guide is David Feinleib, renowned entrepreneur, venture capitalist, and author of Why Startups Fail. Feinleib's Big Data Landscape, a market map featured and explained in the book, is an industry benchmark that has been viewed more than 150,000 times and is used as a reference by VMWare, Dell, Intel, the U. S. Government Accountability Office, and many other organizations. Feinleib also explains: * Why every businessperson needs to understand the fundamentals of big data or get run over by those who do * How big data differs from traditional database management systems * How to create and run a big data project * The technical details powering the big data revolution Whether you're a Fortune 500 executive or the proprietor of a restaurant or web design studio, Big Data Bootcamp will explain how you can take full advantage of new technologies to transform your company and your career. What you'll learn Which big data technologies are the most compelling and how to get started using them How the intersection of big data, cloud computing, and mobile technologies is opening up huge new markets and opportunities for entrepreneurs and existing businesses alike How to find and put to use valuable data sets How marketers can use big data applications to pinpoint and appeal to the most likely customers How to make much better decisions using big data Who this book is for The target audience for this book is business and IT professionals, investors, managers, entrepreneurs and small business owners, and government officials. Table of Contents Big Data The Big Data Landscape Your Big Data Roadmap Big Data at Work Why a Picture Is Worth a Thousand Words The Intersection of Big Data, Mobile, and Cloud Computing Doing a Big Data Project The Next Billion-Dollar IPO: Big Data Entrepreneurship Reach More Customers with Better Data--and Products How Big Data Is Changing the Way We Live Big Data Opportunities in Education Capstone Case Study: Big Data Meets Romance Big Data Resources
Big Data Computing
by Rajendra AkerkarDue to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis. Presenting a mix
Big Data Computing and Communications: Second International Conference, BigCom 2016, Shenyang, China, July 29-31, 2016. Proceedings (Lecture Notes in Computer Science #9784)
by Zhu Han Yu Wang Yanyong Zhang Ge Yu Guoren WangThis book constitutes the proceedings of the First International Conference on Big Data Computing and Communications, BigCom 2015, held in Taiyuan, China, in August 2015. The 41 papers presented in this volume were carefully reviewed and selected from 74 submissions. They were organized in topical sections named: wireless communication and networks; database and big data; smart phone and sensing application; security and privacy; architecture and applications; sensor networks and RFID; social networks and recommendation; signal processing and pattern recognition; and routing and resource management.
Big Data Computing: A Guide for Business and Technology Managers (Chapman & Hall/CRC Big Data Series)
by Vivek KaleThis book unravels the mystery of Big Data computing and its power to transform business operations. The approach it uses will be helpful to any professional who must present a case for realizing Big Data computing solutions or to those who could be involved in a Big Data computing project. It provides a framework that enables business and technical managers to make optimal decisions necessary for the successful migration to Big Data computing environments and applications within their organizations.
Big Data Computing: Advances in Technologies, Methodologies, and Applications (Computational Intelligence Techniques)
by Bishwajeet Kumar Pandey Tanvir Habib SardarThis book primarily aims to provide an in-depth understanding of recent advances in big data computing technologies, methodologies, and applications along with introductory details of big data computing models such as Apache Hadoop, MapReduce, Hive, Pig, Mahout in-memory storage systems, NoSQL databases, and big data streaming services such as Apache Spark, Kafka, and so forth. It also covers developments in big data computing applications such as machine learning, deep learning, graph processing, and many others. Features: Provides comprehensive analysis of advanced aspects of big data challenges and enabling technologies. Explains computing models using real-world examples and dataset-based experiments. Includes case studies, quality diagrams, and demonstrations in each chapter. Describes modifications and optimization of existing technologies along with the novel big data computing models. Explores references to machine learning, deep learning, and graph processing. This book is aimed at graduate students and researchers in high-performance computing, data mining, knowledge discovery, and distributed computing.
Big Data Concepts, Technologies, and Applications
by Mohammad Shahid Husain Mohammad Zunnun Khan Tamanna SiddiquiWith the advent of such advanced technologies as cloud computing, the Internet of Things, the Medical Internet of Things, the Industry Internet of Things and sensor networks as well as the exponential growth in the usage of Internet-based and social media platforms, there are enormous oceans of data. These huge volumes of data can be used for effective decision making and improved performance if analyzed properly. Due to its inherent characteristics, big data is very complex and cannot be handled and processed by traditional database management approaches. There is a need for sophisticated approaches, tools and technologies that can be used to store, manage and analyze these enormous amounts of data to make the best use of them. Big Data Concepts, Technologies, and Applications covers the concepts, technologies, and applications of big data analytics. Presenting the state-of-the-art technologies in use for big data analytics. it provides an in-depth discussion about the important sectors where big data analytics has proven to be very effective in improving performance and helping industries to remain competitive. This book provides insight into the novel areas of big data analytics and the research directions for the scholars working in the domain. Highlights include: The advantages, disadvantages and challenges of big data analytics State-of-the-art technologies for big data analytics such as Hadoop, NoSQL databases, data lakes, deep learning and blockchain The application of big data analytic in healthcare, business, social media analytics, fraud detection and prevention and governance Exploring the concepts and technologies behind big data analytics, the book is an ideal resource for researchers, students, data scientists, data analysts and business analysts who need insight into big data analytics