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| big data case study: Big Data in Practice Bernard Marr, 2016-03-22 The best-selling author of Big Data is back, this time with a unique and in-depth insight into how specific companies use big data. Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario. Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking Develop your own big data strategy by accessing additional reading materials at the end of each chapter |
| big data case study: Too Big to Ignore Phil Simon, 2015-11-02 Residents in Boston, Massachusetts are automatically reporting potholes and road hazards via their smartphones. Progressive Insurance tracks real-time customer driving patterns and uses that information to offer rates truly commensurate with individual safety. Google accurately predicts local flu outbreaks based upon thousands of user search queries. Amazon provides remarkably insightful, relevant, and timely product recommendations to its hundreds of millions of customers. Quantcast lets companies target precise audiences and key demographics throughout the Web. NASA runs contests via gamification site TopCoder, awarding prizes to those with the most innovative and cost-effective solutions to its problems. Explorys offers penetrating and previously unknown insights into healthcare behavior. How do these organizations and municipalities do it? Technology is certainly a big part, but in each case the answer lies deeper than that. Individuals at these organizations have realized that they don't have to be Nate Silver to reap massive benefits from today's new and emerging types of data. And each of these organizations has embraced Big Data, allowing them to make astute and otherwise impossible observations, actions, and predictions. It's time to start thinking big. In Too Big to Ignore, recognized technology expert and award-winning author Phil Simon explores an unassailably important trend: Big Data, the massive amounts, new types, and multifaceted sources of information streaming at us faster than ever. Never before have we seen data with the volume, velocity, and variety of today. Big Data is no temporary blip of fad. In fact, it is only going to intensify in the coming years, and its ramifications for the future of business are impossible to overstate. Too Big to Ignore explains why Big Data is a big deal. Simon provides commonsense, jargon-free advice for people and organizations looking to understand and leverage Big Data. Rife with case studies, examples, analysis, and quotes from real-world Big Data practitioners, the book is required reading for chief executives, company owners, industry leaders, and business professionals. |
| big data case study: Big Data Applications and Use Cases Patrick C. K. Hung, 2016-05-18 This 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 case study: Guide to Big Data Applications S. Srinivasan, 2017-05-25 This handbook brings together a variety of approaches to the uses of big data in multiple fields, primarily science, medicine, and business. This single resource features contributions from researchers around the world from a variety of fields, where they share their findings and experience. This book is intended to help spur further innovation in big data. The research is presented in a way that allows readers, regardless of their field of study, to learn from how applications have proven successful and how similar applications could be used in their own field. Contributions stem from researchers in fields such as physics, biology, energy, healthcare, and business. The contributors also discuss important topics such as fraud detection, privacy implications, legal perspectives, and ethical handling of big data. |
| big data case study: Introducing Data Science Davy Cielen, Arno Meysman, 2016-05-02 Summary Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Many companies need developers with data science skills to work on projects ranging from social media marketing to machine learning. Discovering what you need to learn to begin a career as a data scientist can seem bewildering. This book is designed to help you get started. About the Book Introducing Data ScienceIntroducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You’ll explore data visualization, graph databases, the use of NoSQL, and the data science process. You’ll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science. What’s Inside Handling large data Introduction to machine learning Using Python to work with data Writing data science algorithms About the Reader This book assumes you're comfortable reading code in Python or a similar language, such as C, Ruby, or JavaScript. No prior experience with data science is required. About the Authors Davy Cielen, Arno D. B. Meysman, and Mohamed Ali are the founders and managing partners of Optimately and Maiton, where they focus on developing data science projects and solutions in various sectors. Table of Contents Data science in a big data world The data science process Machine learning Handling large data on a single computer First steps in big data Join the NoSQL movement The rise of graph databases Text mining and text analytics Data visualization to the end user |
| big data case study: Computational and Statistical Methods for Analysing Big Data with Applications Shen Liu, James Mcgree, Zongyuan Ge, Yang Xie, 2015-11-20 Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data. - Advanced computational and statistical methodologies for analysing big data are developed - Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable - Case studies are discussed to demonstrate the implementation of the developed methods - Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation - Computing code/programs are provided where appropriate |
| big data case study: Managerial Perspectives on Intelligent Big Data Analytics Sun, Zhaohao, 2019-02-22 Big data, analytics, and artificial intelligence are revolutionizing work, management, and lifestyles and are becoming disruptive technologies for healthcare, e-commerce, and web services. However, many fundamental, technological, and managerial issues for developing and applying intelligent big data analytics in these fields have yet to be addressed. Managerial Perspectives on Intelligent Big Data Analytics is a collection of innovative research that discusses the integration and application of artificial intelligence, business intelligence, digital transformation, and intelligent big data analytics from a perspective of computing, service, and management. While highlighting topics including e-commerce, machine learning, and fuzzy logic, this book is ideally designed for students, government officials, data scientists, managers, consultants, analysts, IT specialists, academicians, researchers, and industry professionals in fields that include big data, artificial intelligence, computing, and commerce. |
| big data case study: Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics Pradeep N, Sandeep Kautish, Sheng-Lung Peng, 2021-06-10 Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians. - Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies - Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables, and graphs with algorithms and computational methods for developing new applications in healthcare informatics - Unique case study approach provides readers with insights for practical clinical implementation |
| big data case study: Machine Learning and Big Data Uma N. Dulhare, Khaleel Ahmad, Khairol Amali Bin Ahmad, 2020-09-01 This book is intended for academic and industrial developers, exploring and developing applications in the area of big data and machine learning, including those that are solving technology requirements, evaluation of methodology advances and algorithm demonstrations. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the academic and professional community. The 17 chapters are divided into 5 sections: Theoretical Fundamentals; Big Data and Pattern Recognition; Machine Learning: Algorithms & Applications; Machine Learning's Next Frontier and Hands-On and Case Study. While it dwells on the foundations of machine learning and big data as a part of analytics, it also focuses on contemporary topics for research and development. In this regard, the book covers machine learning algorithms and their modern applications in developing automated systems. Subjects covered in detail include: Mathematical foundations of machine learning with various examples. An empirical study of supervised learning algorithms like Naïve Bayes, KNN and semi-supervised learning algorithms viz. S3VM, Graph-Based, Multiview. Precise study on unsupervised learning algorithms like GMM, K-mean clustering, Dritchlet process mixture model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth. Hands-on machine leaning open source tools viz. Apache Mahout, H2O. Case studies for readers to analyze the prescribed cases and present their solutions or interpretations with intrusion detection in MANETS using machine learning. Showcase on novel user-cases: Implications of Electronic Governance as well as Pragmatic Study of BD/ML technologies for agriculture, healthcare, social media, industry, banking, insurance and so on. |
| big data case study: Big Data , 2011 |
| big data case study: Case Studies in Applied Bayesian Data Science Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert, 2020-05-28 Presenting a range of substantive applied problems within Bayesian Statistics along with their Bayesian solutions, this book arises from a research program at CIRM in France in the second semester of 2018, which supported Kerrie Mengersen as a visiting Jean-Morlet Chair and Pierre Pudlo as the local Research Professor. The field of Bayesian statistics has exploded over the past thirty years and is now an established field of research in mathematical statistics and computer science, a key component of data science, and an underpinning methodology in many domains of science, business and social science. Moreover, while remaining naturally entwined, the three arms of Bayesian statistics, namely modelling, computation and inference, have grown into independent research fields. While the research arms of Bayesian statistics continue to grow in many directions, they are harnessed when attention turns to solving substantive applied problems. Each such problem set has its own challenges and hence draws from the suite of research a bespoke solution. The book will be useful for both theoretical and applied statisticians, as well as practitioners, to inspect these solutions in the context of the problems, in order to draw further understanding, awareness and inspiration. |
| big data case study: Big Data Meets Survey Science Craig A. Hill, Paul P. Biemer, Trent D. Buskirk, Lilli Japec, Antje Kirchner, Stas Kolenikov, Lars E. Lyberg, 2020-09-29 Offers a clear view of the utility and place for survey data within the broader Big Data ecosystem This book presents a collection of snapshots from two sides of the Big Data perspective. It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations. It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data. Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more sources of data, with numerous chapters providing consistent illustrations and examples of survey data enriching the evaluation of Big Data sources. Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented. Topics covered include: Total Error Frameworks for Found Data; Performance and Sensitivities of Home Detection on Mobile Phone Data; Assessing Community Wellbeing Using Google Street View and Satellite Imagery; Using Surveys to Build and Assess RBS Religious Flag; and more. Presents groundbreaking survey methods being utilized today in the field of Big Data Explores how machine learning methods can be applied to the design, collection, and analysis of social science data Filled with examples and illustrations that show how survey data benefits Big Data evaluation Covers methods and applications used in combining Big Data with survey statistics Examines regulations as well as ethical and privacy issues Big Data Meets Survey Science: A Collection of Innovative Methods is an excellent book for both the survey and social science communities as they learn to capitalize on this new revolution. It will also appeal to the broader data and computer science communities looking for new areas of application for emerging methods and data sources. |
| big data case study: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| big data case study: R and Data Mining Yanchang Zhao, 2012-12-31 R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. - Presents an introduction into using R for data mining applications, covering most popular data mining techniques - Provides code examples and data so that readers can easily learn the techniques - Features case studies in real-world applications to help readers apply the techniques in their work |
| big data case study: Big Data and Health Analytics Katherine Marconi, Harold Lehmann, 2014-12-20 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. |
| big data case study: Privacy and Security Policies in Big Data Tamane, Sharvari, Solanki, Vijender Kumar, Dey, Nilanjan, 2017-03-03 In recent years, technological advances have led to significant developments within a variety of business applications. In particular, data-driven research provides ample opportunity for enterprise growth, if utilized efficiently. Privacy and Security Policies in Big Data is a pivotal reference source for the latest research on innovative concepts on the management of security and privacy analytics within big data. Featuring extensive coverage on relevant areas such as kinetic knowledge, cognitive analytics, and parallel computing, this publication is an ideal resource for professionals, researchers, academicians, advanced-level students, and technology developers in the field of big data. |
| big data case study: Big Data Analytics in Chemoinformatics and Bioinformatics Subhash C. Basak, Marjan Vračko, 2022-12-06 Big Data Analytics in Chemoinformatics and Bioinformatics: With Applications to Computer-Aided Drug Design, Cancer Biology, Emerging Pathogens and Computational Toxicology provides an up-to-date presentation of big data analytics methods and their applications in diverse fields. The proper management of big data for decision-making in scientific and social issues is of paramount importance. This book gives researchers the tools they need to solve big data problems in these fields. It begins with a section on general topics that all readers will find useful and continues with specific sections covering a range of interdisciplinary applications. Here, an international team of leading experts review their respective fields and present their latest research findings, with case studies used throughout to analyze and present key information. - Brings together the current knowledge on the most important aspects of big data, including analysis using deep learning and fuzzy logic, transparency and data protection, disparate data analytics, and scalability of the big data domain - Covers many applications of big data analysis in diverse fields such as chemistry, chemoinformatics, bioinformatics, computer-assisted drug/vaccine design, characterization of emerging pathogens, and environmental protection - Highlights the considerable benefits offered by big data analytics to science, in biomedical fields and in industry |
| big data case study: New Horizons for a Data-Driven Economy José María Cavanillas, Edward Curry, Wolfgang Wahlster, 2016-04-04 In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment. |
| big data case study: Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data Paul Zikopoulos, Chris Eaton, 2011-10-22 Big Data represents a new era in data exploration and utilization, and IBM is uniquely positioned to help clients navigate this transformation. This book reveals how IBM is leveraging open source Big Data technology, infused with IBM technologies, to deliver a robust, secure, highly available, enterprise-class Big Data platform. The three defining characteristics of Big Data--volume, variety, and velocity--are discussed. You'll get a primer on Hadoop and how IBM is hardening it for the enterprise, and learn when to leverage IBM InfoSphere BigInsights (Big Data at rest) and IBM InfoSphere Streams (Big Data in motion) technologies. Industry use cases are also included in this practical guide. Learn how IBM hardens Hadoop for enterprise-class scalability and reliability Gain insight into IBM's unique in-motion and at-rest Big Data analytics platform Learn tips and tricks for Big Data use cases and solutions Get a quick Hadoop primer |
| big data case study: Big Data Concepts, Theories, and Applications Shui Yu, Song Guo, 2016-03-03 This book covers three major parts of Big Data: concepts, theories and applications. Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice. It also focuses on high level concepts such as definitions of Big Data from different angles; surveys in research and applications; and existing tools, mechanisms, and systems in practice. Each chapter is independent from the other chapters, allowing users to read any chapter directly. After examining the practical side of Big Data, this book presents theoretical perspectives. The theoretical research ranges from Big Data representation, modeling and topology to distribution and dimension reducing. Chapters also investigate the many disciplines that involve Big Data, such as statistics, data mining, machine learning, networking, algorithms, security and differential geometry. The last section of this book introduces Big Data applications from different communities, such as business, engineering and science. Big Data Concepts, Theories and Applications is designed as a reference for researchers and advanced level students in computer science, electrical engineering and mathematics. Practitioners who focus on information systems, big data, data mining, business analysis and other related fields will also find this material valuable. |
| big data case study: Big Data Support of Urban Planning and Management Zhenjiang Shen, Miaoyi Li, 2017-09-26 In the era of big data, this book explores the new challenges of urban-rural planning and management from a practical perspective based on a multidisciplinary project. Researchers as contributors to this book have accomplished their projects by using big data and relevant data mining technologies for investigating the possibilities of big data, such as that obtained through cell phones, social network systems and smart cards instead of conventional survey data for urban planning support. This book showcases active researchers who share their experiences and ideas on human mobility, accessibility and recognition of places, connectivity of transportation and urban structure in order to provide effective analytic and forecasting tools for smart city planning and design solutions in China. |
| big data case study: Big Data Analytics Methods Peter Ghavami, 2019-12-16 Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics. |
| big data case study: Thinking Big Data in Geography Jim Thatcher, Andrew Shears, Josef Eckert, 2018-04-01 Intro -- Title Page -- Copyright Page -- Contents -- List of Illustrations -- List of Tables -- Introduction -- Part 1 -- 1. Toward Critical Data Studies -- 2. Big Data ... Why (Oh Why?) This Computational Social Science? -- Part 2 -- 3. Smaller and Slower Data in an Era of Big Data -- 4. Reflexivity, Positionality, and Rigor in the Context of Big Data Research -- Part 3 -- 5. A Hybrid Approach to Geotweets -- 6. Geosocial Footprints and Geoprivacy Concerns -- 7. Foursquare in the City of Fountains -- Part 4 -- 8. Big City, Big Data -- 9. Framing Digital Exclusion in Technologically Mediated Urban Spaces -- Part 5 -- 10. Bringing the Big Data of Climate Change Down to Human Scale -- 11. Synergizing Geoweb and Digital Humanitarian Research -- Part 6 -- 12. Rethinking the Geoweb and Big Data -- Bibliography -- List of Contributors -- Index -- About Jim Thatcher -- About Josef Eckert -- About Andrew Shears |
| big data case study: Big Data Demystified David Stephenson, 2018-02-19 The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed. 'Big Data' refers to a new class of data, to which 'big' doesn't quite do it justice. Much like an ocean is more than simply a deeper swimming pool, big data is fundamentally different to traditional data and needs a whole new approach. Packed with examples and case studies, this clear, comprehensive book will show you how to accumulate and utilise 'big data' in order to develop your business strategy. Big Data Demystified is your practical guide to help you draw deeper insights from the vast information at your fingertips; you will be able to understand customer motivations, speed up production lines, and even offer personalised experiences to each and every customer. With 20 years of industry experience, David Stephenson shows how big data can give you the best competitive edge, and why it is integral to the future of your business. |
| big data case study: Mobile Big Data Xiang Cheng, Luoyang Fang, Liuqing Yang, Shuguang Cui, 2018-08-23 This book provides a comprehensive picture of mobile big data starting from data sources to mobile data driven applications. Mobile Big Data comprises two main components: an overview of mobile big data, and the case studies based on real-world data recently collected by one of the largest mobile network carriers in China. In the first component, four areas of mobile big data life cycle are surveyed: data source and collection, transmission, computing platform and applications. In the second component, two case studies are provided, based on the signaling data collected in the cellular core network in terms of subscriber privacy evaluation and demand forecasting for network management. These cases respectively give a vivid demonstration of what mobile big data looks like, and how it can be analyzed and mined to generate useful and meaningful information and knowledge. This book targets researchers, practitioners and professors relevant to this field. Advanced-level students studying computer science and electrical engineering will also be interested in this book as supplemental reading. |
| big data case study: Big Data and Social Science Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane, 2016-08-10 Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website. |
| big data case study: Public Administration and Information Technology Christopher Reddick, Reddick, 2011-08-16 Public Administration and Information Technology provides a foundational overview of the impact of information technology (IT) on modern public organizations. The focus is on what public managers need to know about managing IT to create more efficient, effective, and transparent organizations. This book is unique in that it provides a concise introduction to the subject area and leaves students with a broad perspective on the most important issues. Other books in the field either examine e-government, or are large reference volumes that are not easily accessible to most students. This textbook shows the practical application of IT to the most important areas of public administration. Public Administration and Information Technology is ideal for use in traditional public administration courses on IT as well as management information systems courses in schools of business. Divided into 3 parts, the book covers: - Public Organizations and Information Technology I- nformation Technology, Evaluation, and Resource Management - Emerging Issues in for Public Managers |
| big data case study: Multi-agent Systems Jacques Ferber, 1999 In this book, Jacques Ferber has brought together all the recent developments in the field of multi-agent systems - an area that has seen increasing interest and major developments over the last few years. The author draws on work carried out in various disciplines, including information technology, sociology and cognitive psychology to provide a coherent and instructive picture of the current state-of-the-art. The book introduces and defines the fundamental concepts that need to be understood, clearly describes the work that has been done, and invites readers to reflect upon the possibilities of the future. |
| big data case study: Case Studies in Neural Data Analysis Mark A. Kramer, Uri T. Eden, 2016-11-04 A practical guide to neural data analysis techniques that presents sample datasets and hands-on methods for analyzing the data. As neural data becomes increasingly complex, neuroscientists now require skills in computer programming, statistics, and data analysis. This book teaches practical neural data analysis techniques by presenting example datasets and developing techniques and tools for analyzing them. Each chapter begins with a specific example of neural data, which motivates mathematical and statistical analysis methods that are then applied to the data. This practical, hands-on approach is unique among data analysis textbooks and guides, and equips the reader with the tools necessary for real-world neural data analysis. The book begins with an introduction to MATLAB, the most common programming platform in neuroscience, which is used in the book. (Readers familiar with MATLAB can skip this chapter and might decide to focus on data type or method type.) The book goes on to cover neural field data and spike train data, spectral analysis, generalized linear models, coherence, and cross-frequency coupling. Each chapter offers a stand-alone case study that can be used separately as part of a targeted investigation. The book includes some mathematical discussion but does not focus on mathematical or statistical theory, emphasizing the practical instead. References are included for readers who want to explore the theoretical more deeply. The data and accompanying MATLAB code are freely available on the authors' website. The book can be used for upper-level undergraduate or graduate courses or as a professional reference. A version of this textbook with all of the examples in Python is available on the MIT Press website. |
| big data case study: Big Data MBA Bill Schmarzo, 2015-12-11 Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. Understand where and how to leverage big data Integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to “think like a data scientist” Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions. |
| big data case study: Real Estate Analysis in the Information Age Kimberly Winson-Geideman, Andy Krause, Clifford A. Lipscomb, Nick Evangelopoulos, 2017-11-09 The creation, accumulation, and use of copious amounts of data are driving rapid change across a wide variety of industries and academic disciplines. This ‘Big Data’ phenomenon is the result of recent developments in computational technology and improved data gathering techniques that have led to substantial innovation in the collection, storage, management, and analysis of data. Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling focuses on the real estate discipline, guiding researchers and practitioners alike on the use of data-centric methods and analysis from applied and theoretical perspectives. In it, the authors detail the integration of Big Data into conventional real estate research and analysis. The book is process-oriented, not only describing Big Data and associated methods, but also showing the reader how to use these methods through case studies supported by supplemental online material. The running theme is the construction of efficient, transparent, and reproducible research through the systematic organization and application of data, both traditional and 'big'. The final chapters investigate legal issues, particularly related to those data that are publicly available, and conclude by speculating on the future of Big Data in real estate. |
| big data case study: Operations Research and Big Data Ana Paula Ferreira Dias Barbosa Póvoa, Joao Luis de Miranda, 2015-09-11 The development of Operations Research (OR) requires constant improvements, such as the integration of research results with business applications and innovative educational practice. The full deployment and commercial exploitation of goods and services generally need the construction of strong synergies between educational institutions and businesses. The IO2015 -XVII Congress of APDIO aims at strengthening the knowledge triangle in education, research and innovation, in order to maximize the contribution of OR for sustainable growth, the promoting of a knowledge-based economy, and the smart use of finite resources. The IO2015-XVII Congress of APDIO is a privileged meeting point for the promotion and dissemination of OR and related disciplines, through the exchange of ideas among teachers, researchers, students , and professionals with different background, but all sharing a common desire that is the development of OR. |
| big data case study: Big Data, Data Mining, and Machine Learning Jared Dean, 2014-05-07 With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole. |
| big data case study: Big Data and Analytics Vincenzo Morabito, 2015-01-31 This book presents and discusses the main strategic and organizational challenges posed by Big Data and analytics in a manner relevant to both practitioners and scholars. The first part of the book analyzes strategic issues relating to the growing relevance of Big Data and analytics for competitive advantage, which is also attributable to empowerment of activities such as consumer profiling, market segmentation, and development of new products or services. Detailed consideration is also given to the strategic impact of Big Data and analytics on innovation in domains such as government and education and to Big Data-driven business models. The second part of the book addresses the impact of Big Data and analytics on management and organizations, focusing on challenges for governance, evaluation, and change management, while the concluding part reviews real examples of Big Data and analytics innovation at the global level. The text is supported by informative illustrations and case studies, so that practitioners can use the book as a toolbox to improve understanding and exploit business opportunities related to Big Data and analytics. |
| big data case study: Configuring the Networked Self Julie E. Cohen, 2012-01-24 The legal and technical rules governing flows of information are out of balance, argues Julie E. Cohen in this original analysis of information law and policy. Flows of cultural and technical information are overly restricted, while flows of personal information often are not restricted at all. The author investigates the institutional forces shaping the emerging information society and the contradictions between those forces and the ways that people use information and information technologies in their everyday lives. She then proposes legal principles to ensure that people have ample room for cultural and material participation as well as greater control over the boundary conditions that govern flows of information to, from, and about them. |
| big data case study: Data-Driven Design and Construction Randy Deutsch, 2015-08-27 “In this comprehensive book, Professor Randy Deutsch has unlocked and laid bare the twenty-first century codice nascosto of architecture. It is data. Big data. Data as driver. . .This book offers us the chance to become informed and knowledgeable pursuers of data and the opportunities it offers to making architecture a wonderful, useful, and smart art form.” —From the Foreword by James Timberlake, FAIA Written for architects, engineers, contractors, owners, and educators, and based on today’s technology and practices, Data-Driven Design and Construction: 25 Strategies for Capturing, Applying and Analyzing Building Data addresses how innovative individuals and firms are using data to remain competitive while advancing their practices. seeks to address and rectify a gap in our learning, by explaining to architects, engineers, contractors and owners—and students of these fields—how to acquire and use data to make more informed decisions. documents how data-driven design is the new frontier of the convergence between BIM and architectural computational analyses and associated tools. is a book of adaptable strategies you and your organization can apply today to make the most of the data you have at your fingertips. Data-Driven Design and Construction was written to help design practitioners and their project teams make better use of BIM, and leverage data throughout the building lifecycle. |
| big data case study: The Elements of Big Data Value Edward Curry, Andreas Metzger, Sonja Zillner, Jean-Christophe Pazzaglia, Ana García Robles, 2021-08-01 This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation. |
| big data case study: Big Data Analytics with Java Rajat Mehta, 2017-07-31 Learn the basics of analytics on big data using Java, machine learning and other big data tools About This Book Acquire real-world set of tools for building enterprise level data science applications Surpasses the barrier of other languages in data science and learn create useful object-oriented codes Extensive use of Java compliant big data tools like apache spark, Hadoop, etc. Who This Book Is For This book is for Java developers who are looking to perform data analysis in production environment. Those who wish to implement data analysis in their Big data applications will find this book helpful. What You Will Learn Start from simple analytic tasks on big data Get into more complex tasks with predictive analytics on big data using machine learning Learn real time analytic tasks Understand the concepts with examples and case studies Prepare and refine data for analysis Create charts in order to understand the data See various real-world datasets In Detail This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naive Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world. Style and approach The approach of book is to deliver practical learning modules in manageable content. Each chapter is a self-contained unit of a concept in big data analytics. Book will step by step builds the competency in the area of big data analytics. Examples using real world case studies to give ideas of real applications and how to use the techniques mentioned. The examples and case studies will be shown using both theory and code. |
| big data case study: Big Data Analytics Soraya Sedkaoui, Mounia Khelfaoui, Nadjat Kadi, 2021-07-04 This volume explores the diverse applications of advanced tools and technologies of the emerging field of big data and their evidential value in business. It examines the role of analytics tools and methods of using big data in strengthening businesses to meet today’s information challenges and shows how businesses can adapt big data for effective businesses practices. This volume shows how big data and the use of data analytics is being effectively adopted more frequently, especially in companies that are looking for new methods to develop smarter capabilities and tackle challenges in dynamic processes. Many illustrative case studies are presented that highlight how companies in every sector are now focusing on harnessing data to create a new way of doing business. |
| big data case study: Handbook of Research on Engineering Innovations and Technology Management in Organizations Gaur, Loveleen, Solanki, Arun, Jain, Vishal, Khazanchi, Deepak, 2020-04-17 As technology weaves itself more tightly into everyday life, socio-economic development has become intricately tied to these ever-evolving innovations. Technology management is now an integral element of sound business practices, and this revolution has opened up many opportunities for global communication. However, such swift change warrants greater research that can foresee and possibly prevent future complications within and between organizations. The Handbook of Research on Engineering Innovations and Technology Management in Organizations is a collection of innovative research that explores global concerns in the applications of technology to business and the explosive growth that resulted. Highlighting a wide range of topics such as cyber security, legal practice, and artificial intelligence, this book is ideally designed for engineers, manufacturers, technology managers, technology developers, IT specialists, productivity consultants, executives, lawyers, programmers, managers, policymakers, academicians, researchers, and students. |
case study collection 7 get big data - Bernard Marr
big data - case study collection 1 Big Data is a big thing and this case study collection will give you a good overview of how some companies really leverage big data to drive business …
Top Big Data Analytics Use Cases - Oracle
To get started on your big data journey, check out our top twenty-two big data use cases. Each use case ofers a real-world example of how companies are taking advantage of data insights …
CASE STUDY: Business Technology and Big Data The Digital
wide variety of related big data technologies (an enterprise data lake) that integrates structured and unstructured data in near-real time across Amgen’s global operations to process it for …
Uber & Big Data a case study - GitHub Pages
On-the-ground crews that manage and scale Uber’s transportation network in each market. Access data on a regular basis to respond to driver-and-rider-specific issues. ... Generation 3 …
Value Oriented Big Data Strategy: Analysis & Case Study
We in this research are motivated to investigate the value side of big data. We examine the financial statements in CAC40 companies in order to discover the relationship between stock …
Big-Data-Analytics-Retail Case Study - WNS Global Services
WNS deployed its proprietary, big data analytics platform BrandttitudeTM to transform this massive volume of data into valuable insights, accessible on a real-time basis. HERE’S HOW …
Big Data Analytics And Operations Decisions: A Case Study Of …
Big Data Analytics is emerging as technology that re-defines how companies compete through development of capabilities and new business models. In the era of Big Data decisions can be …
A Detailed study of Big Data in Healthcare: Case study of …
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Overcoming resistance to change in a big data analytics …
The objectives of this case study are: 1. To understand the concept of managing resistance to change in the context of implementing big data analytics in the banking sector; 2. To …
Impact of Big Data Analytics on Banking: A Case Study
Guided by the theory of Technological Frames of Reference (TFR) and Transaction Cost Theory (TCT), this paper describes a real-world case study in the banking industry to explain how to …
Big Data in Practice: HOW 45 SUCCESSFUL COMPANIES USED …
Bernard Marr’s book delivers valuable, and diverse insights on Big Data use cases, success stories, and learned from numerous business domains. After diving into this book, have all the …
A Case Study Of Clustering And Classification Methods For Big …
The key objective of this case study is to examine the analysis process done in past through the use of many different clustering and classification methods. this research article is to describe …
A CASE STUDY ON BIG DATA ANALYTICS IN MOBILE …
In this paper we review the various methods of analyzing data generated by mobile cellular networks. We aim to introduce the general background of data generated by mobile cellular …
A CASE STUDY ON FINANCIAL FRAUD DETECTION WITH BIG …
With the help of big data, companiesproviding financial services havechanged their operating methods. Bigdata reduces the risk of fraud detection, enforcement, and portfolio management.
CASE STUDY Big Data in Sales & Marketing - cStor
With an infinitely scalable Big Data deployment based on an Enterprise Data Hub architecture, the global online marketing firm can now centralize all data collection and processing onto a single …
Enterprise Big Data: Case Study of Issues and Challenges for …
that enterprise big data projects deliver their true value and provide sustainable benefits?” It is important to look at the dimensions of the big data concept and how this affects businesses. …
Big Data Case Study: Spark on Armv8 - Arm Developer
In this paper, we examine the readiness of Arm®-based platforms for two of the more prominent big data technologies namely Spark and Hadoop.
The Impact of Big Data on E-commerce: A Case Study of …
This paper thoroughly explores the utilization of the six pivotal stages of big data— generation, acquisition, storage, analysis, visualization, and decision-making—in the context of Amazon's …
Case Studies on Big Data - ela.kpi.ua
Case Studies on Big Data 43 Common data mining techniques are: artificial neural networks, decision trees, genetic algorithms, nearest neighbor method, and rule induction (Fig. 1).
Privacy Issues and Data Protection in Big Data: A Case Study …
In this paper, we discuss the current state of the legal regulations and analyse different data protection and privacy-preserving techniques in the context of big data analysis. In addition, we …
Enterprise Big Data: Case Study of Issues and Challenges for …
required not only for big data but also for all processes. For limited processes, big data should be used as it streams into the organization in order to gain maximum value it. Figure 1: …
Big Data for Smart Cities: A Case Study of NEOM City, Saudi …
presented the big data analytics for IoT and smart cities. Their contributions to this research are as follows. Firstly, they presented the relationship between Big data and IoT technologies, …
Impacts of big data on accounting
Jan 20, 2022 · The motivation for this study is that the use of big data and data analytics in accounting is in its infancy stage and many accountants are unaware of how big data can ...
Construction of Ecological Environment Information System …
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Airbnb Case Study - celerdata.com
CASE STUDY 9 In terms of data update, the data update cycle of traditional data models is excessively long. Data lake products such as Apache Iceberg, Hudi, and Delta Lake also …
LEADING THROUGH INNOVATION: THE DATA …
Big Data has real potential to help organizations better learn from the past, more accurately predict the future and solidify treasury’s strategic role within a company. Volume, velocity and …
Big Data in Customer Acquisition and Retention for …
Big Data in Customer Acquisition and Retention for ... WALMART CASE STUDY Walmart is widely thought to be the largest retailer company in the world. It was founded back in 1945 and …
Functional Data Analysis for Big Data: A case study on …
Functional Data Analysis for Big Data: A case study on California temperature trends 3 of time evolving patterns (eg. [74, 89]) or to exploit structural notions such as con-vexity (eg. [59]) or to …
Big Data Fundamentals - pearsoncmg.com
Big Data Fundamentals Concepts, Drivers & Techniques Thomas Erl, Wajid Khattak, and Paul Buhler BOSTON • COLUMBUS • INDIANAPOLIS • NEW YORK • SAN FRANCISCO
A Case Study for Blockchain in Healthcare - ONC
A Case Study for Blockchain in Healthcare: “MedRec” prototype for electronic health records and medical research data White Paper Ariel Ekblaw*, Asaph †Azaria*, John D. Halamka, MD , …
Value Oriented Big Data Strategy: Analysis & Case Study
Value Oriented Big Data Strategy: Analysis & Case Study Khaled Himmi*, Jonathan Arcondara*, Peiqing Guan†, Wei Zhou*‡ *Department of Information and Operations Management, ESCP …
A Framework for Big Data Governance to Advance RHINs: A …
Advance RHINs: A Case Study of China QUAN LI 1, LAN LAN2, NIANYIN ZENG 3, LEI YOU4, JIN YIN2, XIAOBO ZHOU2,4, AND QUN MENG1,5 ... Big data has been drawing increasing …
case study collection 7 get big data - imgcrown.com
big data - case study collection 1 Big Data is a big thing and this case study collection will give you a good overview of how some companies really leverage big data to drive business …
Mitigating biases in big mobility data: a case study of …
processing technique—data standardization, is effective to mitigate the effects of data biases. Using transit data from Google and Apple data as a case study, we show that the mitigated …
CASE STUDY: Business Technology and Big Data The Digital …
transactional data, systems and processes across sites, and there are also differences in the kinds of analysis that each group needs to do with this data. This is a common big data …
Highlights: The Kaiser Permanente
In a 2019 study, the Center for Disease Control estimated that 6 in 10 adults have a chronic disease, and those with a ... Quality Assurance (NCQA) Quality Compass data set and has …
‘Data over intuition’ – How big data analytics revolutionises …
Subject terms: Big data; Big data analytics; Strategic decision-making; Strategy-as-practice, Data-driven decision-making Abstract Background: Digital technologies are increasingly …
Information data management in big data. Case study: E …
Information data management in big data. Case study: E-Albania government portal. 1. Valma Prifti. 1. Department of Production and Management, Faculty of Mechanical Engineering, …
Credit scoring - Case study in data analytics - Deloitte United …
This is particularly true in recent times with the explosion of big data (big implying data volume, velocity and variety). Some of these ingredients are the listed below: ... Credit scoring - Case …
Case Study: Hadoop - Springer
Case Study: Hadoop Hadoop is one of the most popular distributed big data platforms in the world. Besides computing power, its storage subsystem capability is ... From data …
SQL-Like Big Data Environments: Case study in Clinical …
on July 21, 2004, [2] selected Study Data Tabulation Model (SDTM) as the standard requirement for submitting the tabulation data to FDA for clinical trials. The model defines ... discovery and …
Amazon in the Air: Innovating with Big Data at Lufthansa - TUM
To fill this void, we conducted a two-stage study: (1) to demystify big data adoption, and (2) to study and present an exemplary case of successful big data adoption. The first-stage research …
Big Data Quality Case Study Preliminary Findings - DTIC
to look at quality issues that have arisen within the context of this particular Big Data case study. Section 7 then explores how those Big Data quality issues identified in Section 6 are managed, …
BIG DATA IN LOGISTICS - DHL
Big Data analytics falls into one of three dimensions (see Figure 4). The first and most obvious is operational efficiency. In this case, data is used to make better decisions, to optimize resource …
Big Data: A case study of disruption and government power
Technologies: A Survey on Big Data’ (2014) 275 Information Sciences 314. 3Paul C Zikopoulos et al., Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data …
How Nestlé is using real-time visibility to manage supply …
Case Study Introduction – Challenges & Goals Historically, Nestlé’s transport operations ... Hub was the first big step. The ready-to-use cloud-based solution and quick onboard- ... identified …
Computing the User Experience via Big Data Analysis: A …
2824 CMC, 2021, vol.67, no.3 Table1: Outline of the measurements Elements LIWC category Scale Pragmatic Work, leisure, and home 0–1.0 Expectation confirmation Comparisons 0–1.0
Data Analytics for Effective Project Management in the Oil …
traditional data processing applications. It encompasses the three V’s of big data (vari-ety, volume, velocity) [10]. These include volume, which is the quantity of data to be analyzed. The …
How ‘big data’ can make big impact: Findings from a
prior ‘big data’ studies as well as on an in-depth case study of an Australian state emergency service using ‘Big data’ to improve the delivery of emergency services to achieve the ...
Big Data Case Studies - International Center for Journalists
Cadwalladr recruited Maria Ressa - the subject of the other big data case study presented as part of this study - to sit on the Real Facebook Oversight Board (Halpern, 2020). And while their …
An urban big data-based air quality index prediction: A case …
A case study of routes planning for outdoor activities in Beijing Zhiqiang Zou ... Urban big data include various types of datasets, such as air quality data, meteorological data, and
On using MapReduce to scale algorithms for Big Data …
Kijsanayothin et al. J Big Data Page 6 of 20 self-joiningoperation,ofrelationaldatabase,to k-1,i.e.,L k− L1 ⊗ L k−1 toobtaineligible k-itemsetsbypruningawayk ...
Case Study - Sisense
Data warehouse lacked a variety of data mining functions Case Study . foodpanda wanted centrally available data, to encourage data transparency and democratization, ... to meet the …
Big data analytics in the financial services industry: Trends ...
Furthermore, Big Data Analytics facilitates the development of personalized financial products and services, tailored to meet the unique needs and preferences of individual customers (George, …
Leveraging Big Data for Lending in China
Leveraging big Data and cloud computing has helped Ant financial offer inclusive financial services. The use of Big Data could potentially reshape SME lending in China, since concrete …
BigData Case Study - Talentica.com
Increasing revenue realization by leveraging Big Data CASE STUDY Mobile marketing platform
Research on the Core Competitiveness of Short Video Industry …
the enterprise itself, but also be used to study the situation of competitors. Through data analysis and comparison, it can find out the strategic advantages suitable for the development of the …
Data Analysis Case Studies - Data Action Lab
not solely about Big Data and disruption! P.Boily, 2017 Page 4 of13. DATA SCIENCE REPORT SERIES Data Analysis Case Studies Table 1. Confusion matrices for audit evaluation [1]. Top: …
Big Data Surveillance: The Case of Policing
quest for ‘big data’ approaches are becoming increasingly central.” rISe of BIg DATA Big data is an emerging modality of surveil-lance. A wide range of organizations—from finance to …
Bond University Research Repository Big data: A case study …
BIG DATA: A CASE STUDY OF DISRUPTION AND GOVERNMENT POWER. I. I. NTRODUCTION. History is replete with examples of government data collection to support …
Harvard Case Study Solution & Analysis - HBR Case Study …
Creating Consumer Apps that Leverage Big Data The Weather Company: Creating Consumer Apps that Leverage its Big Data Thecasesolutions.com Problem Statement - Over emphasis …
Investigating the Impact of Big Data Analytics on Supply …
Impact of Big Data and Big Data Analytics on Supply Chain Operations: Systematic Literature Review to Proposed Conceptual Framework”. Production Planning & Control (ABS 3*), …
Actualizing Big Data Analytics Affordances: A Revelatory Case …
Drawing on a revelatory case study, we discuss four big data analytics (BDA) actualization mechanisms: (1) enhancing, (2) constructing, (3) coordinating, and (4) integrating, which …
A Study on the Effectiveness of Logistics Services in Real …
Dr. E. Dhanasekar et.al. A study on the effectiveness of logistics services in real-time data visibility International Journal of Research and Review (ijrrjournal.com) 260 Volume 11; Issue: …
CASE STUDY Big Data in Sales & Marketing - cStor
With an infinitely scalable Big Data deployment based on an Enterprise Data Hub architecture, the global online marketing firm can now centralize all data collection and processing onto a …
Case-study - Nestle-2 .indd - Transporeon
first big step. The ready-to-use cloud- based solution and quick onboarding ... data-driven decisions • Holistic overview of all transport ac-tivity to optimize transport operations across …
Barriers to the Adoption of Big Data Analytics in the
Adoption, big data analytics, case study, barriers, automotive. Introduction Digitization strongly affects the car manufacturing market and leads to disruptive product innovations
Can Big Data provide good quality statistics? A case study on …
5 We refer to a corpus as a set of multiple similar documents.In our case, the totality of Tweets represents the corpus while each Tweet is a document. In R, we use different packages to …
Case Studies on Big Data - ela.kpi.ua
1.1. The sources of Big Data Based on the book [4]: “The most outstanding Big Data sources are such modern tech-nologies like GIS, parallel clusters and grids, semantic and social networks …
CASE STUDy 1: Cambridge Analytica and the 2016 U.S
Mar 1, 2019 · their data and how. In other words, in the case of big data it is resource consumption, not resource origination as in the case of oil, that yields political might. Whilst …
