Big Data Integration Theory

This book presents a novel approach to database concepts, describing a categorical logic for database schema mapping based on views, within a framework for database integration/exchange and peer-to-peer.

Big Data Integration Theory

Author: Zoran Majkić

Publisher: Springer Science & Business Media

ISBN: 3319041568

Page: 516

View: 627

This book presents a novel approach to database concepts, describing a categorical logic for database schema mapping based on views, within a framework for database integration/exchange and peer-to-peer. Database mappings, database programming languages, and denotational and operational semantics are discussed in depth. An analysis method is also developed that combines techniques from second order logic, data modeling, co-algebras and functorial categorial semantics. Features: provides an introduction to logics, co-algebras, databases, schema mappings and category theory; describes the core concepts of big data integration theory, with examples; examines the properties of the DB category; defines the categorial RDB machine; presents full operational semantics for database mappings; discusses matching and merging operators for databases, universal algebra considerations and algebraic lattices of the databases; explores the relationship of the database weak monoidal topos w.r.t. intuitionistic logic.

Big Data Databases and Ownership Rights in the Cloud

Lohr S (2015) Data-ism: the revolution transforming decision making, consumer
behavior, and almost everything else. ... Springer, London Majkic Z (2014) Big
data integration theory: theory and methods of database mappings, programming
 ...

Big Data  Databases and  Ownership  Rights in the Cloud

Author: Marcelo Corrales Compagnucci

Publisher: Springer Nature

ISBN: 9811503494

Page: 308

View: 275

Two of the most important developments of this new century are the emergence of cloud computing and big data. However, the uncertainties surrounding the failure of cloud service providers to clearly assert ownership rights over data and databases during cloud computing transactions and big data services have been perceived as imposing legal risks and transaction costs. This lack of clear ownership rights is also seen as slowing down the capacity of the Internet market to thrive. Click-through agreements drafted on a take-it-or-leave-it basis govern the current state of the art, and they do not allow much room for negotiation. The novel contribution of this book proffers a new contractual model advocating the extension of the negotiation capabilities of cloud customers, thus enabling an automated and machine-readable framework, orchestrated by a cloud broker. Cloud computing and big data are constantly evolving and transforming into new paradigms where cloud brokers are predicted to play a vital role as innovation intermediaries adding extra value to the entire life cycle. This evolution will alleviate the legal uncertainties in society by means of embedding legal requirements in the user interface and related computer systems or its code. This book situates the theories of law and economics and behavioral law and economics in the context of cloud computing and takes database rights and ownership rights of data as prime examples to represent the problem of collecting, outsourcing, and sharing data and databases on a global scale. It does this by highlighting the legal constraints concerning ownership rights of data and databases and proposes finding a solution outside the boundaries and limitations of the law. By allowing cloud brokers to establish themselves in the market as entities coordinating and actively engaging in the negotiation of service-level agreements (SLAs), individual customers as well as small and medium-sized enterprises could efficiently and effortlessly choose a cloud provider that best suits their needs. This approach, which the author calls “plan-like architectures,” endeavors to create a more trustworthy cloud computing environment and to yield radical new results for the development of the cloud computing and big data markets.

Big Data

The book includes Use Cases and Tutorials to provide an integrated approach that answers the ‘What’, ‘How’, and ‘Why’ of Big Data.

Big Data

Author: Nasir Raheem

Publisher: CRC Press

ISBN: 0429592450

Page: 176

View: 507

Big Data: A Tutorial-Based Approach explores the tools and techniques used to bring about the marriage of structured and unstructured data. It focuses on Hadoop Distributed Storage and MapReduce Processing by implementing (i) Tools and Techniques of Hadoop Eco System, (ii) Hadoop Distributed File System Infrastructure, and (iii) efficient MapReduce processing. The book includes Use Cases and Tutorials to provide an integrated approach that answers the ‘What’, ‘How’, and ‘Why’ of Big Data. Features Identifies the primary drivers of Big Data Walks readers through the theory, methods and technology of Big Data Explains how to handle the 4 V’s of Big Data in order to extract value for better business decision making Shows how and why data connectors are critical and necessary for Agile text analytics Includes in-depth tutorials to perform necessary set-ups, installation, configuration and execution of important tasks Explains the command line as well as GUI interface to a powerful data exchange tool between Hadoop and legacy r-dbms databases

Exam Prep for Big Data Recommender Systems

This book provides over 2,000 Exam Prep questions and answers to accompany the text Big Data Recommender Systems Items include highly probable exam items: IDEF1X, negative, encryption, bid, Data integration, stock, Data independence, ...

Exam Prep for  Big Data Recommender Systems

Author:

Publisher:

ISBN:

Page:

View: 554

Big Data Analytics for Intelligent Healthcare Management

The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques that analyze huge amounts of real-time healthcare data.

Big Data Analytics for Intelligent Healthcare Management

Author: Nilanjan Dey

Publisher: Academic Press

ISBN: 0128181478

Page: 312

View: 816

Big Data Analytics for Intelligent Healthcare Management covers both the theory and application of hardware platforms and architectures, the development of software methods, techniques and tools, applications and governance, and adoption strategies for the use of big data in healthcare and clinical research. The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques that analyze huge amounts of real-time healthcare data. Examines the methodology and requirements for development of big data architecture, big data modeling, big data as a service, big data analytics, and more Discusses big data applications for intelligent healthcare management, such as revenue management and pricing, predictive analytics/forecasting, big data integration for medical data, algorithms and techniques, etc. Covers the development of big data tools, such as data, web and text mining, data mining, optimization, machine learning, cloud in big data with Hadoop, big data in IoT, and more

Principles of Database Management

This combined learning approach connects key concepts throughout the text to the important, practical tools to get started in database management.

Principles of Database Management

Author: Wilfried Lemahieu

Publisher: Cambridge University Press

ISBN: 1316946754

Page:

View: 215

This comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more. Written by experienced educators and experts in big data, analytics, data quality, and data integration, it provides an up-to-date approach to database management. This full-color, illustrated text has a balanced theory-practice focus, covering essential topics, from established database technologies to recent trends, like Big Data, NoSQL, and more. Fundamental concepts are supported by real-world examples, query and code walkthroughs, and figures, making it perfect for introductory courses for advanced undergraduates and graduate students in information systems or computer science. These examples are further supported by an online playground with multiple learning environments, including MySQL; MongoDB; Neo4j Cypher; and tree structure visualization. This combined learning approach connects key concepts throughout the text to the important, practical tools to get started in database management.

Loyalty 3 0 How to Revolutionize Customer and Employee Engagement with Big Data and Gamification

... 232 OIT (Organismic integration theory), 86 Omidyar, Pam, 131—132
Onboarding: in gamification, 78—79 for Loyalty ... 17 Performance reviews, using
big data in, 59—60 Perkscom, 87 Personalization: employee big data for, 61 of
goals, ...

Loyalty 3 0  How to Revolutionize Customer and Employee Engagement with Big Data and Gamification

Author: Rajat Paharia

Publisher: McGraw Hill Professional

ISBN: 0071813373

Page: 281

View: 214

A guide to sustaining loyalty among customers, employees, and business partners using motivation, big data, and gamification.

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and ...

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Author: Thomas, J. Joshua

Publisher: IGI Global

ISBN: 1799811948

Page: 355

View: 447

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Computer and Information Technology

Water resources big data has the characteristics of diversity and extremely
complex data types, and needs for dynamic extraction and integration, according
to the water resources information organization theory, so we apply SQL and
NoSQL ...

Computer and Information Technology

Author: Prasad Yarlagadda

Publisher: Trans Tech Publications Ltd

ISBN: 3038264008

Page: 1772

View: 258

Collection of selected, peer reviewed papers from the International Forum on Computer and Information Technology (IFCIT 2013), December 24-25, 2013, Shenzhen, China. Volume is indexed by Thomson Reuters CPCI-S (WoS). The 335 papers are grouped as follows: Chapter 1: Databases, Data Processing and Data Management, Chapter 2: Parallel and Distributed Computing, Chapter 3: Computer Network Technology and Applications, Chapter 4: Software Engineering, Chapter 5: E-Commerce and E-Government, Chapter 6: Multimedia Technology and Application, Chapter 7: Computer Vision and Image Processing Technology, Chapter 8: Artificial Intelligence, Intelligent Algorithms and Computational Mathematics, Chapter 9: Computer Aided Design and Research, Chapter 10: Communications Technology and Signal Processing, Chapter 11: Electronic Devices and Embedded Systems, Chapter 12: Intelligent Instruments, Techniques for Detection and Testing, Sensors and Measurement, Chapter 13: Automation and Control, Chapter 14: Information Technologies in Engineering Management, Chapter 15: Enterprise Resource Planning and Management System, Chapter 16: Information Technologies in Education

Big Data Cloud Computing Data Science Engineering

This book presents the outcomes of the 3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD 2018), which was held on July 10–12, 2018 in Kanazawa.

Big Data  Cloud Computing  Data Science   Engineering

Author: Roger Lee

Publisher: Springer

ISBN: 3319968033

Page: 189

View: 923

This book presents the outcomes of the 3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD 2018), which was held on July 10–12, 2018 in Kanazawa. The aim of the conference was to bring together researchers and scientists, businesspeople and entrepreneurs, teachers, engineers, computer users, and students to discuss the various fields of computer science, to share their experiences, and to exchange new ideas and information in a meaningful way. All aspects (theory, applications and tools) of computer and information science, the practical challenges encountered along the way, and the solutions adopted to solve them are all explored here. The conference organizers selected the best papers from among those accepted for presentation. The papers were chosen on the basis of review scores submitted by members of the program committee and subsequently underwent further rigorous review. Following this second round of review, 13 of the conference’s most promising papers were selected for this Springer (SCI) book. We eagerly await the important contributions that we know these authors will make to the field of computer and information science.

Encyclopedia of Business Analytics and Optimization

Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal.

Encyclopedia of Business Analytics and Optimization

Author: Wang, John

Publisher: IGI Global

ISBN: 1466652039

Page: 2754

View: 562

As the age of Big Data emerges, it becomes necessary to take the five dimensions of Big Data- volume, variety, velocity, volatility, and veracity- and focus these dimensions towards one critical emphasis - value. The Encyclopedia of Business Analytics and Optimization confronts the challenges of information retrieval in the age of Big Data by exploring recent advances in the areas of knowledge management, data visualization, interdisciplinary communication, and others. Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal.

Entity Information Life Cycle for Big Data

This book explains big data’s impact on MDM and the critical role of entity information management system (EIMS) in successful MDM.

Entity Information Life Cycle for Big Data

Author: John R. Talburt

Publisher: Morgan Kaufmann

ISBN: 012800665X

Page: 254

View: 219

Entity Information Life Cycle for Big Data walks you through the ins and outs of managing entity information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data’s impact on MDM and the critical role of entity information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the entity information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on entity and identity management, data management, customer relationship management (CRM), and related topics. Explains the business value and impact of entity information management system (EIMS) and directly addresses the problem of EIMS design and operation, a critical issue organizations face when implementing MDM systems Offers practical guidance to help you design and build an EIM system that will successfully handle big data Details how to measure and evaluate entity integrity in MDM systems and explains the principles and processes that comprise EIM Provides an understanding of features and functions an EIM system should have that will assist in evaluating commercial EIM systems Includes chapter review questions, exercises, tips, and free downloads of demonstrations that use the OYSTER open source EIM system Executable code (Java .jar files), control scripts, and synthetic input data illustrate various aspects of CSRUD life cycle such as identity capture, identity update, and assertions

Deep Learning Convergence to Big Data Analytics

This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data.

Deep Learning  Convergence to Big Data Analytics

Author: Murad Khan

Publisher: Springer

ISBN: 9811334595

Page: 79

View: 407

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

Intelligent Computing Theories and Methodologies

This two-volume set LNCS 9225 and LNCS 9226 constitutes - in conjunction with the volume LNAI 9227 - the refereed proceedings of the 11th International Conference on Intelligent Computing, ICIC 2015, held in Fuzhou, China, in August 2015.

Intelligent Computing Theories and Methodologies

Author: De-Shuang Huang

Publisher: Springer

ISBN: 3319221868

Page: 755

View: 214

This two-volume set LNCS 9225 and LNCS 9226 constitutes - in conjunction with the volume LNAI 9227 - the refereed proceedings of the 11th International Conference on Intelligent Computing, ICIC 2015, held in Fuzhou, China, in August 2015. The total of 191 full and 42 short papers presented in the three ICIC 2015 volumes was carefully reviewed and selected from 671 submissions. The papers are organized in topical sections such as evolutionary computation and learning; compressed sensing, sparse coding and social computing; neural networks, nature inspired computing and optimization; pattern recognition and signal processing; image processing; biomedical informatics theory and methods; differential evolution, particle swarm optimization and niche technology; intelligent computing and knowledge discovery and data mining; soft computing and machine learning; computational biology, protein structure and function prediction; genetic algorithms; artificial bee colony algorithms; swarm intelligence and optimization; social computing; information security; virtual reality and human-computer interaction; healthcare informatics theory and methods; unsupervised learning; collective intelligence; intelligent computing in robotics; intelligent computing in communication networks; intelligent control and automation; intelligent data analysis and prediction; gene expression array analysis; gene regulation modeling and analysis; protein-protein interaction prediction; biology inspired computing and optimization; analysis and visualization of large biological data sets; motif detection; biomarker discovery; modeling; simulation; and optimization of biological systems; biomedical data modeling and mining; intelligent computing in biomedical signal/image analysis; intelligent computing in brain imaging; neuroinformatics; cheminformatics; intelligent computing in computational biology; computational genomics; special session on biomedical data integration and mining in the era of big data; special session on big data analytics; special session on artificial intelligence for ambient assisted living; and special session on swarm intelligence with discrete dynamics.

Research Advances in the Integration of Big Data and Smart Computing

The volume, complexity, and irregularity of computational data in modern algorithms and simulations necessitates an unorthodox approach to computing.

Research Advances in the Integration of Big Data and Smart Computing

Author: Mallick, Pradeep Kumar

Publisher: IGI Global

ISBN: 146668738X

Page: 374

View: 650

The volume, complexity, and irregularity of computational data in modern algorithms and simulations necessitates an unorthodox approach to computing. Understanding the facets and possibilities of soft computing algorithms is necessary for the accurate and timely processing of complex data. Research Advances in the Integration of Big Data and Smart Computing builds on the available literature in the realm of Big Data while providing further research opportunities in this dynamic field. This publication provides the resources necessary for technology developers, scientists, and policymakers to adopt and implement new paradigms in computational methods across the globe. The chapters in this publication advance the body of knowledge on soft computing techniques through topics such as transmission control protocol for mobile ad hoc networks, feature extraction, comparative analysis of filtering techniques, big data in economic policy, and advanced dimensionality reduction methods.

Learning Science Theory Research and Practice

And the collection of short-interval time series of high quality data about CPS
processes can only be achieved with further advancement in learning analytics
research (Hofman et al., 2018). Thirdly, the integration of heterogeneous big data
is a ...

Learning Science  Theory  Research  and Practice

Author: Feldman

Publisher: McGraw Hill Professional

ISBN: 1260458008

Page: 384

View: 580

Cutting-edge insights and perspectives from today’s leading minds in the field of learning science The discipline of learning science is fast becoming a primary approach for answering one of the most important questions of our time: How do we most effectively educate students to reach their full potential? Spanning the disciplines of psychology, data science, cognitive science, sociology, and anthropology, Learning Science offers solutions to our most urgent educational challenges. Composed of insightful essays from top figures in their respective fields, the book also shows how a thorough understanding of this critical discipline all but ensures better decision making when it comes to education. Chapters include: • Exploring Student Interactions in Collaborative Problem-Solving with a Multimodal Approach • Learning Science Research Through a Social Science Lens • Semantic Representation & Analysis and its Application in Conversation-based Intelligent Tutoring Systems • Advancing the Relationship Between Learning Sciences and Teaching Practice • Advancing the State of Online Learning: Stay Integrated, Stay Accessible, Stay Curious • Designing Immersive Authentic Simulations that Enhance Motivation and Learning • High School OER STEM Lessons Leading to Deep Learning, For Students and Teachers • How to Increase Learning While Not Decreasing the Fun in Educational Games Whether you’re creating curricula, developing policies, or educating students in a classroom setting, Learning Science delivers the knowledge, insight, and inspiration you need to do your part to ensure every student meets his or her full potential.

Guide to Mobile Data Analytics in Refugee Scenarios

This book summarizes the most important findings of the Data for Refugees (D4R) Challenge, which was a non-profit project initiated to improve the conditions of the Syrian refugees in Turkey by providing a database for the scientific ...

Guide to Mobile Data Analytics in Refugee Scenarios

Author: Albert Ali Salah

Publisher: Springer Nature

ISBN: 3030125548

Page: 500

View: 262

After the start of the Syrian Civil War in 2011–12, increasing numbers of civilians sought refuge in neighboring countries. By May 2017, Turkey had received over 3 million refugees — the largest refugee population in the world. Some lived in government-run camps near the Syrian border, but many have moved to cities looking for work and better living conditions. They faced problems of integration, income, welfare, employment, health, education, language, social tension, and discrimination. In order to develop sound policies to solve these interlinked problems, a good understanding of refugee dynamics isnecessary. This book summarizes the most important findings of the Data for Refugees (D4R) Challenge, which was a non-profit project initiated to improve the conditions of the Syrian refugees in Turkey by providing a database for the scientific community to enable research on urgent problems concerning refugees. The database, based on anonymized mobile call detail records (CDRs) of phone calls and SMS messages of one million Turk Telekom customers, indicates the broad activity and mobility patterns of refugees and citizens in Turkey for the year 1 January to 31 December 2017. Over 100 teams from around the globe applied to take part in the challenge, and 61 teams were granted access to the data. This book describes the challenge, and presents selected and revised project reports on the five major themes: unemployment, health, education, social integration, and safety, respectively. These are complemented by additional invited chapters describing related projects from international governmental organizations, technological infrastructure, as well as ethical aspects. The last chapter includes policy recommendations, based on the lessons learned. The book will serve as a guideline for creating innovative data-centered collaborations between industry, academia, government, and non-profit humanitarian agencies to deal with complex problems in refugee scenarios. It illustrates the possibilities of big data analytics in coping with refugee crises and humanitarian responses, by showcasing innovative approaches drawing on multiple data sources, information visualization, pattern analysis, and statistical analysis.It will also provide researchers and students working with mobility data with an excellent coverage across data science, economics, sociology, urban computing, education, migration studies, and more.

Data Science and Big Data Analytics

This book presents conjectural advances in big data analysis, machine learning and computational intelligence, as well as their potential applications in scientific computing.

Data Science and Big Data Analytics

Author: Durgesh Kumar Mishra

Publisher: Springer

ISBN: 9811076413

Page: 406

View: 665

This book presents conjectural advances in big data analysis, machine learning and computational intelligence, as well as their potential applications in scientific computing. It discusses major issues pertaining to big data analysis using computational intelligence techniques, and the conjectural elements are supported by simulation and modelling applications to help address real-world problems. An extensive bibliography is provided at the end of each chapter. Further, the main content is supplemented by a wealth of figures, graphs, and tables, offering a valuable guide for researchers in the field of big data analytics and computational intelligence.

Understanding Religion Through Artificial Intelligence

In this way, the same algorithms from big data can be applied to smaller social
groups to answer questions by looking deeper into a relatively smaller dataset.
One of the more interesting promises of cultural cybernetics is the possibility for
vertical research integration. Typically ... Researchers will use a preexisting
theory, gather data, code and clean the data, analyze the data, and then report
results.

Understanding Religion Through Artificial Intelligence

Author: Justin E. Lane

Publisher: Bloomsbury Publishing

ISBN: 1350103578

Page: 256

View: 223

In Understanding Religion through Artificial Intelligence, Justin E. Lane looks at the reasons why humans feel they are part of a religious group, despite often being removed from other group members by vast distances or multiple generations. To achieve this, Lane offers a new perspective that integrates religious studies with psychology, anthropology, and data science, as well as with research at the forefront of Artificial Intelligence (AI). After providing a critical analysis of approaches to religion and social cohesion, Lane proposes a new model for religious studies, which he calls the “Information Identity System.” This model focuses on the idea of conceptual ties: links between an individual's self-concept and the ancient beliefs of their religious group. Lane explores this idea through real-world examples, ranging from the rise in global Pentecostalism, to religious extremism and self-radicalization, to the effect of 9/11 on sermons. Lane uses this lens to show how we can understand religion and culture today, and how we can better contextualize the changes we see in the social world around us.

Analytics and Knowledge Management

Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds.

Analytics and Knowledge Management

Author: Suliman Hawamdeh

Publisher: CRC Press

ISBN: 1351806998

Page: 446

View: 745

The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms.