“…Provides foundational understanding of new and emerging technologies—social data, collaboration, big data, advanced analytics Includes case studies and practical examples of success and failures Will prepare you to lead projects and advance initiatives that will benefit you and your organization…”

“…Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines is a must-read for all scientists about a very simple computation method designed to simulate big-data neural processing. This book is inspired by the Calculus Ratiocinator idea of Gottfried Leibniz, which is that machine computation should be developed to simulate human cognitive processes, thus avoiding problematic subjective bias in analytic solutions to practical and scientific problems. …”

Tabla de Contenidos: “…Abad -- 5.1 Introduction 133 -- 5.2 Vision: Using Cloud Technology in Missions 134 -- 5.3 State of the Art 136 -- 5.4 Data Replication in a Cloud File System 137 -- 5.4.1 MapReduce Clusters 138 -- 5.4.1.1 File Popularity, Temporal Locality, and Arrival Patterns 142 -- 5.4.1.2 Synthetic Workloads for Big Data 144 -- 5.4.2 Related Work 147 -- 5.4.3 Contribution from Our Approach to Generating Big Data Request Streams Using Clustered Renewal Processes 149 -- 5.4.3.1 Scalable Geo-Distributed Storage 149 -- 5.4.4 Related Work 151 -- 5.4.5 Summary of Ambry 152 -- 5.5 Summary 153 -- 5.6 The Future 153 -- 6 Resource Management: Performance Assuredness in Distributed Cloud Computing via Online Reconfigurations 160 /Mainak Ghosh, Le Xu, and Indranil Gupta -- 6.1 Introduction 161 -- 6.2 Vision: Using Cloud Technology in Missions 163 -- 6.3 State of the Art 164 -- 6.3.1 State of the Art: Reconfigurations in Sharded Databases/Storage 164 -- 6.3.1.1 Database Reconfigurations 164 -- 6.3.1.2 Live Migration 164 -- 6.3.1.3 Network Flow Scheduling 164 -- 6.3.2 State of the Art: Scale-Out/Scale-In in Distributed Stream Processing Systems 165 -- 6.3.2.1 Real-Time Reconfigurations 165 -- 6.3.2.2 Live Migration 165 -- 6.3.2.3 Real-Time Elasticity 165 -- 6.3.3 State of the Art: Scale-Out/Scale-In in Distributed Graph Processing Systems 166 -- 6.3.3.1 Data Centers 166 -- 6.3.3.2 Cloud and Storage Systems 166 -- 6.3.3.3 Data Processing Frameworks 166.…”

Tabla de Contenidos: “…The concept of proactive behavior-based malware detection -- The concept of malware's behavioral characteristic -- Operational aspects of software behavior data collection -- Operational aspects of behavior modeling using machine learning or AI -- Operational aspects of behavior monitoring -- Operational aspects of malware behavior-based response -- Operational aspects of anomaly detection -- Operational aspects of specification-based techniques -- Normalcy and anomaly detection -- Concept of normalcy -- Concept of anomaly detection -- Operational aspects of anomaly detection -- Future concepts of normalcy and anomaly detection -- Overcoming the increased complexity of evolving cyber threats -- Handling increased complexity and data volume -- Navigating privacy regulations -- Mitigating evolving cyber threats -- Implementing the solutions -- Starting with the basics - organizational capability maturity -- The relationship between the CMMI maturity process and the increased complexity of threat management -- Operational challenges and mitigation strategies to enhance organizational cybersecurity capabilities -- Summary -- Part 3 - The Future State of AI's Use for Malware Science -- Chapter 6: The Future State of Malware Data Analysis and Detection -- The future state of advanced ML and AI integration in malware detection -- Beyond signature-based detection -- The future state of automated malware analysis -- Why manual processes are no longer viable -- The dawn of automated malware analysis -- The future state of cloud-based TI -- The current landscape of TIPs -- The advent of cloud-driven TI -- The future state of integration of big data analytics in cybersecurity -- Understanding the magnitude of modern data -- The imperative for big data analytics -- Integration of AI - the game-changer…”

Tabla de Contenidos: “…Real-World Information -- 2.1 Real-world data in trials -- 2.2 Real-world data and real-world evidence -- 2.2 Real-world limitations -- 3.0 Making RWD Work -- 3.1 Digital twins -- 4.0 Conclusions -- References -- ADVANCED METHODS FOR BIG DATA ANALYTICS IN WOMEN'S HEALTH -- Session Introduction: Advanced Methods for Big Data Analytics in Women's Health -- 1. …”

Tabla de Contenidos: “…9.4 Motivating Example (continued) 206 -- 9.5 Related Work 207 -- 9.6 Future Research Directions 214 -- 9.7 Conclusions 216 -- References 217 -- 10 Using Machine Learning for Protecting the Security and Privacy of Internet of Things (IoT) Systems 223 / Melody Moh and Robinson Raju -- 10.1 Introduction 223 -- 10.2 Background 234 -- 10.3 Survey of ML Techniques for Defending IoT Devices 242 -- 10.4 Machine Learning in Fog Computing 248 -- 10.4.1 Introduction 248 -- 10.5 Future Research Directions 252 -- 10.6 Conclusions 252 -- References 253 -- Part III Applications and Issues 259 -- 11 Fog Computing Realization for Big Data Analytics 261 / Farhad Mehdipour, Bahman Javadi, AniketMahanti, and Guillermo Ramirez-Prado -- 11.1 Introduction 261 -- 11.2 Big Data Analytics 262 -- 11.3 Data Analytics in the Fog 267 -- 11.4 Prototypes and Evaluation 272 -- 11.4.1 Architecture 272 -- 11.4.2 Configurations 274 -- 11.5 Case Studies 277 -- 11.6 Related Work 282 -- 11.7 Future Research Directions 287 -- 11.8 Conclusions 287 -- References 288 -- 12 Exploiting Fog Computing in Health Monitoring 291 / Tuan Nguyen Gia and Mingzhe Jiang -- 12.1 Introduction 291 -- 12.2 An Architecture of a Health Monitoring IoT-Based System with Fog Computing 293 -- 12.3 Fog Computing Services in Smart E-Health Gateways 297 -- 12.4 System Implementation 304 -- 12.5 Case Studies, Experimental Results, and Evaluation 308 -- 12.6 Discussion of Connected Components 313 -- 12.7 Related Applications in Fog Computing 313 -- 12.8 Future Research Directions 314 -- 12.9 Conclusions 314 -- References 315 -- 13 Smart Surveillance Video Stream Processing at the Edge for Real-Time Human Objects Tracking 319 / Seyed Yahya Nikouei, Ronghua Xu, and Yu Chen -- 13.1 Introduction 319 -- 13.2 Human Object Detection 320 -- 13.3 Object Tracking 327 -- 13.4 Lightweight Human Detection 335 -- 13.5 Case Study 337 -- 13.6 Future Research Directions 342 -- 13.7 Conclusions 343 -- References 343 -- 14 Fog Computing Model for Evolving Smart Transportation Applications 347 / M. …”

Tabla de Contenidos: “…. -- See also -- 8. Providing a Big Data Integration Layer with Apache Cassandra -- Introduction -- Installing Cassandra client bundles in Apache Karaf -- Getting ready -- How to do it... -- Modeling data with Apache Cassandra -- Getting ready -- How to do it... -- How it works...…”

“…This book reports the latest progress in cognitive big data and sustainable computing. Various real-time case studies and implemented works are discussed for better understanding and more clarity to readers. …”

Tabla de Contenidos: “…-- Las V del big data -- Algoritmos distribuidos: HDFS y MapReduce -- La promesa del big data -- 3.5.Inteligencia artificial y machine learning -- Pero ¿qué es inteligencia artificial? …”

Tabla de Contenidos: “…Strengthening the Foundation -- 6.1 Two-Speed IT: Accelerating the Pace for the Digital Age -- Three Routes to a Two-Speed IT -- The Agile IT Often Accelerates All Elements of the Organization -- 6.2 Big Data and Advanced Analytics -- The Three Success Factors of Big Data and Advanced Analytics -- The Foundations -- Artificial Intelligence: The Next Disruptor -- 6.3 Cyber Security: The Art of the Secure Digital Economy -- Seven Steps toward Cyber Resilience…”

Tabla de Contenidos: “…5.9.4 Study Conclusions and EC Policy Recommendations -- Bibliography -- Chapter 6 - Securing the Internet of Things - Security and Privacy in a HyperconnectedWorld -- 6.1 Introduction -- 6.2 End-to-End Security and Privacy by Design -- 6.3 Physical IoT Security -- 6.3.1 Selected Low-Cost Attacks -- 6.3.2 Key Extraction Attacks and Countermeasures -- 6.4 On Device Security and Privacy -- 6.4.1 Mediated Device Access for Security and Privacy -- 6.4.2 Encryption -- 6.4.3 Integrity -- 6.4.4 Data Minimisation -- 6.5 Unobservable Communication -- 6.5.1 Resisting Network Traffic Analysis -- 6.6 Access Control Based on Policy Management -- 6.7 Security and Privacy in the IoT Cloud -- 6.7.1 Verifiable and Authenticity Preserving Data Processing -- 6.7.2 Structural Integrity and Certification of Virtualized Infrastructure -- 6.7.3 Privacy Preserving Service Usage and Data Handling -- 6.7.4 Confidentiality of (Un-)structured Data -- 6.7.5 Long Term Security and Everlasting Privacy -- 6.7.6 Conclusion -- 6.8 Outlook -- Acknowledgment -- Bibliography -- Chapter 7 - IoT Analytics: Collect, Process, Analyze, and Present Massive Amounts of Operational Data - Research and Innovation Challenges -- 7.1 Introduction -- 7.2 Deep Internet of Things Data Analytics -- 7.2.1 Introduction -- 7.2.2 Designing for Real World Problems -- 7.2.3 Real World Data -- 7.2.4 Data Interoperability -- 7.2.5 Deep Data Analytics Methods -- 7.2.6 Conclusions -- 7.3 Cloud-Based IoT Big Data Platform -- 7.3.1 Introduction -- 7.3.2 Big Data in the Context of IoT -- 7.3.3 Applications of IoT Big Data Analytics -- 7.3.4 Requirements of IoT Big Data Analytic Platform -- 7.3.4.1 Intelligent and dynamic -- 7.3.4.2 Distributed -- 7.3.4.3 Scalable and elastic -- 7.3.4.4 Real-time -- 7.3.4.5 Heterogeneous (unified) -- 7.3.4.6 Security and privacy -- 7.3.5 Cloud-Based IoT Analytic Platform…”

Tabla de Contenidos: “…Tecnologías base para las Fintech -- 3.4. Big data -- 3.5. IA -- 3.6. Blockchain -- 3.7. Resumen -- 4. -- Verticales en Fintech -- 4.1. …”

Tabla de Contenidos: “…The Reuse of Digital Computer Data: Transformation, Recombination and Generation of Data Mixes in Big Data Science (N Tempini) -- Chapter 14. Data, Meta Data and Pattern Data: How Franz Boas Mobilized Anthropometric Data, 1890 and Beyond (S Müller-Wille) -- Chapter 15. …”

Tabla de Contenidos: “…Setting up PyPy -- Running a particle simulator in PyPy -- Other interesting projects -- Summary -- Chapter 6: Implementing Concurrency -- Asynchronous programming -- Waiting for I/O -- Concurrency -- Callbacks -- Futures -- Event loops -- The asyncio framework -- Coroutines -- Converting blocking code into non-blocking code -- Reactive programming -- Observables -- Useful operators -- Hot and cold observables -- Building a CPU monitor -- Summary -- Chapter 7: Parallel Processing -- Introduction to parallel programming -- Graphic processing units -- Using multiple processes -- The Process and Pool classes -- The Executor interface -- Monte Carlo approximation of pi -- Synchronization and locks -- Parallel Cython with OpenMP -- Automatic parallelism -- Getting started with Theano -- Profiling Theano -- Tensorflow -- Running code on a GPU -- Summary -- Chapter 8: Distributed Processing -- Introduction to distributed computing -- An introduction to MapReduce -- Dask -- Directed Acyclic Graphs -- Dask arrays -- Dask Bag and DataFrame -- Dask distributed -- Manual cluster setup -- Using PySpark -- Setting up Spark and PySpark -- Spark architecture -- Resilient Distributed Datasets -- Spark DataFrame -- Scientific computing with mpi4py -- Summary -- Chapter 9: Designing for High Performance -- Choosing a suitable strategy -- Generic applications -- Numerical code -- Big data -- Organizing your source code -- Isolation, virtual environments, and containers -- Using conda environments -- Virtualization and Containers -- Creating docker images -- Continuous integration -- Summary -- Index…”

Tabla de Contenidos: “…Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: The Julia Environment; Introduction; Philosophy; Role in data science and big data; Comparison with other languages; Features; Getting started; Julia sources; Building from source; Installing on CentOS; Mac OS X and Windows; Exploring the source stack; Juno; IJulia; A quick look at some Julia; Julia via the console; Installing some packages; A bit of graphics creating more realistic graphics with Winston; My benchmarks; Package management; Listing, adding, and removing…”

Tabla de Contenidos: “…; Communicating Upward: Harnessing Top Management Support; CASE STUDY - Dashboards Empower Middle Management: Discover Financial Services; Wrap-Up and Manager's Memo; Chapter 4: Assemble your Team; Cross-Functional Development Teams; CASE STUDY - Not All Fun and EB Games; Team Management is Ongoing; Sustained Assistance from Top Management; Wrap-Up and Manager's Memo; Chapter 5: Gain IT Support on Big and Not-So-Big Data…”

Tabla de Contenidos: “…Developing a Training PlanTraining the Masses; Part VI: The Part of Tens; Chapter 15: Ten Simple Management Guidelines; Limiting E-Mail Attachments; Knowing How to Dispose of Information; Structuring Electronic Folders; Naming Files; Managing Active and Inactive Periods; Scheduling Destruction and Deletion; Creating the Proper Filing System; Avoiding the "Keep Everything" Syndrome; Managing Copies; Keeping It Simple; Chapter 16: Ten Emerging Trends; Governing Information; Computing in the Clouds; Big Data; Social Media; Putting Some Structure to It; Let's Collaborate…”

Tabla de Contenidos: “…""Reading files having one field per row""""Reading files with some fields occupying two or more rows""; ""Writing a simple file""; ""Writing a semi-structured file""; ""Providing the name of a file (for reading or writing) dynamically""; ""Using the name of a file (or part of it) as a field""; ""Reading an Excel file""; ""Getting the value of specific cells in an Excel file""; ""Writing an Excel file with several sheets""; ""Writing an Excel file with a dynamic number of sheets""; ""Reading data from an AWS S3 Instance""; ""Chapter 3: Working with Big Data and Cloud Sources""…”

Tabla de Contenidos: “…Belén Fernández Collados -- Tecnología y discriminación / Antonio Fernández García -- Inteligencia artificial y trabajo por cuenta ajena: algunas consideraciones a la altura de 2023 / Román Gil Alburquerque -- La recepción de las tecnologías digitales en el diálogo social y la negociación colectiva de la Unión Europea / Francisco Xabiere Gómez García -- Efectos adversos para las personas trabajadoras de la irrupción de la cuarta revolución industrial / Sara Guindo Morales -- Los derechos digitales : garantías y relación con la negociación colectiva / Rafael José López Bedmar -- Datos biométricos, big data y datos personales en el ámbito laboral / Belén del Mar López Insua -- El reto del teletrabajo ante los avances tecnológicos / Sheila López Vico -- La prevención de riesgos laborales en la cuarta revolución industrial / Carlos José Martínez Mateo -- Hacia un empleador automatizado / Helena Monzón Pérez, César Carlos Navarro Contreras -- Derecho de huelga y esquirolaje tecnológico / Alejandro Muros Polo -- Desafío tecnológico e inteligencia artificial en las relaciones laborales / Pompeyo Gabriel Ortega Lozano -- La incidencia de las nuevas tecnologías en las relaciones colectivas de Trabajo / José Antonio Rueda Monroy -- Tecnología y empleo de personas con discapacidad / José Luis Ruiz Santamaría, Rafael Borrego Peña -- La limitación de la jornada en la cuarta revolución industrial : viabilidad de la jornada laboral de cuatro días semanales / Diego Velasco Fernández…”

Tabla de Contenidos: “…-- Conclusiones -- Bibliografía -- Elecciones provinciales 2019: el caso de Cambia Mendoza, por Andrés Lombardi -- Cuando el Clio le ganó al big data: notas sobre un triunfo inesperado, por Ignacio Ramírez -- La sorpresa -- La hegemonía duranbarbista -- La campaña de la campaña -- "Axel en persona" -- ¿El Clio fue un in. …”