Tabla de Contenidos: “…7.4 The MPEG-1 System Target Decoder 143 -- 7.5 The MPEG-1 System Stream 155 -- 7.5.1 Data Structure and Design Considerations 155 -- 7.5.2 Constrained System Parameter Streams 161 -- 7.5.3 Compliancy Requirements of MPEG-1 System Streams 166 -- 7.6 MPEG-1 Applications 168 -- 7.6.1 Compact Disc 168 -- 7.6.2 Computers 169 -- 7.7 Conclusions on MPEG-1 169 -- References 170 -- Part Two THE MPEG-2 SYSTEMS STANDARD 171 -- 8 The Development of MPEG-2 Systems 173 -- 8.1 Driving Forces 173 -- 8.2 Objectives and Requirements 176 -- 8.3 The Evolution of MPEG-2 Systems 178 -- References 185 -- 9 Layering in MPEG-2 Systems 187 -- 9.1 Need for Program Streams and Transport Streams 187 -- 9.2 PES Packets as a Common Layer 188 -- 9.3 Program Streams 189 -- 9.4 Transport Streams 193 -- 9.4.1 Transport Packets 193 -- 9.4.2 Conveying PES Packets in Transport Packets 195 -- 9.4.3 The Size of Transport Packets 196 -- 9.4.4 Multiple Programs, PSI, Descriptors and Sections 199 -- 9.4.5 Conveying Sections in Transport Packets 213 -- References 214 -- 10 Conditional Access and Scrambling 217 -- 10.1 Support of Conditional Access Systems 217 -- 10.2 Scrambling in Transport Streams 219 -- 10.3 Improving the Interoperability between CA Systems 224 -- 10.4 Scrambling in Program Streams 225 -- Reference 226 -- 11 Other Features of MPEG-2 Systems 227 -- 11.1 Error Resiliency 227 -- 11.2 Re-Multiplexing of Transport Streams 230 -- 11.3 Local Program Insertion in Transport Streams 234 -- 11.3.1 Usage of Local Program Insertions 234 -- 11.3.2 Associated PSI Issues 235 -- 11.3.3 Time Base Discontinuities 236 -- 11.4 Splicing in Transport Streams 239 -- 11.5 Variable Bitrate and Statistical Multiplexing 245 -- 11.6 Padding and Stuffing 245 -- 11.7 Random Access and Parsing Convenience 248 -- 11.8 Carriage of Private Data 250 -- 11.9 Copyright and Copy Control Support 254 -- 11.10 Playback Trick Modes 255 -- 11.11 Single Program and Partial Transport Streams 255 -- 11.12 Program Stream Carriage within a Transport Stream 258.…”

Tabla de Contenidos: “…The basics -- The java.util.function package -- Streams -- Comparators and collectors -- Issues with streams, Lambdas, and method references -- The optional type -- File I/O -- The java.time package -- Parallelism and concurrency -- Java 9 additions -- Generics and Java 8.…”

“…"In this easy-to-use streaming video, top Fortune® 100 network consultant David L. …”

“…Empower yourself with the knowledge and skills to conquer the world of real-time data using Apache Kafka, the industry's leading streaming platform. This course is more than just technical training, it's your starting line to learning the potential of streaming data and its use cases. …”

Tabla de Contenidos: “…Introductions -- 1 - Cloud and Big Data -- 2 - Spring Boot -- 3 - Spring Integration -- 4 - Spring Batch -- 5 - Spring Cloud -- 6 - Spring Cloud Stream -- 7 - Spring Cloud Stream Binders -- 8 – Spring Cloud Data Flow Introduction & Installation -- Part II. …”

Tabla de Contenidos: “…chapter 1 Introduction -- part PART I Supporting Technologies for BDMA and BDSP -- chapter Introduction to Part I -- chapter 2 Data Security and Privacy -- chapter 3 Data Mining Techniques -- chapter 4 Data Mining for Security Applications -- chapter 5 Cloud Computing and Semantic Web Technologies -- chapter 6 Data Mining and Insider Threat Detection -- chapter 7 Big Data Management and Analytics Technologies -- chapter Conclusion to Part I -- part PART II Stream Data Analytics -- chapter Introduction to Part II -- chapter 8 Challenges for Stream Data Classification -- chapter 9 Survey of Stream Data Classification -- chapter 10 A Multi-Partition, Multi-Chunk Ensemble for Classifying Concept-Drifting Data Streams -- chapter 11 Classification and Novel Class Detection in Concept-Drifting Data Streams -- chapter 12 Data Stream Classification with Limited Labeled Training Data -- chapter 13 Directions in Data Stream Classification -- chapter Conclusion to Part II -- part PART III Stream Data Analytics for Insider Threat Detection -- chapter Introduction to Part III -- chapter 14 Insider Threat Detection as a Stream Mining Problem -- chapter 15 Survey of Insider Threat and Stream Mining -- chapter 16 Ensemble-Based Insider Threat Detection -- chapter 17 Details of Learning Classes -- chapter 18 Experiments and Results for Nonsequence Data -- chapter 19 Insider Threat Detection for Sequence Data -- chapter 20 Experiments and Results for Sequence Data -- chapter 21 Scalability Using Big Data Technologies -- chapter 22 Stream Mining and Big Data for Insider Threat Detection -- chapter Conclusion to Part III -- part PART IV Experimental BDMA and BDSP Systems -- chapter Introduction to Part IV -- chapter 23 Cloud Query Processing System for Big Data Management -- chapter 24 Big Data Analytics for Multipurpose Social Media Applications -- chapter 25 Big Data Management and Cloud for Assured Information Sharing -- chapter 26 Big Data Management for Secure Information Integration -- chapter 27 Big Data Analytics for Malware Detection -- chapter 28 A Semantic Web-Based Inference Controller for Provenance Big Data -- part PART V Next Steps for BDMA and BDSP -- chapter Introduction to Part V -- chapter 29 Confidentiality, Privacy, and Trust for Big Data Systems -- chapter 30 Unified Framework for Secure Big Data Management and Analytics -- chapter 31 Big Data, Security, and the Internet of Things -- chapter 32 Big Data Analytics for Malware Detection in Smartphones -- chapter 33 Toward a Case Study in Healthcare for Big Data Analytics and Security -- chapter 34 Toward an Experimental Infrastructure and Education Program for BDMA and BDSP -- chapter 35 Directions for BDSP and BDMA -- chapter Conclusion to Part V -- chapter 36 Summary and Directions…”

Tabla de Contenidos: “…-- Streaming architectures -- Introducing Spark Streaming -- Exploring the architecture of Spark Streaming -- Key concepts -- Advantages -- Challenges -- Introducing Structured Streaming -- Key features and advantages -- Structured Streaming versus Spark Streaming -- Limitations and considerations -- Streaming fundamentals -- Stateless streaming - processing one event at a time -- Stateful streaming - maintaining stateful information -- The differences between stateless and stateful streaming -- Structured Streaming concepts -- Event time and processing time -- Watermarking and late data handling -- Triggers and output modes -- Windowing operations -- Joins and aggregations -- Streaming sources and sinks…”

“…This hands-on guide introduces you to Storm, a distributed, JVM-based system for processing streaming data. Through simple tutorials, sample Java code, and a complete real-world scenario, you’ll learn how to build fast, fault-tolerant solutions that process results as soon as the data arrives…”

Tabla de Contenidos: “…Chapter 1: Introduction to PySpark -- Chapter 2: Data Processing -- Chapter 3: Spark Structured Streaming -- Chapter 4: Airflow -- Chapter 5: Machine Learning Library (MLlib) -- Chapter 6: Supervised Machine Learning -- Chapter 7: Unsupervised Machine Learning -- Chapter 8: Deep Learning Using PySpark…”

Tabla de Contenidos:

Tabla de Contenidos: “…kurtosis -- skewness -- Variance -- Standard deviation -- Covariance -- groupBy -- Rollup -- Cube -- Window functions -- ntiles -- Joins -- Inner workings of join -- Shuffle join -- Broadcast join -- Join types -- Inner join -- Left outer join -- Right outer join -- Outer join -- Left anti join -- Left semi join -- Cross join -- Performance implications of join -- Summary -- Chapter 7: Real-Time Analytics with Apache Spark -- Streaming -- At-least-once processing -- At-most-once processing -- Exactly-once processing -- Spark Streaming -- StreamingContext -- Creating StreamingContext -- Starting StreamingContext -- Stopping StreamingContext -- Input streams -- receiverStream -- socketTextStream -- rawSocketStream -- fileStream -- textFileStream -- binaryRecordsStream -- queueStream -- textFileStream example -- twitterStream example -- Discretized Streams -- Transformations -- Windows operations -- Stateful/stateless transformations -- Stateless transformations -- Stateful transformations -- Checkpointing -- Metadata checkpointing -- Data checkpointing -- Driver failure recovery -- Interoperability with streaming platforms (Apache Kafka) -- Receiver-based -- Direct Stream -- Structured Streaming -- Getting deeper into Structured Streaming -- Handling event time and late date -- Fault-tolerance semantics -- Summary -- Chapter 8: Batch Analytics with Apache Flink -- Introduction to Apache Flink -- Continuous processing for unbounded datasets -- Flink, the streaming model, and bounded datasets -- Installing Flink -- Downloading Flink -- Installing Flink -- Starting a local Flink cluster -- Using the Flink cluster UI -- Batch analytics -- Reading file -- File-based -- Collection-based -- Generic -- Transformations -- GroupBy -- Aggregation -- Joins -- Inner join -- Left outer join -- Right outer join -- Full outer join -- Writing to a file -- Summary…”

Tabla de Contenidos: “…Creating and using fluent interfacesUsing fluent interfaces to hide older interfaces/classes; Using fluent interfaces with the Properties class; Extending fluent interfaces; Default methods and functions; Static default methods; Default methods in Java 8; Multiple inheritance in Java 8; Summary; Chapter 4: Streams and the Evaluation of Expressions; The Stream class and its use; Intermediate and terminal methods; Creating streams; Fixed length streams; Infinite streams; Using the iterate method to create an infinite stream; Using the generate method to create an infinite stream…”

“…So how do you combine real-time, streaming analytics with real-time decisions in an architecture that’s reliable, scalable, and simple? …”

“…No matter where you are in your event streaming journey, Kafka Connect is the ideal tool for building a modern data pipeline…”

“…This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud. …”

“…This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud. …”

Tabla de Contenidos: “…Introducing Akka -- Up and running -- One actor is no actor -- Akka test kit -- Fault tolerance -- Discovery and routing -- Configuration -- Clustering -- Sharding and persistence -- Streams, persistence queries, and projections -- Akka ports -- Real-world example: an Akka betting house -- Clustering, part 2 -- Connecting to systems with Alpakka -- Akka betting house, part 2 -- Akka streams, part 2.…”

Tabla de Contenidos: “…Front Cover; Endorsements; Contents; Foreword: Run Grow Transform; Acknowledgments; Author and Editor; Contributors; Introduction; Chapter 1 - In Pursuit of Growth and Innovation; Chapter 2 - Value Streams: Aligning Resources to Create Value; Chapter 3 - Integrating IT Capabilities into Value Streams; Chapter 4 - Leveraging Value Stream Resources; Chapter 5 - Speeding Ideas to Market; Chapter 6 - Measuring Value; Chapter 7 - Lean Leadership and the Lean Management System; Chapter 8 - The Lean Learning Enterprise…”

Tabla de Contenidos: “…Chapter 1: Introduction to Enterprise Data Lakes -- Chapter 2: Data Lake Ingestion Strategies -- Chapter – 3: Capture Streaming Data with Change-Data-Capture -- Chapter 4: Data Processing Strategies in Data Lakes -- Chapter 5: Data Archiving Strategies in Data Lakes -- Chapter 6: Data Security in Data Lakes -- Chapter 7: Ensuring High-Availability of Data Lakes -- Chapter 8: Managing Data Lake Operations. …”

Tabla de Contenidos: “…Chapter 9: Functional Programming and Reactive Programming -- Combining FP with OOP -- Functional reactive programming -- The Reactive Manifesto -- Functional reactive frameworks for Kotlin -- Getting started with RxKotlin -- Downloading and setting up RxKotlin -- Comparing the Pull mechanism with the RxJava Push mechanism -- Observables -- How Observable works -- The Observable.create method -- The Observable.from methods -- Iterator&lt -- T&gt -- .toObservable -- Subscriber - the Observer interface -- Subscribing and disposing -- Summary -- Chapter 10: Functors, Applicatives, and Monads -- Functors -- Monads -- Applicatives -- Summary -- Chapter 11: Working with Streams in Kotlin -- Introduction to Streams -- Collections versus Streams -- Working with Streams -- Primitive streams -- Stream factory methods -- Stream Builder -- Creating empty Streams - Stream.empty() -- Creating a Stream by passing elements - Stream.of() -- Generating Streams - Stream.generate() -- Collector and Stream.collect - collecting Streams -- The Collectors.toList(), Collectors.toSet(), and Collectors.toCollection() methods -- Collecting into Map - Collectors.toMap() -- Joining Stream of strings - Collectors.joining() -- Grouping elements of Stream - Collectors.groupingBy() -- Summary -- Chapter 12: Getting Started with Arrow -- Function composition -- Partial application -- Binding -- Reverse -- Pipes -- Currying -- Differences between the currying and partial application -- Logical complement -- Memoization -- Partial functions -- Identity and constant -- Optics -- Configuring Arrows code generation -- Generating lenses -- Summary -- Chapter 13: Arrow Types -- Option -- Arrow's type hierarchy -- Either -- Monad transformers -- Try -- State -- Corecursion with State -- Summary -- Appendix: Kotlin's Quick Start -- Writing and running Kotlin -- Kotlin online -- On your console…”