Cognitive Computing for Big Data Systems Over IoT下载

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Cognitive Computing for Big Data Systems Over IoT: Frameworks, Tools and Applications 1st ed. 2018 Edition , 相关下载链接：https://download.csdn.net/download/kxg1005/10430670?utm_source=bbsseo
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Cognitive Computing for Big Data Systems Over IoT
Cognitive Computing for Big Data Systems Over IoT: Frameworks, Tools and Applications 1st ed. 2018 Edition
Cognitive Computing for Big Data Systems Over IoT Frameworks, Tools and 无水印原版pdf
Cognitive Computing for Big Data Systems Over IoT Frameworks, Tools and Applications 英文无水印原版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书
Cognitive Computing for Big Data Systems Over IoT(2017).epub
Cognitive systems attract major attention in the new era of computing. In addition, cognitive computing delivers an extended guidance towards building a new class of systems with convergence of big data and Internet of Things (IoT). The human life is driven by the smart electronic devices called IoT. Moreover, IoT devices generate and exchange more amounts of data. Extracting the valid truth from this data becomes a hectic task. Consequently, machine learning techniques have been proposed to analyse large amounts of data and enhance decision-making. The development of new techniques helps to provide relevant information to users with high efficiency. Today’s world is driven by the era of digital data. People not only look into conceiving information from the data but also perform exploratory data analysis, thus, the area of big data analytics has emerged. Analysis of data sets can find new correlations to spot business trends, prevent diseases, and combat crime, etc. In cognitive computing, new hardware or software devices mimic human brain and take a decision appropriate to the situation. Moreover, cognitive computing is used in numerous artificial intelligence (AI) applications, including expert systems, natural language programming, neural networks, robotics and virtual reality. Further, cognitive computing has lots of applications in every area of our lives, from travel, sports and entertainment, to fitness, health and wellness, etc. In the business domain, entrepreneurs from different industries have already created products and services based on cognitive technologies. Cognitive computing helps them by giving intelligent recommendation through data analysis. Cognitive computing is not helping only humans, it is also helping veterinarians take better care of the animals that come into their practices. In future, cognitive systems provide expert assistance to a problem without the intervention of human beings. Self-learning capability of human beings is adapted to the system by applying artificial intelligence to it. Thus, the combination of big data analysis and cognitive computing methodologies over IoT devices can change the world with new colour of Intelligence. This book attempts to explicate the state-of-the-art research in cognitive computing for big data systems and provide a comprehensive and in-depth coverage of the key subjects in the field of IoT. The book is invaluable, topical and timely and can serve nicely as a reference book for courses at both undergraduate and postgraduate levels. It can also serve as a key source of knowledge for scientists, professionals, researchers and academicians, who are interested in new challenges, theories, practice and advanced applications of cognitive computing. I am happy to inform the readers that this book titled “Cognitive Computing for Big Data Systems over IoT” addresses important research directions in cognitive computing and the development of innovative big data models for analysing the data generated by IOT devices. It marks an important step in the maturation of this field and will serve to unify, advance and challenge the scientific community in many ways. I commend the editors and the authors on their accomplishment, and hope that the readers will find the book useful and a source of inspiration for their research and professional activity.
Handbook of Big Data Technologies
Title: Handbook of Big Data Technologies Length: 895 pages Edition: 1st ed. 2017 Language: English Publisher: Springer Publication Date: 2017-03-26 ISBN-10: 3319493396 ISBN-13: 9783319493398 Table of Contents Part I Fundamentals of Big Data Processing Big Data Storage and Data Models 1 Storage Models 2 Data Models Big Data Programming Models 1 MapReduce 2 Functional Programming 3 SQL-Like 4 Actor Model 5 Statistical and Analytical 6 Dataflow-Based 7 Bulk Synchronous Parallel 8 High Level DSL 9 Discussion and Conclusion Programming Platforms for Big Data Analysis 1 Introduction 2 Requirements of Big Data Programming Support 3 Classification of Programming Platforms 4 Major Existing Programming Platforms 5 A Unifying Framework 6 Conclusion and Future Directions Big Data Analysis on Clouds 1 Introduction 2 Introducing Cloud Computing 3 Cloud Solutions for Big Data 4 Systems for Big Data Analytics in the Cloud 5 Research Trends 6 Conclusions Data Organization and Curation in Big Data 1 Big Data Indexing Techniques 2 Data Organization and Layout Techniques 3 Non-traditional Workloads in Big Data 4 Curation and Metadata Management in Big Data 5 Conclusion Big Data Query Engines 1 Introduction 2 Massively Parallel Query Engines 3 Hadoop Query Engines 4 SQL on Hadoop 5 Query Optimization 6 Query Execution 7 Summary Large-Scale Data Stream Processing Systems 1 Introduction 2 Programming Models 3 System Support for Distributed Data Streaming 4 Case Study: Stream Processing with Apache Flink 5 Applications, Trends and Open Challenges 6 Conclusions and Outlook Part II Semantic Big Data Management Semantic Data Integration 1 An Important Challenge 2 Current State-of-the-Art 3 The Path Forward Linked Data Management 1 Introduction 2 Background Information 3 Native Linked Data Stores 4 Provenance for Linked Data Non-native RDF Storage Engines 1 Introduction 2 Storing Linked Data Using Relational Databases 3 No-SQL Stores 4 Massively Parallel Processing for Linked Data Exploratory Ad-Hoc Analytics for Big Data 1 Exploratory Analytics for Big Data 2 A Top-K Entity Augmentation System 3 DrillBeyond -- Processing Open World SQL 4 Summary and Future Work Pattern Matching Over Linked Data Streams 1 Overview 2 Linked Data Dissemination System 3 Experimental Evaluation 4 Related Work 5 Summary Searching the Big Data: Practices and Experiences in Efficiently Querying Knowledge Bases 1 Introduction 2 Background 3 The Framework of Cache-Based Knowledge Base Querying 4 Similar Queries Suggestion 5 Cache Replacement 6 Implementation and Experimental Evaluation 7 Related Work 8 Discussion and Conclusion Part III Big Graph Analytics Management and Analysis of Big Graph Data: Current Systems and Open Challenges 1 Introduction 2 Graph Databases 3 Graph Processing 4 Graph Dataflow Systems 5 Gradoop 6 Comparison 7 Current Research and Open Challenges 8 Conclusions and Outlook Similarity Search in Large-Scale Graph Databases 1 Introduction 2 Preliminaries 3 The Pruning-Verification Framework 4 State-of-the-Art Approaches 5 Future Research Directions 6 Summary Big-Graphs: Querying, Mining, and Beyond 1 Introduction 2 Graph Data Models 3 Pattern Matching Techniques Over Big-Graphs 4 Mining Techniques Over Big-Graphs 5 Open Problems 6 Conclusions 7 About Authors Link and Graph Mining in the Big Data Era 1 Introduction 2 Definitions 3 Temporal Evolution 4 Link Prediction 5 Community Detection 6 Graphs in Big Data 7 Weighted Networks 8 Extending Graph Models: Multilayer Networks 9 Open Challenges 10 Conclusions Granular Social Network: Model and Applications 1 Introduction 2 Preliminaries 3 Literature Review 4 Fuzzy Granular Social Networks (FGSN) 5 Discussions and Conclusions Part IV Big Data Applications Big Data, IoT and Semantics 1 Introduction 2 Semantics for Big Data 3 Big Data and Semantics in the Internet of Things 4 Social Mining 5 Graph Mining 6 Big Stream Data Mining 7 Geo-Referenced Data Mining 8 Conclusion SCADA Systems in the Cloud 1 Introduction 2 Related Work 3 An Overview of SCADA 4 Moving SCADA to the Cloud 5 Conceptual SCADA Cloud Orchestration Framework 6 Results 7 Conclusion Quantitative Data Analysis in Finance 1 Introduction 2 The Three V's of Big Data in High Frequency Data 3 Data Cleaning, Aggregating and Management 4 Modeling High Frequency Data in Finance 5 Portfolio Selection and Evaluation 6 The Future 7 Conclusion Emerging Cost Effective Big Data Architectures 1 Introduction 2 Emerging Solutions for Big Data 3 Future Directions 4 Conclusion Bringing High Performance Computing to Big Data Algorithms 1 Introduction 2 GPU Acceleration of Alternating Least Squares 3 GPU Acceleration of Singular Value Decomposition 4 Conclusions Cognitive Computing: Where Big Data Is Driving Us 1 Cognitive Computing: An Alternative Approach for Clear Understanding 2 Big Data Impulsing Cognitive System 3 Traditional Systems versus Cognitive Systems? 4 Data Mining in the Era of Cognitive Systems 5 Design Methods for Cognitive Systems 6 Cognitive Systems 7 The Future of Cognitive Systems 8 Final Remarks Privacy-Preserving Record Linkage for Big Data: Current Approaches and Research Challenges 1 Introduction 2 Background 3 Privacy Aspects and Techniques for PPRL 4 Scalability Techniques for PPRL 5 Multi-party PPRL 6 Open Challenges 7 Conclusions
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