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• INTRODUCTION ix
• CHAPTER 1. CONTENT DISTRIBUTION ON THE INTERNET 1 1.1. End-to-end concept and limitations 2 1.2. Multicast communication 4 1.3. Peer-to-peer systems 5 1.4. Content distribution networks 6 1.5. Publish/subscribe systems 9
• CHAPTER 2. INFORMATION-CENTRIC NETWORKS 13 2.1. Content naming 13 2.1.1. Flat naming 14 2.1.2. Hierarchical naming 16 2.1.3. Attribute-based names 17 2.2. Content or name-based routing 18 2.2.1. Non-hierarchical routing 19 2.2.2. Hierarchical routing 20 2.3. Content caching 22
• CHAPTER 3. MAIN ICN ARCHITECTURES 23 3.1. Content-based networking/combined broadcast and content-based 23 3.2. Data-oriented network architecture 26 3.3. Content-centric networking/named-data networking 29 3.4. Publish-subscribe Internet routing paradigm/publish-subscribe Internet technologies 33 3.5. Content-centric inter-network architecture 37 3.6. Other architectures 40 3.7. General comparison 41
• CHAPTER 4. CHALLENGES 43 4.1. Naming 43 4.2. Routing 52 4.3. Caching 58 4.3.1. Analytical models for networks of caches 60 4.3.2. Content replacement policies 62 4.3.3. Content storage policies 65 4.4. Security 69 4.5. Mobility support in ICN 73 4.6. Applications 78 4.6.1. Real-time applications 78 4.6.2. Vehicular networks 80 4.6.3. Autonomous driving 81 4.6.4. Other applications 82
• CHAPTER 5. PRACTICAL ISSUES 83 5.1. Economic models 83 5.2. Content routers 88 CONCLUSION 97 ACKNOWLEDGMENT 99 BIBLIOGRAPHY 101 INDEX 119.
• (source: Nielsen Book Data)

Since its inception, the Internet has evolved from a textual information system towards a multimedia information system, in which data, services and applications are consumed as content. Today, however, the main problem faced is that applications are now content-oriented but the protocol stack remains the same, based on the content location. Thus, it is clear that the Internet s current architecture must change. This new architecture should take into account aspects to improve content location and delivery efficiency and also content availability. Fulfilling these requirements is the main goal of information-centric networks (ICNs). ICN is a new communication paradigm to increase the efficiency of content delivery and also content availability. In this new concept, the network infrastructure actively contributes to content caching and distribution. This book presents the basic concepts of ICNs, describes the main architecture proposals for these networks, and discusses the main challenges to their development. Information Centric-Networks looks at the current challenges for this concept, including: naming, routing and caching on the network-core elements, several aspects of content security, user privacy, and practical issues in implementing ICNs. Contents 1. Content Distribution on the Internet. 2. Information-Centric Networks. 3. Main ICN Architectures. 4. Challenges. 5. Practical Issues. About the Authors Gabriel M. Brito is an Engineer at Petrobras in Brazil and studying for a Master s degree at the Universidade Federal Fluminense in Brazil. Pedro Braconnot Velloso is an Associate Professor in the Department of Computer Science at the Universidade Federal Fluminense (UFF), Brazil. He worked for Bell Labs France as a research engineer from 2009 to 2011. Igor M. Moraes is an Associate Professor at the Universidade Federal Fluminense in Brazil.
(source: Nielsen Book Data)

• 1. Big Data Moves to the Center of the Universe
• 2. Measurement
• 3. Annotation
• 4. Identification, De-identification, and Re-identification
• 5. Ontologies and Semantics: How information is endowed with meaning
• 6. Standards and their Versions
• 7. Legacy Data
• 8. Hypothesis Testing
• 9. Prediction
• 10. Software
• 11. Complexity
• 12. Vulnerabilities
• 13. Legalities
• 14. Social and Ethical Issues.
• (source: Nielsen Book Data)

Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators.
(source: Nielsen Book Data)

• Acknowledgments xi 1 Introduction 1 1.1 Part I: The Self-Organizing Method 1 1.2 Part II: Dynamic Self-Organization for Image Filtering andMultimedia Retrieval 2 1.3 Part III: Dynamic Self-Organization for Image Segmentationand Visualization 5 1.4 Future Directions 7 2 Unsupervised Learning 9 2.1 Introduction 9 2.2 Unsupervised Clustering 9 2.3 Distance Metrics for Unsupervised Clustering 11 2.4 Unsupervised Learning Approaches 13 2.4.1 Partitioning and Cluster Membership 13 2.4.2 Iterative Mean-Squared Error Approaches 15 2.4.3 Mixture Decomposition Approaches 17 2.4.4 Agglomerative Hierarchical Approaches 18 2.4.5 Graph-Theoretic Approaches 20 2.4.6 Evolutionary Approaches 20 2.4.7 Neural Network Approaches 21 2.5 Assessing Cluster Quality and Validity 21 2.5.1 Cost Function Based Cluster Validity Indices 22 2.5.2 Density-Based Cluster Validity Indices 23 2.5.3 Geometric-Based Cluster Validity Indices 24 3 Self-Organization 27 3.1 Introduction 27 3.2 Principles of Self-Organization 27 3.2.1 Synaptic Self-Amplification and Competition 27 3.2.2 Cooperation 28 3.2.3 Knowledge Through Redundancy 29 3.3 Fundamental Architectures 29 3.3.1 Adaptive Resonance Theory 29 3.3.2 Self-Organizing Map 37 3.4 Other Fixed Architectures for Self-Organization 43 3.4.1 Neural Gas 44 3.4.2 Hierarchical Feature Map 45 3.5 Emerging Architectures for Self-Organization 46 3.5.1 Dynamic Hierarchical Architectures 47 3.5.2 Nonstationary Architectures 48 3.5.3 Hybrid Architectures 50 3.6 Conclusion 50 4 Self-Organizing Tree Map 53 4.1 Introduction 53 4.2 Architecture 54 4.3 Competitive Learning 55 4.4 Algorithm 57 4.5 Evolution 61 4.5.1 Dynamic Topology 61 4.5.2 Classification Capability 64 4.6 Practical Considerations, Extensions, and Refinements 68 4.6.1 The Hierarchical Control Function 68 4.6.2 Learning, Timing, and Convergence 71 4.6.3 Feature Normalization 73 4.6.4 Stop Criteria 73 4.7 Conclusions 74 5 Self-Organization in Impulse Noise Removal 75 5.1 Introduction 75 5.2 Review of Traditional Median-Type Filters 76 5.3 The Noise-Exclusive Adaptive Filtering 82 5.3.1 Feature Selection and Impulse Detection 82 5.3.2 Noise Removal Filters 84 5.4 Experimental Results 86 5.5 Detection-Guided Restoration and Real-Time Processing 99 5.5.1 Introduction 99 5.5.2 Iterative Filtering 101 5.5.3 Recursive Filtering 104 5.5.4 Real-Time Processing of Impulse Corrupted TV Pictures105 5.5.5 Analysis of the Processing Time 109 5.6 Conclusions 115 6 Self-Organization in Image Retrieval 119 6.1 Retrieval of Visual Information 120 6.2 Visual Feature Descriptor 122 6.2.1 Color Histogram and Color Moment Descriptors 122 6.2.2 Wavelet Moment and Gabor Texture Descriptors 123 6.2.3 Fourier and Moment-based Shape Descriptors 125 6.2.4 Feature Normalization and Selection 127 6.3 User-Assisted Retrieval 130 6.3.1 Radial Basis Function Method 132 6.4 Self-Organization for Pseudo Relevance Feedback 136 6.5 Directed Self-Organization 140 6.5.1 Algorithm 142 6.6 Optimizing Self-Organization for Retrieval 146 6.6.1 Genetic Principles 147 6.6.2 System Architecture 149 6.6.3 Genetic Algorithm for Feature Weight Detection 150 6.7 Retrieval Performance 153 6.7.1 Directed Self-Organization 153 6.7.2 Genetic Algorithm Weight Detection 155 6.8 Summary 157 7 The Self-Organizing Hierarchical Variance Map 159 7.1 An Intuitive Basis 160 7.2 Model Formulation and Breakdown 162 7.2.1 Topology Extraction via Competitive Hebbian Learning163 7.2.2 Local Variance via Hebbian Maximal Eigenfilters 165 7.2.3 Global and Local Variance Interplay for Map Growth andTermination 170 7.3 Algorithm 173 7.3.1 Initialization, Continuation, and Presentation 173 7.3.2 Updating Network Parameters 175 7.3.3 Vigilance Evaluation and Map Growth 175 7.3.4 Topology Adaptation 176 7.3.5 Node Adaptation 177 7.3.6 Optional Tuning Stage 177 7.4 Simulations and Evaluation 177 7.4.1 Observations of Evolution and Partitioning 178 7.4.2 Visual Comparisons with Popular Mean-Squared ErrorArchitectures 181 7.4.3 Visual Comparison Against Growing Neural Gas 183 7.4.4 Comparing Hierarchical with Tree-Based Methods 183 7.5 Tests on Self-Determination and the Optional Tuning Stage187 7.6 Cluster Validity Analysis on Synthetic and UCI Data 187 7.6.1 Performance vs. Popular Clustering Methods 190 7.6.2 IRIS Dataset 192 7.6.3 WINE Dataset 195 7.7 Summary 195 8 Microbiological Image Analysis Using Self-Organization197 8.1 Image Analysis in the Biosciences 197 8.1.1 Segmentation: The Common Denominator 198 8.1.2 Semi-supervised versus Unsupervised Analysis 199 8.1.3 Confocal Microscopy and Its Modalities 200 8.2 Image Analysis Tasks Considered 202 8.2.1 Visualising Chromosomes During Mitosis 202 8.2.2 Segmenting Heterogeneous Biofilms 204 8.3 Microbiological Image Segmentation 205 8.3.1 Effects of Feature Space Definition 207 8.3.2 Fixed Weighting of Feature Space 209 8.3.3 Dynamic Feature Fusion During Learning 213 8.4 Image Segmentation Using Hierarchical Self-Organization215 8.4.1 Gray-Level Segmentation of Chromosomes 215 8.4.2 Automated Multilevel Thresholding of Biofilm 220 8.4.3 Multidimensional Feature Segmentation 221 8.5 Harvesting Topologies to Facilitate Visualization 226 8.5.1 Topology Aware Opacity and Gray-Level Assignment 227 8.5.2 Visualization of Chromosomes During Mitosis 228 8.6 Summary 233 9 Closing Remarks and Future Directions 237 9.1 Summary of Main Findings 237 9.1.1 Dynamic Self-Organization: Effective Models for EfficientFeature Space Parsing 237 9.1.2 Improved Stability, Integrity, and Efficiency 238 9.1.3 Adaptive Topologies Promote Consistency and UncoverRelationships 239 9.1.4 Online Selection of Class Number 239 9.1.5 Topologies Represent a Useful Backbone for Visualizationor Analysis 240 9.2 Future Directions 240 9.2.1 Dynamic Navigation for Information Repositories 241 9.2.2 Interactive Knowledge-Assisted Visualization 243 9.2.3 Temporal Data Analysis Using Trajectories 245 Appendix A 249 A.1 Global and Local Consistency Error 249 References 251 Index 269.
• (source: Nielsen Book Data)

A new approach to unsupervised learning Evolving technologies have brought about an explosion ofinformation in recent years, but the question of how suchinformation might be effectively harvested, archived, and analyzedremains a monumental challenge for the processing of suchinformation is often fraught with the need for conceptualinterpretation: a relatively simple task for humans, yet an arduousone for computers. Inspired by the relative success of existing popular research onself-organizing neural networks for data clustering and featureextraction, Unsupervised Learning: A Dynamic Approachpresents information within the family of generative, self-organizing maps, such as the self-organizing tree map (SOTM)and the more advanced self-organizing hierarchical variance map(SOHVM). It covers a series of pertinent, real-world applicationswith regard to the processing of multimedia data from itsrole in generic image processing techniques, such as the automatedmodeling and removal of impulse noise in digital images, toproblems in digital asset management and its various roles infeature extraction, visual enhancement, segmentation, and analysisof microbiological image data. Self-organization concepts and applications discussedinclude: * Distance metrics for unsupervised clustering * Synaptic self-amplification and competition * Image retrieval * Impulse noise removal * Microbiological image analysis Unsupervised Learning: A Dynamic Approach introduces anew family of unsupervised algorithms that have a basis inself-organization, making it an invaluable resource forresearchers, engineers, and scientists who want to create systemsthat effectively model oppressive volumes of data with little or nouser intervention.
(source: Nielsen Book Data)

• Acknowledgments xi 1 Introduction 1 1.1 Part I: The Self-Organizing Method 1 1.2 Part II: Dynamic Self-Organization for Image Filtering andMultimedia Retrieval 2 1.3 Part III: Dynamic Self-Organization for Image Segmentationand Visualization 5 1.4 Future Directions 7 2 Unsupervised Learning 9 2.1 Introduction 9 2.2 Unsupervised Clustering 9 2.3 Distance Metrics for Unsupervised Clustering 11 2.4 Unsupervised Learning Approaches 13 2.4.1 Partitioning and Cluster Membership 13 2.4.2 Iterative Mean-Squared Error Approaches 15 2.4.3 Mixture Decomposition Approaches 17 2.4.4 Agglomerative Hierarchical Approaches 18 2.4.5 Graph-Theoretic Approaches 20 2.4.6 Evolutionary Approaches 20 2.4.7 Neural Network Approaches 21 2.5 Assessing Cluster Quality and Validity 21 2.5.1 Cost Function Based Cluster Validity Indices 22 2.5.2 Density-Based Cluster Validity Indices 23 2.5.3 Geometric-Based Cluster Validity Indices 24 3 Self-Organization 27 3.1 Introduction 27 3.2 Principles of Self-Organization 27 3.2.1 Synaptic Self-Amplification and Competition 27 3.2.2 Cooperation 28 3.2.3 Knowledge Through Redundancy 29 3.3 Fundamental Architectures 29 3.3.1 Adaptive Resonance Theory 29 3.3.2 Self-Organizing Map 37 3.4 Other Fixed Architectures for Self-Organization 43 3.4.1 Neural Gas 44 3.4.2 Hierarchical Feature Map 45 3.5 Emerging Architectures for Self-Organization 46 3.5.1 Dynamic Hierarchical Architectures 47 3.5.2 Nonstationary Architectures 48 3.5.3 Hybrid Architectures 50 3.6 Conclusion 50 4 Self-Organizing Tree Map 53 4.1 Introduction 53 4.2 Architecture 54 4.3 Competitive Learning 55 4.4 Algorithm 57 4.5 Evolution 61 4.5.1 Dynamic Topology 61 4.5.2 Classification Capability 64 4.6 Practical Considerations, Extensions, and Refinements 68 4.6.1 The Hierarchical Control Function 68 4.6.2 Learning, Timing, and Convergence 71 4.6.3 Feature Normalization 73 4.6.4 Stop Criteria 73 4.7 Conclusions 74 5 Self-Organization in Impulse Noise Removal 75 5.1 Introduction 75 5.2 Review of Traditional Median-Type Filters 76 5.3 The Noise-Exclusive Adaptive Filtering 82 5.3.1 Feature Selection and Impulse Detection 82 5.3.2 Noise Removal Filters 84 5.4 Experimental Results 86 5.5 Detection-Guided Restoration and Real-Time Processing 99 5.5.1 Introduction 99 5.5.2 Iterative Filtering 101 5.5.3 Recursive Filtering 104 5.5.4 Real-Time Processing of Impulse Corrupted TV Pictures105 5.5.5 Analysis of the Processing Time 109 5.6 Conclusions 115 6 Self-Organization in Image Retrieval 119 6.1 Retrieval of Visual Information 120 6.2 Visual Feature Descriptor 122 6.2.1 Color Histogram and Color Moment Descriptors 122 6.2.2 Wavelet Moment and Gabor Texture Descriptors 123 6.2.3 Fourier and Moment-based Shape Descriptors 125 6.2.4 Feature Normalization and Selection 127 6.3 User-Assisted Retrieval 130 6.3.1 Radial Basis Function Method 132 6.4 Self-Organization for Pseudo Relevance Feedback 136 6.5 Directed Self-Organization 140 6.5.1 Algorithm 142 6.6 Optimizing Self-Organization for Retrieval 146 6.6.1 Genetic Principles 147 6.6.2 System Architecture 149 6.6.3 Genetic Algorithm for Feature Weight Detection 150 6.7 Retrieval Performance 153 6.7.1 Directed Self-Organization 153 6.7.2 Genetic Algorithm Weight Detection 155 6.8 Summary 157 7 The Self-Organizing Hierarchical Variance Map 159 7.1 An Intuitive Basis 160 7.2 Model Formulation and Breakdown 162 7.2.1 Topology Extraction via Competitive Hebbian Learning163 7.2.2 Local Variance via Hebbian Maximal Eigenfilters 165 7.2.3 Global and Local Variance Interplay for Map Growth andTermination 170 7.3 Algorithm 173 7.3.1 Initialization, Continuation, and Presentation 173 7.3.2 Updating Network Parameters 175 7.3.3 Vigilance Evaluation and Map Growth 175 7.3.4 Topology Adaptation 176 7.3.5 Node Adaptation 177 7.3.6 Optional Tuning Stage 177 7.4 Simulations and Evaluation 177 7.4.1 Observations of Evolution and Partitioning 178 7.4.2 Visual Comparisons with Popular Mean-Squared ErrorArchitectures 181 7.4.3 Visual Comparison Against Growing Neural Gas 183 7.4.4 Comparing Hierarchical with Tree-Based Methods 183 7.5 Tests on Self-Determination and the Optional Tuning Stage187 7.6 Cluster Validity Analysis on Synthetic and UCI Data 187 7.6.1 Performance vs. Popular Clustering Methods 190 7.6.2 IRIS Dataset 192 7.6.3 WINE Dataset 195 7.7 Summary 195 8 Microbiological Image Analysis Using Self-Organization197 8.1 Image Analysis in the Biosciences 197 8.1.1 Segmentation: The Common Denominator 198 8.1.2 Semi-supervised versus Unsupervised Analysis 199 8.1.3 Confocal Microscopy and Its Modalities 200 8.2 Image Analysis Tasks Considered 202 8.2.1 Visualising Chromosomes During Mitosis 202 8.2.2 Segmenting Heterogeneous Biofilms 204 8.3 Microbiological Image Segmentation 205 8.3.1 Effects of Feature Space Definition 207 8.3.2 Fixed Weighting of Feature Space 209 8.3.3 Dynamic Feature Fusion During Learning 213 8.4 Image Segmentation Using Hierarchical Self-Organization215 8.4.1 Gray-Level Segmentation of Chromosomes 215 8.4.2 Automated Multilevel Thresholding of Biofilm 220 8.4.3 Multidimensional Feature Segmentation 221 8.5 Harvesting Topologies to Facilitate Visualization 226 8.5.1 Topology Aware Opacity and Gray-Level Assignment 227 8.5.2 Visualization of Chromosomes During Mitosis 228 8.6 Summary 233 9 Closing Remarks and Future Directions 237 9.1 Summary of Main Findings 237 9.1.1 Dynamic Self-Organization: Effective Models for EfficientFeature Space Parsing 237 9.1.2 Improved Stability, Integrity, and Efficiency 238 9.1.3 Adaptive Topologies Promote Consistency and UncoverRelationships 239 9.1.4 Online Selection of Class Number 239 9.1.5 Topologies Represent a Useful Backbone for Visualizationor Analysis 240 9.2 Future Directions 240 9.2.1 Dynamic Navigation for Information Repositories 241 9.2.2 Interactive Knowledge-Assisted Visualization 243 9.2.3 Temporal Data Analysis Using Trajectories 245 Appendix A 249 A.1 Global and Local Consistency Error 249 References 251 Index 269.
• (source: Nielsen Book Data)

A new approach to unsupervised learning Evolving technologies have brought about an explosion ofinformation in recent years, but the question of how suchinformation might be effectively harvested, archived, and analyzedremains a monumental challenge for the processing of suchinformation is often fraught with the need for conceptualinterpretation: a relatively simple task for humans, yet an arduousone for computers. Inspired by the relative success of existing popular research onself-organizing neural networks for data clustering and featureextraction, Unsupervised Learning: A Dynamic Approachpresents information within the family of generative, self-organizing maps, such as the self-organizing tree map (SOTM)and the more advanced self-organizing hierarchical variance map(SOHVM). It covers a series of pertinent, real-world applicationswith regard to the processing of multimedia data from itsrole in generic image processing techniques, such as the automatedmodeling and removal of impulse noise in digital images, toproblems in digital asset management and its various roles infeature extraction, visual enhancement, segmentation, and analysisof microbiological image data. Self-organization concepts and applications discussedinclude: * Distance metrics for unsupervised clustering * Synaptic self-amplification and competition * Image retrieval * Impulse noise removal * Microbiological image analysis Unsupervised Learning: A Dynamic Approach introduces anew family of unsupervised algorithms that have a basis inself-organization, making it an invaluable resource forresearchers, engineers, and scientists who want to create systemsthat effectively model oppressive volumes of data with little or nouser intervention.
(source: Nielsen Book Data)

• Basics Database Queries Basic Setting Exact Search Similarity Search Join Errors
• Low-Dimensional Index Structures Hashing Static Hashing Dynamic Hashing Locality Sensitive Hashing (LSH) Multi-Dimensional Hashing Space-Filling Curves
• Memory-Based Index Structures Index Structures Binary Search Tree (BST) Quadtree K-D-Tree Range Tree Voronoi Diagram Tries Suffix Tree Bitmap Index
• Disk-Based Index Structures Hierarchical Structures B-Tree and B+-Tree K-D-B-Tree General Framework R-Tree R*-Tree R+-Tree Hilbert R-Tree SS-Tree SR-Tree P-Tree Bulk-Loading
• Distances Distance Functions Metric Spaces Lp Norm Quadratic Form Distance Cosine Similarity Statistical Distance Measures Distances between Sets of Objects Earth Mover's Distance Edit Distance
• Distance-Based Structures Triangular Inequality VP-Tree GH-Tree GNAT M-Tree SA-Tree AESA Linear AESA (LAESA) AESA for Vector Spaces
• High-Dimensional Spaces Curse of Dimensionality Analysis of Search for High-Dimensional Data Expected Nearest Neighbor Distance Expected Number of Page Accesses Curse of Dimensionality
• High-Dimensionality Structures X-Tree Pyramid Technique IMinMax VA-File A-Tree IQ-Tree
• Data Reduction Techniques Dimensionality Reduction Techniques Properties Useful for Similarity Search Quality Measures Embedding Singular Value Decomposition (SVD) Principal Component Analysis (PCA) Multi-Dimensional Scaling (MDS) IsoMap FastMap Embedding Methods Bounds on Distortion
• Data Representation Techniques Discrete Fourier Transform (DFT) Discrete Cosine Transform (DCT) Discrete Wavelet Transform (DWT) V-Optimal Histogram
• Appendices A Memory and Disk Accesses Memory Access Disks Flash
• B Distances of Bounding Boxes Distance of a Point from a Rectangle Distance of a Point from a Sphere Distance of a Sphere from a Rectangle Distance of a Sphere from a Sphere Distance of a Rectangle from a Rectangle
• C Vectors and Matrices Vector Spaces Matrices Properties of Matrices Dimensionality
• D Probability and Statistics Random Variable Probability Distribution Statistical Parameters.
• (source: Nielsen Book Data)

• Basics. Low-Dimensional Index Structures. Disk-Based Index Structures. Distances. High-Dimensional Spaces. Data Reduction Techniques. Appendices.
• (source: Nielsen Book Data)

Fundamentals of Database Indexing and Searching presents well-known database searching and indexing techniques. It focuses on similarity search queries, showing how to use distance functions to measure the notion of dissimilarity. After defining database queries and similarity search queries, the book organizes the most common and representative index structures according to their characteristics. The author first describes low-dimensional index structures, memory-based index structures, and hierarchical disk-based index structures. He then outlines useful distance measures and index structures that use the distance information to efficiently solve similarity search queries. Focusing on the difficult dimensionality phenomenon, he also presents several indexing methods that specifically deal with high-dimensional spaces. In addition, the book covers data reduction techniques, including embedding, various data transforms, and histograms. Through numerous real-world examples, this book explores how to effectively index and search for information in large collections of data. Requiring only a basic computer science background, it is accessible to practitioners and advanced undergraduate students.
(source: Nielsen Book Data)
Fundamentals of Database Indexing and Searching presents well-known database searching and indexing techniques. It focuses on similarity search queries, showing how to use distance functions to measure the notion of dissimilarity.After defining database queries and similarity search queries, the book organizes the most common and representative ind.
(source: Nielsen Book Data)

• 1. Bridging the Blockchain Knowledge Gap
• 2. The Mist Browser
• 3. The EVM
• 4. Solidity Programming
• 5. Smart Contacts and Tokens
• 6. Mining Ether
• 7. Cryptoeconomics Survey
• 8. Dapp Deployment
• 9. Creating Private Chains
• 10. Use Cases
• 11. Advanced Concepts.
• (source: Nielsen Book Data)

Learn how to use Solidity and the Ethereum project - second only to Bitcoin in market capitalization. Blockchain protocols are taking the world by storm, and the Ethereum project, with its Turing-complete scripting language Solidity, has rapidly become a front-runner. This book presents the blockchain phenomenon in context; then situates Ethereum in a world pioneered by Bitcoin. See why professionals and non-professionals alike are honing their skills in smart contract patterns and distributed application development. You'll review the fundamentals of programming and networking, alongside its introduction to the new discipline of crypto-economics. You'll then deploy smart contracts of your own, and learn how they can serve as a back-end for JavaScript and HTML applications on the Web. Many Solidity tutorials out there today have the same flaw: they are written for "advanced" JavaScript developers who want to transfer their skills to a blockchain environment. Introducing Ethereum and Solidity is accessible to technology professionals and enthusiasts of all levels. You'll find exciting sample code that can move forward real world assets in both the academic and the corporate arenas. Find out now why this book is a powerful gateway for creative technologists of all types, from concept to deployment. What You'll Learn See how Ethereum (and other cryptocurrencies) work Compare distributed apps (dapps) to web apps Write Ethereum smart contracts in Solidity Connect Ethereum smart contracts to your HTML/CSS/JavaScript web applications Deploy your own dapp, coin, and blockchain Work with basic and intermediate smart contracts Who This Book Is For Anyone who is curious about Ethereum or has some familiarity with computer science Product managers, CTOs, and experienced JavaScript programmers Experts will find the advanced sample projects in this book rewarding because of the power of Solidity.
(source: Nielsen Book Data) Written by a technology journalist trained in breaking down technical concepts into easy-to-understand prose, this book updates you on the last three years of progress since Bitcoin became popular ئ and then situates Ethereum in a world pioneered by Bitcoin. -- Edited summary from book.

• Cover; Title; EUL; Dedication; Contents; Foreword; Preface; Introduction; Chapter 01; Chapter 02; Chapter 03; Chapter 04; Chapter 05; Chapter 06; Chapter 07; Chapter 08; Chapter 09; Chapter 10; Chapter 11; Chapter 12; Bibliography; Index.

Introduction to Computer Data Representation introduces readers to the representation of data within computers. Starting from basic principles of number representation in computers, the book covers the representation of both integer and floating point numbers, and characters or text. It comprehensively explains the main techniques of computer arithmetic and logical manipulation. The book also features chapters covering the less usual topics of basic checksums and 'universal' or variable length representations for integers, with additional coverage of Gray Codes, BCD codes and logarithmic repre.

• 1 FRONT MATTER
• 2 1. EXECUTIVE SUMMARY
• 3 2. INTRODUCTION
• 4 3. IMPACT INDICATORS
• 5 4. INDICATORS OF INTERNET IMPACTS
• 6 5. INTERNET DIFFUSION OR PATHS OF IMPACTS
• 7 6. CONCLUSIONS AND CALL FOR CONTINUED RESEARCH
• 8 APPENDIX A
• 9 APPENDIX B
• 10 APPENDIX C
• 11 APPENDIX D
• 12 APPENDIX E
• 13 APPENDIX F
• 14 APPENDIX G.
• (source: Nielsen Book Data)

• Cover; Copyright; Credits; About the Author; About the Reviewers; www.packtpub.com; packtlib.packtpub.com; Table of Contents; Instant BlueStacks; So, what is BlueStacks?; App Player; Cloud Connect; Installation; Step 1
• what do I need?; Step 2
• downloading BlueStacks App Player; Step 3
• installing BlueStacks App Player; For Windows 7; For Windows 8; For Mac OS X; And that's it; Quick start
• installing your first app; Step 1
• getting familiar with App Player; Step 2
• installing Flipboard; Top 5 features you need to know about; The BlueStacks environment; 1-Click Sync; Cloud Connect.

• Apps from outside the app storeMac; PC; Tips for troubleshooting; People and places you should get to know; Official sites; Articles and tutorials; Community; Blogs; Twitter.

Get to grips with a new technology, understand what it is and what it can do for you, and then get to work with the most important features and tasks. A fast-paced, example-based approach guide for learning BlueStacks. This book is for anyone with a Mac or PC who wants to run Android apps on their computer. Whether you want to play games that are freely available for Android but not your computer, or you want to try apps before you install them on a physical device or use it as a development tool, this book will show you how. No previous experience is needed as this is written in plain English so that you can get going with BlueStacks quickly and easily.
(source: Nielsen Book Data)

• Front Cover; Copyright; Contents; Introduction; USB Essentials; How Data Travels on the Bus; Bus Speeds; Devices; Transfers; Transfer Types; How the Host Communicates with Devices; Device Classes; Learning about Attached Devices; USB Hosts for Embedded Systems; Embedded Hosts are Different; Dedicated Functions; The Targeted Peripheral List; Requirements; Switching Off Bus Power; Attach Detection Protocol; Session Request Protocol; Functioning as a USB Device; Necessary Hardware; System Processor; USB Host Controller; Root Hub; Host Connectors; Source of Bus Current; What the Host Does.

A guide for designing and programming small, embedded systems that access USB devices, this book includes topics such as how embedded USB hosts differ from USB hosts in PCs, choosing a hardware and programming platform for a project, understanding USB host programming in embedded Linux systems, how host applications can access USB devices of all types, and designing a system that can communicate with both USB hosts and USB devices. Example code explains how to read and write to files on drives, get user input from keyboards, communicate over virtual serial ports and Ethernet bridges, record an.

• High-Density Magnetic Data Storage
• Materials for High-Density Optical Data Storage
• Electrical Information Storage: Mechanism and Materials
• Nanoscale Data Storage.
• (source: Nielsen Book Data)

The explosive increase in information and the miniaturization of electronic devices demand new recording technologies and materials that combine high density, fast response, long retention time and rewriting capability. As predicted, the current silicon-based computer circuits are reaching their physical limits. Further miniaturization of the electronic components and increase in data storage density are vital for the next generation of IT equipment such as ultra high-speed mobile computing, communication devices and sophisticated sensors. This original book presents a comprehensive introduction to the significant research achievements on high-density data storage from the aspects of recording mechanisms, materials and fabrication technologies, which are promising for overcoming the physical limits of current data storage systems. The book serves as an useful guide for the development of optimized materials, technologies and device structures for future information storage, and will lead readers to the fascinating world of information technology in the future.
(source: Nielsen Book Data)

• Preface Acknowledgments Introduction The Virtual Memory Abstraction Implementing Virtual Memory: An Overview Modern VM Hardware Stack Modern VM Software Stack Virtual Memory, Coherence, and Consistency Heterogeneity and Virtualization Advanced VM Hardware Advanced VM Hardware-software Co-design Conclusion Bibliography Authors' Biographies.
• (source: Nielsen Book Data)

This book provides computer engineers, academic researchers, new graduate students, and seasoned practitioners an end-to-end overview of virtual memory. We begin with a recap of foundational concepts and discuss not only state-of-the-art virtual memory hardware and software support available today, but also emerging research trends in this space. The span of topics covers processor microarchitecture, memory systems, operating system design, and memory allocation. We show how efficient virtual memory implementations hinge on careful hardware and software cooperation, and we discuss new research directions aimed at addressing emerging problems in this space. Virtual memory is a classic computer science abstraction and one of the pillars of the computing revolution. It has long enabled hardware flexibility, software portability, and overall better security, to name just a few of its powerful benefits. Nearly all user-level programs today take for granted that they will have been freed from the burden of physical memory management by the hardware, the operating system, device drivers, and system libraries. However, despite its ubiquity in systems ranging from warehouse-scale datacenters to embedded Internet of Things (IoT) devices, the overheads of virtual memory are becoming a critical performance bottleneck today. Virtual memory architectures designed for individual CPUs or even individual cores are in many cases struggling to scale up and scale out to today's systems which now increasingly include exotic hardware accelerators (such as GPUs, FPGAs, or DSPs) and emerging memory technologies (such as non-volatile memory), and which run increasingly intensive workloads (such as virtualized and/or "big data" applications). As such, many of the fundamental abstractions and implementation approaches for virtual memory are being augmented, extended, or entirely rebuilt in order to ensure that virtual memory remains viable and performant in the years to come.
(source: Nielsen Book Data)

• Cover; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface;
• Chapter 1: Introducing Data Modeling; The relationship between MongoDB and NoSQL; Introducing NoSQL (Not Only SQL); NoSQL databases types; Dynamic schema, scalability, and redundancy; Database design and data modeling; The ANSI-SPARC architecture; The external level; The conceptual level; The internal level; Data modeling; The conceptual model; The logical model; The physical model; Summary;
• Chapter 2: Data Modeling with MongoDB

• Introducing documents and collectionsJSON; BSON; Characteristics of documents; The document size; Names and values for a field in a document; The document primary key; Support collections; The optimistic loop; Designing a document; Working with embedded documents; Working with references; Atomicity; Common document patterns; One-to-one; One-to-many; Many-to-many; Summary;
• Chapter 3: Querying Documents; Understanding the read operations; Selecting all documents; Selecting documents using criteria; Comparison operators; Logical operators; Element operators; Evaluation operators; Array operators

• ProjectionsIntroducing the write operations; Inserts; Updates; Write concerns; Unacknowledged; Acknowledged; Journaled; Replica acknowledged; Bulk writing documents; Summary;
• Chapter 4: Indexing; Indexing documents; Indexing a single field; Indexing more than one field; Indexing multi-key fields; Indexing for text search; Creating special indexes; Time to live indexes; Unique indexes; Sparse indexes; Summary;
• Chapter 5: Optimizing Queries; Understanding the query plan; Evaluating queries; Covering a query; The query optimizer; Reading from many MongoDB instances; Summary

• Chapter 6: Managing the DataOperational segregation; Giving priority to read operations; Capped collections; Data self-expiration; Summary;
• Chapter 7: Scaling; Scaling out MongoDB with sharding; Choosing the shard key; Basic concerns when choosing a shard key; Scaling a social inbox schema design; Fan out on read; Fan out on write; Fan out on write with buckets; Summary;
• Chapter 8: Logging and Real-time Analytics with MongoDB; Log data analysis; Error logs; Access logs; What we are looking for; Measuring the traffic on the web server; Designing the schema; Capturing an event request

• A one-document solutionTTL indexes; Sharding; Querying for reports; Summary; Index

This book is intended for database professionals, software developers, and architects who have some previous experience with MongoDB and now want to shift their focus to the concepts of data modeling. If you wish to develop better schema designs for MongoDB-based applications, this book is ideal for you.
(source: Nielsen Book Data)

• Basic use of local storage
• Viewing local storage content in a browser
• Basic demo for session storage
• Comparing local storage and session storage
• Using web storage instead of cookies
• Basic detection for storage support
• Improving detection using Modernizr
• Providing fallback support
• Is the user online or offline?
• Using a manifest for caching
• Providing support for the mobile platform
• Building a simple plugin using jQuery
• Adding objects, arrays, and TTL support to storage
• Storing images within local storage
• Adjusting the local storage space for browsers
• Building a stickies option for using in a browser
• Building a simple to-do list
• Using local storage in a form
• Using local storage in a CMS
• Using local storage to hide sign-up forms.

Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. Get the job done and learn as you go. Step-by-step recipes to get to grips with the LocalStorage standard. This book is great for those who maintain sites that use cookies in some form or another, and who want to learn how to take advantage of the LocalStorage standard to improve browser security and store more relevant information that can be retrieved at some point in the future, without recourse to the server.
(source: Nielsen Book Data)

• Preface
• The Usefulness of Collecting Data from Discharge Abstracts to Estimate Cancer Incidence
• Multiplexing Holograms for Data Page Storage
• Novel Metallic Nanocluster-Based Structures & Semiconductor Thin Films for Information Storage
• The Influence of Bias Against Target Culture on Motivation of Young Learners to Learn English: Some Thoughts on Data Collection
• Animal & Seasonal Effectors of Cow Behavior in Dairy Houses: An Observational Collection
• Swift Collection & Quantification of Cow Cervix Morphology Data: Validating a Practical Apparatus
• Activating Psychiatric Data in a Relational Database, Statistical Analyses & Case Study Mining
• Index.
• (source: Nielsen Book Data)

This book discusses data collection and storage across a broad spectrum of applications. Topics discussed include the collection of mortality data in relation to cancer prevalence and incidence to achieve successful national health policies; a study of the basic elements needed for a holographic data storage system; metallic nanocluster-based structures and semiconductor thin films for information storage and swift collection and quantification of cow cervix morphology data.
(source: Nielsen Book Data)

• Preface to the Fourth Edition . Symbols and Notation. Abbreviations. 1 Introduction. 1.1 The history of smart cards. 1.2 Card types and applications. 1.3 Standardization. 2 Card Types. 2.1 Embossed cards. 2.2 Magnetic-stripe cards. 2.3 Smart cards. 2.4 Optical memory cards. 3 Physical Properties. 3.1 Card formats. 3.2 Contact field. 3.3 Card body. 3.4 Card materials. 3.5 Card components and security features. 3.6 Chip modules. 4 Electrical Properties. 4.1 Electrical connections. 4.2 Supply voltage. 4.3 Supply current. 4.4 Clock supply. 4.5 Data transmission with T = 0 or T
• =1. 4.6 Activation and deactivation sequences. 5 Smart Card Microcontrollers. 5.1 Semiconductor technology. 5.2 Processor types. 5.3 Memory types. 5.4 Supplementary hardware. 5.5 Extended temperature range. 6 Information Technology Foundations. 6.1 Data structures. 6.2 Encoding alphanumeric data. 6.3 SDL notation. 6.4 State machines. 6.5 Error detection and correction codes. 6.6 Data compression. 7 Security Foundations. 7.1 Cryptology. 7.2 Hash functions. 7.3 Random numbers. 7.4 Authentication. 7.5 Digital signatures. 7.6 Certificates. 7.7 Key management. 7.8 Identification of persons. 8 Communication with Smart Cards. 8.1 Answer to reset (ATR). 8.2 Protocol Parameter Selection (PPS). 8.3 Message structure: APDUS. 8.4 Secure Data Transmission. 8.5 Logical channels. 8.6 Logical protocols. 8.7 Connecting terminals to higher-level systems. 9 Data Transmission with Contact Cards. 9.1 Physical transmission layer. 9.2 Memory card protocols. 9.3 ISO transmission protocols. 9.4 USB transmission protocol. 9.5 MMC transmission protocol. 9.6 Single-wire protocol (SWP). 10 Contactless Data Transmission. 10.1 Inductive coupling. 10.2 Power transmission. 10.3 Data transmission. 10.4 Capacitive coupling. 10.5 Collision avoidance. 10.6 State of standardization. 10.7 Close-coupling cards (ISO/IEC 10536). 10.8 Remote coupling cards. 10.9 Proximity cards (ISO/IEC 14443). 10.10 Vicinity integrated circuit cards (ISO/IEC 15693). 10.11 Near field communication (NFC). 10.12 FeliCa. 10.13 Mifare. 11 Smart Card Commands. 11.1 File selection commands. 11.2 Read and write commands. 11.3 Search commands. 11.4 File operation commands. 11.5 Commands for authenticating persons. 11.6 Commands for authenticating devices. 11.7 Commands for cryptographic algorithms. 11.8 File management commands. 11.9 Application management commands. 11.10 Completion commands. 11.11 Commands for hardware testing. 11.12 Commands for data transmission. 11.13 Database commands (SCQL). 11.14 Commands for electronic purses. 11.15 Commands for credit and debit cards. 11.16 Application-specific commands. 11.17 Command processing times. 12 Smart Card File Management. 12.1 File structure. 12.2 The life cycle of files. 12.3 File types. 12.4 Application files. 12.5 File names. 12.6 File selection. 12.7 EF file structures. 12.8 File access conditions. 12.9 File attributes. 13 Smart Card Operating Systems. 13.1 Evolution of smart card operating systems. 13.2 Fundamental aspects and tasks. 13.3 Command processing. 13.4 Design and implementation principles. 13.5 Operating system completion. 13.6 Memory organization and memory management. 13.7 File management. 13.8 Sequence control. 13.9 ISO/IEC 7816-9 resource access. 13.10 Atomic operations. 13.11 Multitasking. 13.12 Performance. 13.13 Application management with global platform. 13.14 Downloadable program code. 13.15 Executable native code. 13.16 Open platforms. 13.17 The small-OS smart card operating system. 14 Smart Card Production. 14.1 Tasks and roles in the production process. 14.2 The smart card life cycle. 14.3 Chip and module production. 14.4 Card Body production. 14.5 Combining the card body and the chip. 14.6 Electrical testing of modules. 14.7 Loading static data. 14.8 Loading individual data. 14.9 Envelope stuffing and dispatching. 14.10 Special types of production. 14.11 Termination of card usage. 15 Quality Assurance. 15.1 Card body tests. 15.2 Microcontroller hardware tests. 15.3 Test methods for contactless smart cards. 15.5 Evaluation of hardware and software. 16 Smart Card Security. 16.1 Classification of attacks and attackers. 16.2 A history of attacks. 16.3 Attacks and defense measures during development. 16.4 Attacks and defense measures during production. 16.5 Attacks and defense measures during card usage. 17 Smart Card Terminals. 17.1 Mechanical properties. 17.2 Electrical properties. 17.3 User interface. 17.4 Application interface. 17.5 Security. 18 Smart Cards in Payment Systems. 18.1 Payment transactions with cards. 18.2 Prepaid memory cards. 18.3 Electronic purses. 18.4 EMV Application. 18.5 PayPass and payWave. 18.6 The Eurocheque System in Germany. 19 Smart Cards in Telecommunication Systems. 19.1 Public card phones in Germany. 19.2 Telecommunication. 19.3 Overview of mobile telecommunication systems. 19.4 The GSM system. 19.5 The UMTS system. 19.6 The wireless identification module (WIM). 19.7 Microbrowsers. 20 Smart Cards in Health Care Systems. 20.1 Health insurance cards in Germany. 20.2 Electronic health care cards in Germany. 21 Smart Cards in Transportation Systems. 21.1 Electronic tickets. 21.2 Ski Passes. 21.3 Tachosmart. 21.4 Electronic toll systems. 22 Smart Cards for Identification and Passports. 22.1 FINEID personal ID card. 22.2 ICAO-compliant passports. 23 Smart Cards for IT Security. 23.1 Digital signatures. 23.2 Signature applications compliant with PKCS
• #15. 23.3 Smart Card Web Server (SCWS). 24 Application Design. 24.1 General information and characteristic data. 24.2 Application generation tools. 24.3 Analyzing an unknown smart card. 25 Appendix. 25.1 Glossary. 25.2 Related reading. 25.3 Bibliography. 25.4 Directory of standards and specifications. 25.5 Web addresses. Index.
• (source: Nielsen Book Data)

The most comprehensive book on state-of-the-art smart card technology available Updated with new international standards and specifications, this essential fourth edition now covers all aspects of smart card in a completely revised structure. Its enlarged coverage now includes smart cards for passports and ID cards, health care cards, smart cards for public transport, and Java Card 3.0. New sub-chapters cover near field communication (NFC), single wire protocol (SWP), and multi megabyte smart cards (microcontroller with NAND-Flash). There are also extensive revisions to chapters on smart card production, the security of smart cards (including coverage of new attacks and protection methods), and contactless card data transmission (ISO/IEC 10536, ISO/IEC 14443, ISO/IEC 15693). This edition also features: * additional views to the future development of smart cards, such as USB, MMU, SWP, HCI, Flash memory and their usage; * new internet technologies for smart cards; smart card web server, HTTP-Protocol, TCP/IP, SSL/TSL; * integration of the new flash-based microcontrollers for smart cards (until now the usual ROM-based microcontrollers), and; * a completely revised glossary with explanations of all important smart card subjects (600 glossary terms). Smart Card Handbook is firmly established as the definitive reference to every aspect of smart card technology, proving an invaluable resource for security systems development engineers. Professionals and microchip designers working in the smart card industry will continue to benefit from this essential guide. This book is also ideal for newcomers to the field. The Fraunhofer Smart Card Award was presented to the authors for the Smart Card Handbook, Third Edition in 2008.
(source: Nielsen Book Data)

• Introduction / M. Brian Blake, Liliana Cabral, Birgitta König-Ries, Ulrich Küster and David Martin
• Part 1. Results from the S3 Contest: OWL-S and SAWSDL Matchmaker Evaluation Tracks
• Overview of the S3 Contest: Performance Evaluation of Semantic Service Matchmakers / Matthias Klusch
• SeMa2: A Hybrid Semantic Service Matching Approach / N. Masuch, B. Hirsch, M. Burkhardt, A. Heßler and S. Albayrak
• OPOSSUM: Indexing Techniques for an Order-of-Magnitude Improvement of Service Matchmaking Times / Eran Toch
• Adaptive Hybrid Selection of Semantic Services: The iSeM Matchmaker / Patrick Kapahnke and Matthias Klusch
• SPARQLent: A SPARQL Based Intelligent Agent Performing Service Matchmaking / Marco Luca Sbodio
• Semantic Annotations and Web Service Retrieval: The URBE Approach / Pierluigi Plebani and Barbara Pernici
• SAWSDL Services Matchmaking Using SAWSDL-iMatcher / Dengping Wei and Abraham Bernstein
• Self-Adaptive Semantic Matchmaking Using COV4SWS. KOM and LOG4SWS. KOM / Ulrich Lampe and Stefan Schulte
• Part 2. Results from the S3 Contest: Cross Evaluation Track
• Overview of the Jena Geography Dataset Cross Evaluation / Ulrich Küster and Birgitta König-Ries
• Evaluation of Structured Collaborative Tagging for Web Service Matchmaking / Maciej Gawinecki, Giacomo Cabri, Marcin Paprzycki and Maria Ganzha
• Ontology Based Discovery of Semantic Web Services with IRS-III / Liliana Cabral and John Domingue
• Part 3. Results from the Semantic Web Service Challenge
• Overview of the Semantic Web Service Challenge / Liliana Cabral
• Loosely Coupled Information Models for Business Process Integration: Incorporating Rule-Based Semantic Bridges into BPEL / Nils Barnickel and Matthias Fluegge
• The XMDD Approach to the Semantic Web Services Challenge / Tiziana Margaria, Christian Kubczak and Bernhard Steffen
• Service Offer Discovery in the SWS Challenge Shipment Discovery Scenario / Maciej Zaremba, Tomas Vitvar, Raluca Zaharia and Sami Bhiri
• A Solution to the Logistics Management Scenario with the Glue2 Web Service Discovery Engine / Alessio Carenini, Dario Cerizza, Emanuele Della Valle, Andrea Turati and Marco Comerio, et al.
• The COSMO Solution to the SWS Challenge Mediation Problem Scenarios: An Evaluation / Camlon H. Asuncion, Marten van Sinderen and Dick Quartel
• Part 4. Results from the Web Services Challenge
• Overview of the Web Services Challenge (WSC): Discovery and Composition of SemanticWeb Services / Ajay Bansal, Srividya Bansal, M. Brian Blake, Steffen Bleul and Thomas Weise
• Effective QoS Aware Service Composition Based on Forward Chaining with Service Space Restriction / Peter Bartalos and Mária Bieliková
• Semantics-Based Web Service Composition Engine / Srividya K. Bansal, Ajay Bansal and Gopal Gupta
• Efficient Composition of Semantic Web Services with End-to-End QoS Optimization / Bin Xu and Sen Luo.

Over the last decade, a great amount of effort and resources have been invested in the development of Semantic Web Service (SWS) frameworks. Numerous description languages, frameworks, tools, and matchmaking and composition algorithms have been proposed. Nevertheless, when faced with a real-world problem, it is still very hard to decide which of these different approaches to use. In this book, the editors present an overall overview and comparison of the main current evaluation initiatives for SWS. The presentation is divided into four parts, each referring to one of the evaluation initiatives. Part I covers the long-established first two tracks of the Semantic Service Selection (S3) Contest - the OWL-S matchmaker evaluation and the SAWSDL matchmaker evaluation. Part II introduces the new S3 Jena Geography Dataset (JGD) cross evaluation contest. Part III presents the Semantic Web Service Challenge. Lastly, Part IV reports on the semantic aspects of the Web Service Challenge. The introduction to each part provides an overview of the evaluation initiative and overall results for its latest evaluation workshops. The following chapters in each part, written by the participants, detail their approaches, solutions and lessons learned. This book is aimed at two different types of readers. Researchers on SWS technology receive an overview of existing approaches in SWS with a particular focus on evaluation approaches; potential users of SWS technologies receive a comprehensive summary of the respective strengths and weaknesses of current systems and thus guidance on factors that play a role in evaluation.
(source: Nielsen Book Data)

• Part I - Foundations 1) From Web to Semantic Web (G. Hench) - 2) Semantic Web Services (Ch. Bussler) - 3) WSMO & WSML (M. Kerrigan, J. de Bruijn) Part II - SESA Environment 4) Introduction to Semantically Enabled Service-Oriented Architectures (M. Zaremba) - 5) SESA Middleware (M. Moran, T.Vitvar, Z. Yan, M. Zaremba) - 6) SESA Execution Semantics (M. Zaremba) Part III - SESA Services 7) Reasoning (U. Keller, N.Steinmetz) - 8) Discovery (H. Lausen) - 9) Selection (I. Toma) - 10) Mediation (E. Cimpian, A.Mocan) - 11) Storage and Internal Communication (O. Shafiq) Part IV - SESA Application and Compatible Systems 12) SESA Application (J. Viskova, T. Vitvar) - 13) Compatible and Related Systems (B. Norton, C.Pedrinaci) - 15) Conclusions and Outlook (M. Zaremba) References.
• (source: Nielsen Book Data)

In this book, Dieter Fensel and his qualified team lay the foundation for understanding the Semantic Web Services infrastructure, aimed at eliminating human intervention and thus allowing for seamless integration of information systems. They focus on the currently most advanced SWS infrastructure, namely SESA and related work such as the Web Services Execution Environment (WSMX) activities and the Semantic Execution Environment (OASIS SEE TC) standardization effort.
(source: Nielsen Book Data)

• Overview: On the Topic of Memory Systems and Their Design Part I: Cache Ch.
• 1. An Overview of Cache Principles
• 2. Logical Organization
• 3. Management of Cache Contents
• 4. Cache Cohenrence
• 5. Implementation Issues
• 6. Cache Case Studies Part II: DRAM
• 7. Memory Systems Overview
• 8. DRAM Device: Basic Circuits and Architecture
• 9. DRAM System Signalling and Timing
• 10. DRAM Memory System Organization
• 11. Generic DRAM Memory Access Protocol
• 12. Evolution of DRAM Devices
• 13. DRAM Memory Controller
• 14. Memory System Design Analysis Part II: Disk
• 15. Overview of Disks
• 16. The Physical Layer
• 17. The Data Layer
• 18. Performance Issues and Design Tradeoffs
• 19. Drive Interface
• 20. Operational Performance Improvement
• 21. The Cache Layer,
• 23. Performance Testing
• 24. Storage Subsystems
• 25. Advanced Topics
• 26. Case Study Part IV: Cross-Cutting Issues
• 27. The Holistic Design of Memory Hierarchies
• 28. Analysis of Cost and Performance
• 29. Power and Energy
• 30. Reliability
• 31. Virtual Memory.
• (source: Nielsen Book Data)

Is your memory hierarchy stopping your microprocessor from performing at the high level it should be? Memory Systems: Cache, DRAM, Disk shows you how to resolve this problem. The book tells you everything you need to know about the logical design and operation, physical design and operation, performance characteristics and resulting design trade-offs, and the energy consumption of modern memory hierarchies. You learn how to to tackle the challenging optimization problems that result from the side-effects that can appear at any point in the entire hierarchy. As a result you will be able to design and emulate the entire memory hierarchy.
(source: Nielsen Book Data)

Today's office assistants are so overwhelmed with the demands of multiple bosses, they need their own assistants! The Internet provides a motherload of resources and tools -- If you know how to use it! Cyber Assistant is a quick-reading guide to this amazing technology.
(source: Nielsen Book Data)