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Big-Data Analytics and Cloud Computing

E-BookPDF1 - PDF WatermarkE-Book
169 Seiten
Englisch
Springer International Publishingerschienen am12.01.20161st ed. 2015
This book reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures; examines the applications and implementations that utilize big data in cloud architectures; surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions; identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches; provides relevant theoretical frameworks, empirical research findings, and numerous case studies; discusses real-world applications of algorithms and techniques to address the challenges of big datasets.



The editors are all members of the Computing and Mathematics Department at the University of Derby, UK, where Dr. Marcello Trovati serves as a Senior Lecturer in Mathematics, Dr. Richard Hill as a Professor and Head of the Computing and Mathematics Department, Dr. Ashiq Anjum as a Professor of Distributed Computing, Dr. Shao Ying Zhu as a Senior Lecturer in Computing, and Dr. Lu Liu as a Professor of Distributed Computing. The other publications of the editors include the Springer titles Guide to Security Assurance for Cloud Computing, Guide to Cloud Computing and Cloud Computing for Enterprise Architectures.mehr
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E-BookPDF1 - PDF WatermarkE-Book
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Produkt

KlappentextThis book reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures; examines the applications and implementations that utilize big data in cloud architectures; surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions; identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches; provides relevant theoretical frameworks, empirical research findings, and numerous case studies; discusses real-world applications of algorithms and techniques to address the challenges of big datasets.



The editors are all members of the Computing and Mathematics Department at the University of Derby, UK, where Dr. Marcello Trovati serves as a Senior Lecturer in Mathematics, Dr. Richard Hill as a Professor and Head of the Computing and Mathematics Department, Dr. Ashiq Anjum as a Professor of Distributed Computing, Dr. Shao Ying Zhu as a Senior Lecturer in Computing, and Dr. Lu Liu as a Professor of Distributed Computing. The other publications of the editors include the Springer titles Guide to Security Assurance for Cloud Computing, Guide to Cloud Computing and Cloud Computing for Enterprise Architectures.
Details
Weitere ISBN/GTIN9783319253138
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2016
Erscheinungsdatum12.01.2016
Auflage1st ed. 2015
Seiten169 Seiten
SpracheEnglisch
IllustrationenXVI, 169 p. 67 illus. in color.
Artikel-Nr.1881611
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
1;Foreword;6
2;Preface;8
2.1;Overview and Goals;8
2.2;Organisation and Features;9
2.3;Target Audiences;9
2.4;Suggested Uses;9
3;Acknowledgements;12
4;Contents;14
5;Contributors;16
6;Part I Theory;18
6.1;1 Data Quality Monitoring of Cloud Databases Based on Data Quality SLAs;19
6.1.1;1.1 Introduction and Summary;19
6.1.2;1.2 Background;21
6.1.2.1;1.2.1 Data Quality in the Context of Big Data;21
6.1.2.2;1.2.2 Cloud Computing;22
6.1.2.3;1.2.3 Data Quality Monitoring in the Cloud;24
6.1.2.4;1.2.4 The Challenge of Specifying a DQSLA;24
6.1.2.5;1.2.5 The Infrastructure Estimation Problem;25
6.1.3;1.3 Proposed Solutions;26
6.1.3.1;1.3.1 Data Quality SLA Formalization;26
6.1.3.2;1.3.2 Examples of Data Quality SLAs;27
6.1.3.3;1.3.3 Data Quality-Aware Service Architecture;29
6.1.4;1.4 Future Research Directions;31
6.1.5;1.5 Conclusions;35
6.1.6;References;35
6.2;2 Role and Importance of Semantic Search in Big Data Governance;37
6.2.1;2.1 Introduction;37
6.2.2;2.2 Big Data: Promises and Challenges;38
6.2.3;2.3 Participatory Design for Big Data;39
6.2.4;2.4 Self-Service Discovery;42
6.2.5;2.5 Conclusion;49
6.2.6;References;51
6.3;3 Multimedia Big Data: Content Analysis and Retrieval;52
6.3.1;3.1 Introduction;52
6.3.2;3.2 The MapReduce Framework and Multimedia Big Data;54
6.3.2.1;3.2.1 Indexing;55
6.3.2.2;3.2.2 Caveats on Indexing;57
6.3.2.3;3.2.3 Multiple Multimedia Processing;57
6.3.2.4;3.2.4 Additional Work Required?;59
6.3.3;3.3 Deep Learning and Multimedia Data;60
6.3.4;3.4 Conclusions;64
6.3.5;References;64
6.4;4 An Overview of Some Theoretical Topological Aspects of Big Data;67
6.4.1;4.1 Introduction;67
6.4.2;4.2 Representation of Data;68
6.4.3;4.3 Homology Theory;70
6.4.3.1;4.3.1 Simplicial Complexes;70
6.4.3.2;4.3.2 Voronoi Diagrams and Delaunay Triangulations;72
6.4.3.3;4.3.3 Vietoris and ?ech Complexes;73
6.4.3.4;4.3.4 Graph-Induced Complexes;74
6.4.3.5;4.3.5 Chains;74
6.4.4;4.4 Network Theory for Big Data;75
6.4.4.1;4.4.1 Scale-Free, Small-World and Random Networks;75
6.4.5;4.5 Conclusions;78
6.4.6;References;78
7;Part II Applications;79
7.1;5 Integrating Twitter Traffic Information with Kalman Filter Models for Public Transportation Vehicle Arrival Time Prediction;80
7.1.1;5.1 Introduction;80
7.1.2;5.2 Communication Platform on Twitter;82
7.1.3;5.3 Communication for Data Collection on Twitter;82
7.1.4;5.4 Event Detection and Analysis: Tweets Relating to Road Incidents;83
7.1.4.1;5.4.1 Twitter Data: Incident Data Set;84
7.1.5;5.5 Methodology;87
7.1.5.1;5.5.1 Time Series and Temporal Analysis of Textual Twitter;87
7.1.6;5.6 Proposed Refined Kalman Filter (KF) Model-Based System;91
7.1.7;5.7 Conclusion;94
7.1.8;References;94
7.2;6 Data Science and Big Data Analytics at Career Builder;96
7.2.1;6.1 Carotene: A Job Title Classification System;96
7.2.1.1;6.1.1 Occupation Taxonomies;98
7.2.1.2;6.1.2 The Architecture of Carotene;99
7.2.1.2.1;6.1.2.1 Taxonomy Discovery Using Clustering;100
7.2.1.2.2;6.1.2.2 Coarse-Level Classification: SOC Major Classifier;101
7.2.1.2.3;6.1.2.3 Fine-Level Classification: Proximity-Based Classifier;101
7.2.1.3;6.1.3 Experimental Results and Discussion;102
7.2.2;6.2 CARBi: A Data Science Ecosystem;103
7.2.2.1;6.2.1 Accessing CB Data and Services Using WebScalding;103
7.2.2.2;6.2.2 ScriptDB: Managing Hadoop Jobs;106
7.2.3;References;108
7.3;7 Extraction of Bayesian Networks from Large Unstructured Datasets;110
7.3.1;7.1 Introduction;110
7.3.2;7.2 Text Mining;111
7.3.2.1;7.2.1 Text Mining Techniques;112
7.3.2.2;7.2.2 General Architecture and Various Components of Text Mining;113
7.3.2.3;7.2.3 Lexical Analysis;113
7.3.2.4;7.2.4 Part-of-Speech Tagging;114
7.3.2.5;7.2.5 Parsing;114
7.3.2.6;7.2.6 Named Entity Recognition;115
7.3.2.7;7.2.7 Named Entity Recognition;115
7.3.2.8;7.2.8 Concept Extraction;115
7.3.2.9;7.2.9 Sentiment Analysis;116
7.3.3;7.3 The Automatic Extraction of Bayesian Networks from Text;116
7.3.3.1;7.3.1 Dependence Relation Extraction from Text;117
7.3.3.2;7.3.2 Variable Identification;118
7.3.3.3;7.3.3 BN Structure Definition;118
7.3.3.4;7.3.4 Probability Information Extraction;118
7.3.3.5;7.3.5 Probability Information Extraction;119
7.3.3.6;7.3.6 General Architecture;120
7.3.4;7.4 Conclusions;121
7.3.5;References;121
7.4;8 Two Case Studies Based on Large Unstructured Sets;123
7.4.1;8.1 Introduction;123
7.4.2;8.2 Case Study 1: Computational Objectivity in the PHQ-9 Depression Assessment;124
7.4.2.1;8.2.1 Reliability and Validity Issues of the PHQ-9;124
7.4.2.2;8.2.2 Analytic Hierarchy Process: Defining a Weighting System;126
7.4.2.2.1;8.2.2.1 PHQ-9 Analysis via the Analytic Hierarchy Process;127
7.4.2.2.2;8.2.2.2 Advantages of AHP;127
7.4.2.3;8.2.3 A Text Mining Approach;127
7.4.3;8.3 Case Study 2: Evaluation of Probabilistic Information Extraction from Large Unstructured Datasets;130
7.4.3.1;8.3.1 Description of the Method;131
7.4.3.1.1;8.3.1.1 Description of Text and Data Patterns;131
7.4.3.2;8.3.2 Network Extraction Method;132
7.4.3.3;8.3.3 Description of Datasets;132
7.4.3.4;8.3.4 Evaluation;133
7.4.4;8.4 Conclusion;136
7.4.5;References;136
7.5;9 Information Extraction from Unstructured Data Sets: An Application to Cardiac Arrhythmia Detection;138
7.5.1;9.1 Introduction;138
7.5.2;9.2 Background;139
7.5.3;9.3 Automated Extraction of Fuzzy Partition Rules from Text;140
7.5.3.1;9.3.1 Text Mining Extraction Results;142
7.5.4;9.4 Data Preparation;142
7.5.4.1;9.4.1 Feature Selection;144
7.5.5;9.5 Fuzzy Partition Design;144
7.5.5.1;9.5.1 Criteria for the Evaluation of Fuzzy Partitions;147
7.5.6;9.6 Rule Base Generation;151
7.5.6.1;9.6.1 Knowledge Base Accuracy;151
7.5.7;9.7 Evaluation;152
7.5.8;9.8 Conclusion;154
7.5.9;References;154
7.6;10 A Platform for Analytics on Social Networks Derived from Organisational Calendar Data;157
7.6.1;10.1 Introduction;157
7.6.2;10.2 Literature Review/Related Work;158
7.6.2.1;10.2.1 Social Capital and the Exchange of Knowledge/Resources;158
7.6.2.2;10.2.2 Social Capital and the Exchange of Knowledge/Resources;159
7.6.2.3;10.2.3 Repurposing Redundant Organisational Data;160
7.6.2.4;10.2.4 Graph Databases;160
7.6.3;10.3 Proposed Platform;161
7.6.3.1;10.3.1 Capture: Capturing the Calendar Data;161
7.6.3.2;10.3.2 Process: Processing the Captured Data into Social Data;162
7.6.3.3;10.3.3 Build: Building the Social Network;164
7.6.3.4;10.3.4 Visualise: Visualising the Social Network Structure;166
7.6.3.5;10.3.5 Analyse: Performing Analysis Against the Social Network;167
7.6.3.6;10.3.6 Experimental Setup;167
7.6.3.7;10.3.7 Solution Setup;168
7.6.3.8;10.3.8 Hardware Setup;170
7.6.4;10.4 Results;170
7.6.4.1;10.4.1 Outlier Detection;170
7.6.4.2;10.4.2 Detection of Outlying Groups;170
7.6.4.3;10.4.3 Identification of Key Communicators for Specific Groups and Highly Connected Individuals;171
7.6.4.4;10.4.4 Frequency of Interaction;172
7.6.4.5;10.4.5 Experiment Data Statistics;172
7.6.5;10.5 Conclusions;174
7.6.6;References;174
8;Index;176mehr

Autor

The editors are all members of the Computing and Mathematics Department at the University of Derby, UK, where Dr. Marcello Trovati serves as a Senior Lecturer in Mathematics, Dr. Richard Hill as a Professor and Head of the Computing and Mathematics Department, Dr. Ashiq Anjum as a Professor of Distributed Computing, Dr. Shao Ying Zhu as a Senior Lecturer in Computing, and Dr. Lu Liu as a Professor of Distributed Computing. The other publications of the editors include the Springer titles Guide to Security Assurance for Cloud Computing, Guide to Cloud Computing and Cloud Computing for Enterprise Architectures.

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