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Big Data Management.

By: García Márquez, Fausto Pedro.
Contributor(s): Lev, Benjamin.
Material type: TextTextSeries: eBooks on Demand.Publisher: Cham : Springer International Publishing, 2016Copyright date: ©2017Description: 1 online resource (274 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9783319454986.Subject(s): Big dataGenre/Form: Electronic books.Additional physical formats: Print version:: Big Data ManagementDDC classification: 650 LOC classification: HF4999.2-6182Online resources: Click here to view this ebook.
Contents:
Preface -- Contents -- About the Editors -- Visualizing Big Data: Everything Old Is New Again -- 1 Introduction -- 2 Case Study Data: Dominick's Finer Foods -- 3 Big Data: Pre-processing and Management -- 3.1 Data Pre-processing -- 3.2 Data Management -- 4 Big Data Visualization -- 4.1 Visualization Semiotics -- 4.2 Visualization of the DFF Database -- 5 Conclusions -- References -- Managing Cloud-Based Big Data Platforms: A Reference Architecture and Cost Perspective -- 1 Introduction -- 2 Big Data Processing in Cloud Environments -- 2.1 Generic Reference Architecture -- 2.2 Implementations of the Generic Reference Architecture -- 3 Cloud Pricing and Cost Perspective -- 3.1 Data Streams and Stream Processing -- 3.2 Data Storage -- 3.3 Hadoop Cluster -- 3.4 Data Warehouse -- 3.5 Machine Learning -- 4 Discussion -- 5 Conclusions and Outlook -- References -- 3 The Strategic Business Value of Big Data -- Abstract -- 1 Introduction -- 2 Strategic and Organizational Opportunities -- 2.1 Mapping a New Position on a Dynamic Industry -- 2.2 A Dynamic Organization in a Dynamic World -- 3 Big Data Strategies -- 4 Decision Making Under Big Data -- 5 Industry Applications -- 5.1 Retail -- 5.2 Manufacturing -- 5.3 Telecommunications -- 5.4 Public Sector Administration -- 5.5 Health Care -- 6 Conclusions -- References -- 4 A Review on Big Data Security and Privacy in Healthcare Applications -- Abstract -- 1 Big Data -- 1.1 Introduction -- 1.2 Big Data Technologies -- 2 E-Health or Medical and Health Informatics -- 2.1 Electronic Health Records (EHRs) -- 2.2 Social Health -- 3 Data Collection via Bio Informatics -- 3.1 Pharmacogenomics -- 4 Security and Privacy Issues in Big Data -- 4.1 Overview -- 4.2 Data Control -- 4.3 Instantaneous Security Analytics -- 4.4 Privacy-Maintaining Analytics -- 4.5 Data Leakage -- 4.5.1 Current Keys to Counter Data Leakage.
4.5.2 Data Leakage Prevention -- 4.6 Data Privacy -- 4.7 Data Sharing -- 5 Open Questions -- 5.1 Who Will Own the Collected Data? -- 5.2 Which Type of Data to Be Collected and What Will Be the Amount, of Data to Be Stored? -- 5.3 What Will Be the Storage Location? -- 5.4 To Whom Patient's Medical Record Should Be Visible? -- 5.5 Is Disclosure of Information Without Patient Permission Allowed? -- 6 Conclusions -- References -- What Is Big Data -- 1 What Is Big Data -- 1.1 Predictions by Use of Big Data -- 1.2 Big Data and Hypotheses -- 1.3 Big Data and Electric Power -- 2 Why We Need Big Data -- 3 The Example of Big Data -- 4 Conclusions -- References -- Big Data for Conversational Interfaces: Current Opportunities and Prospects -- 1 Introduction -- 2 Spoken Language Recognition -- 3 Spoken Language Understanding -- 4 Dialog Management -- 5 Natural Language Generation -- 6 Text-To-Speech Synthesis -- 7 User Modeling and Evaluation of the System -- 8 Future Research and Challenges -- 9 Conclusions -- References -- 7 Big Data Analytics in Telemedicine: A Role of Medical Image Compression -- Abstract -- 1 Telemedicine -- 1.1 Types of Telemedicine -- 1.1.1 Real-Time -- 1.1.2 Store-and-Forward -- 1.2 Applications of Telemedicine -- 1.3 Use of ICT in Medical Field -- 2 Telemedicine in Rural Area -- 2.1 Application to Rural Areas -- 2.2 Connectivity and Bandwidth in Rural Area -- 2.3 Role of Compression in Telemedicine -- 3 DICOM Format -- 4 Image Compression -- 5 Static Predictors -- 5.1 Differential Coding -- 5.2 Lossless JPEG -- 5.3 EDPCM -- 6 Dynamic Predictor -- 6.1 Median Edge Detector -- 6.2 Gradient Adjusted Predictor -- 6.3 Calic -- 6.4 JPEG Lossless -- 6.5 Edge Enhanced Predictor -- 6.6 GAP with Positive Error Modelling -- 7 Block Coding -- 7.1 Block Truncation Coding -- 7.2 Predictor with Block Coding.
7.3 Blocking Coding with Variable Block Size -- 7.4 Multidimensional Scanning of Blocks -- 8 Symmetry Based Compression -- 8.1 Concept of Symmetry -- 9 Volumetric Image Compression -- 10 Conclusion -- References -- 8 A Bundle-Like Algorithm for Big Data Network Design with Risk-Averse Signal Control Optimization -- Abstract -- 1 Introduction -- 2 A Big Data Network Design Model -- 2.1 Notation -- 2.2 The Lower Level Problem -- 2.2.1 The Cost Minimum (CM) Model -- 2.2.2 A Maximum Risk Model (MM) -- 2.2.3 A Maximum Risk Model with Mixed Routes (MM2) -- 2.2.4 A Weighted Sum Risk Equilibrium Model (WSM) -- 2.3 The Upper Level Problem -- 3 A Bundle-like Method -- 3.1 A Cutting Plane (CP) Model -- 3.2 A Proximal Bundle Method (PBM) -- 3.3 A Bounding Strategy -- 4 Numerical Computations -- 4.1 The First Example Road Network -- 4.2 The Second Example Road Network -- 4.3 The Third Example Road Network -- 5 Conclusions and Discussions -- Acknowledgements -- References -- 9 Evaluation of Evacuation Corridors and Traffic Big Data Management Strategies for Short-Notice Evacuation -- Abstract -- 1 Introduction -- 2 Literature Review -- 2.1 Contra-Flow Operation -- 2.2 Demand Loading and Staging Strategy -- 2.3 Traffic Simulation Tool DynusT -- 3 Background Description for Simulation -- 4 Evacuation Trip Demand Modeling -- 4.1 Background and Evacuation Trip Demands -- 4.2 Evacuation Trip Production and Attraction -- 4.3 Evacuation Trip Distribution and O-D Demand Table -- 5 Traffic Management Strategy Development -- 5.1 Baseline Traffic Management -- 5.2 Advanced Traffic Management Strategies -- 5.3 Traffic Signal Consideration -- 5.4 Simulation Calibration -- 6 Simulation Results and Analysis -- 6.1 Step 1-Evacuation Corridor Selection -- 6.2 Step 2-Traffic Management Strategy Evaluation -- 6.3 Step 3-Evacuation Demand Staging Effect -- 7 Conclusions.
Acknowledgments -- References -- 10 Analyzing Network Log Files Using Big Data Techniques -- Abstract -- 1 Introduction -- 2 Big Data State-of-the-Art -- 2.1 The Hadoop Framework -- 3 Problem Description -- 3.1 Modeling the WiFi Log System -- 3.2 Solution Achieved -- 3.2.1 Layered Viewpoint -- 3.2.2 Application Behavior Viewpoint -- 4 Project Development -- 4.1 Working Methodology -- 5 Results -- 5.1 Cluster Configuration -- 5.2 Sample Results for Each Task -- 5.3 Dashboards Using R Charts and Data from Counters -- 5.4 Graphical Results -- 6 Conclusions -- Acknowledgments -- References -- 11 Big Data and Earned Value Management in Airspace Industry -- Abstract -- 1 Introduction -- 2 Big Data -- 3 Earned Value Management -- 4 EVM Extensions -- 5 Big Data and Earned Value Management -- 6 Conclusions -- References.
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Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
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HF4999.2-6182 (Browse shelf) http://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=4744612 Available EBC4744612

Preface -- Contents -- About the Editors -- Visualizing Big Data: Everything Old Is New Again -- 1 Introduction -- 2 Case Study Data: Dominick's Finer Foods -- 3 Big Data: Pre-processing and Management -- 3.1 Data Pre-processing -- 3.2 Data Management -- 4 Big Data Visualization -- 4.1 Visualization Semiotics -- 4.2 Visualization of the DFF Database -- 5 Conclusions -- References -- Managing Cloud-Based Big Data Platforms: A Reference Architecture and Cost Perspective -- 1 Introduction -- 2 Big Data Processing in Cloud Environments -- 2.1 Generic Reference Architecture -- 2.2 Implementations of the Generic Reference Architecture -- 3 Cloud Pricing and Cost Perspective -- 3.1 Data Streams and Stream Processing -- 3.2 Data Storage -- 3.3 Hadoop Cluster -- 3.4 Data Warehouse -- 3.5 Machine Learning -- 4 Discussion -- 5 Conclusions and Outlook -- References -- 3 The Strategic Business Value of Big Data -- Abstract -- 1 Introduction -- 2 Strategic and Organizational Opportunities -- 2.1 Mapping a New Position on a Dynamic Industry -- 2.2 A Dynamic Organization in a Dynamic World -- 3 Big Data Strategies -- 4 Decision Making Under Big Data -- 5 Industry Applications -- 5.1 Retail -- 5.2 Manufacturing -- 5.3 Telecommunications -- 5.4 Public Sector Administration -- 5.5 Health Care -- 6 Conclusions -- References -- 4 A Review on Big Data Security and Privacy in Healthcare Applications -- Abstract -- 1 Big Data -- 1.1 Introduction -- 1.2 Big Data Technologies -- 2 E-Health or Medical and Health Informatics -- 2.1 Electronic Health Records (EHRs) -- 2.2 Social Health -- 3 Data Collection via Bio Informatics -- 3.1 Pharmacogenomics -- 4 Security and Privacy Issues in Big Data -- 4.1 Overview -- 4.2 Data Control -- 4.3 Instantaneous Security Analytics -- 4.4 Privacy-Maintaining Analytics -- 4.5 Data Leakage -- 4.5.1 Current Keys to Counter Data Leakage.

4.5.2 Data Leakage Prevention -- 4.6 Data Privacy -- 4.7 Data Sharing -- 5 Open Questions -- 5.1 Who Will Own the Collected Data? -- 5.2 Which Type of Data to Be Collected and What Will Be the Amount, of Data to Be Stored? -- 5.3 What Will Be the Storage Location? -- 5.4 To Whom Patient's Medical Record Should Be Visible? -- 5.5 Is Disclosure of Information Without Patient Permission Allowed? -- 6 Conclusions -- References -- What Is Big Data -- 1 What Is Big Data -- 1.1 Predictions by Use of Big Data -- 1.2 Big Data and Hypotheses -- 1.3 Big Data and Electric Power -- 2 Why We Need Big Data -- 3 The Example of Big Data -- 4 Conclusions -- References -- Big Data for Conversational Interfaces: Current Opportunities and Prospects -- 1 Introduction -- 2 Spoken Language Recognition -- 3 Spoken Language Understanding -- 4 Dialog Management -- 5 Natural Language Generation -- 6 Text-To-Speech Synthesis -- 7 User Modeling and Evaluation of the System -- 8 Future Research and Challenges -- 9 Conclusions -- References -- 7 Big Data Analytics in Telemedicine: A Role of Medical Image Compression -- Abstract -- 1 Telemedicine -- 1.1 Types of Telemedicine -- 1.1.1 Real-Time -- 1.1.2 Store-and-Forward -- 1.2 Applications of Telemedicine -- 1.3 Use of ICT in Medical Field -- 2 Telemedicine in Rural Area -- 2.1 Application to Rural Areas -- 2.2 Connectivity and Bandwidth in Rural Area -- 2.3 Role of Compression in Telemedicine -- 3 DICOM Format -- 4 Image Compression -- 5 Static Predictors -- 5.1 Differential Coding -- 5.2 Lossless JPEG -- 5.3 EDPCM -- 6 Dynamic Predictor -- 6.1 Median Edge Detector -- 6.2 Gradient Adjusted Predictor -- 6.3 Calic -- 6.4 JPEG Lossless -- 6.5 Edge Enhanced Predictor -- 6.6 GAP with Positive Error Modelling -- 7 Block Coding -- 7.1 Block Truncation Coding -- 7.2 Predictor with Block Coding.

7.3 Blocking Coding with Variable Block Size -- 7.4 Multidimensional Scanning of Blocks -- 8 Symmetry Based Compression -- 8.1 Concept of Symmetry -- 9 Volumetric Image Compression -- 10 Conclusion -- References -- 8 A Bundle-Like Algorithm for Big Data Network Design with Risk-Averse Signal Control Optimization -- Abstract -- 1 Introduction -- 2 A Big Data Network Design Model -- 2.1 Notation -- 2.2 The Lower Level Problem -- 2.2.1 The Cost Minimum (CM) Model -- 2.2.2 A Maximum Risk Model (MM) -- 2.2.3 A Maximum Risk Model with Mixed Routes (MM2) -- 2.2.4 A Weighted Sum Risk Equilibrium Model (WSM) -- 2.3 The Upper Level Problem -- 3 A Bundle-like Method -- 3.1 A Cutting Plane (CP) Model -- 3.2 A Proximal Bundle Method (PBM) -- 3.3 A Bounding Strategy -- 4 Numerical Computations -- 4.1 The First Example Road Network -- 4.2 The Second Example Road Network -- 4.3 The Third Example Road Network -- 5 Conclusions and Discussions -- Acknowledgements -- References -- 9 Evaluation of Evacuation Corridors and Traffic Big Data Management Strategies for Short-Notice Evacuation -- Abstract -- 1 Introduction -- 2 Literature Review -- 2.1 Contra-Flow Operation -- 2.2 Demand Loading and Staging Strategy -- 2.3 Traffic Simulation Tool DynusT -- 3 Background Description for Simulation -- 4 Evacuation Trip Demand Modeling -- 4.1 Background and Evacuation Trip Demands -- 4.2 Evacuation Trip Production and Attraction -- 4.3 Evacuation Trip Distribution and O-D Demand Table -- 5 Traffic Management Strategy Development -- 5.1 Baseline Traffic Management -- 5.2 Advanced Traffic Management Strategies -- 5.3 Traffic Signal Consideration -- 5.4 Simulation Calibration -- 6 Simulation Results and Analysis -- 6.1 Step 1-Evacuation Corridor Selection -- 6.2 Step 2-Traffic Management Strategy Evaluation -- 6.3 Step 3-Evacuation Demand Staging Effect -- 7 Conclusions.

Acknowledgments -- References -- 10 Analyzing Network Log Files Using Big Data Techniques -- Abstract -- 1 Introduction -- 2 Big Data State-of-the-Art -- 2.1 The Hadoop Framework -- 3 Problem Description -- 3.1 Modeling the WiFi Log System -- 3.2 Solution Achieved -- 3.2.1 Layered Viewpoint -- 3.2.2 Application Behavior Viewpoint -- 4 Project Development -- 4.1 Working Methodology -- 5 Results -- 5.1 Cluster Configuration -- 5.2 Sample Results for Each Task -- 5.3 Dashboards Using R Charts and Data from Counters -- 5.4 Graphical Results -- 6 Conclusions -- Acknowledgments -- References -- 11 Big Data and Earned Value Management in Airspace Industry -- Abstract -- 1 Introduction -- 2 Big Data -- 3 Earned Value Management -- 4 EVM Extensions -- 5 Big Data and Earned Value Management -- 6 Conclusions -- References.

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Author notes provided by Syndetics

<p>Dr. Fausto Pedro García Márquez completed his European Doctorate in Engineering at the University of Castilla-La Mancha (UCLM) in 2004. He received his Engineering degree from the University of Murcia, Spain in 1998, and his Technical Engineering degree at UCLM in 1995 and degree in Business Administration and Management at UCLM in 2006. He has also served as Technician in Labor Risk Prevention by UCLM (2000) and Transport Specialist at the Polytechnic University of Madrid, Spain (2001). He was a Senior Manager at Accenture in 2013/2014, and is currently a Senior Lecturer (Full Professor accredited) at UCLM, an Honorary Senior Research Fellow at the University of Birmingham, UK, a Lecturer at the Instituto Europeo de Postgrado and Director of the Ingenium Research Group. He has been the principal investigator in 3 European Projects and 60 national and corporate research projects. He holds international and national patents, and has authored more than 110 international papers and 10 books. His work has been recognized with 3 International Awards in Engineering Management and Management Science. </p> <p>Dr. Benjamin Lev is a Professor and Head of Decision Sciences at LeBow College of Business. He holds a PhD in Operations Research from Case Western Reserve University. Prior to joining Drexel University, Dr. Lev held academic and administrative positions at Temple University, the University of Michigan-Dearborn and Worcester Polytechnic Institute. He is the Editor-in-Chief of OMEGA - The International journal of Management Science, the Co-Editor-in-Chief of the International Journal of Management Science and Engineering Management, and serves on several other journal editorial boards. He has published over ten books and numerous articles, and has organized many national and international conferences.</p>

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