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Principles of Big Data:

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Bibliographische Detailangaben
1. Verfasser:	Luna, Alvin Albuero De
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Ashland : Arcler Press, 2020.
Schlagworte:	Big data. Database management. Données volumineuses. Bases de données > Gestion. Big data Database management
Online-Zugang:	Volltext
Beschreibung:	7.4. Step Wise Approach In Analysis of Big Data.
Beschreibung:	1 online resource (200 pages)
ISBN:	1774078147 9781774078143

Internformat

MARC


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505	0		\|a Cover -- Title Page -- Copyright -- ABOUT THE AUTHOR -- TABLE OF CONTENTS -- List of Abbreviations -- Preface -- Chapter 1 Introduction to Big Data -- 1.1. Introduction -- 1.2. Concept of Big Data -- 1.3. What is Data? -- 1.4. What is Big Data? -- 1.5. The Big Data Systems are Different -- 1.6. Big Data Analytics -- 1.7. Case Study: German Telecom Company -- 1.8. Checkpoints -- Chapter 2 Identifier Systems -- 2.1. Meaning Of Identifier System -- 2.2. Features Of An Identifier System -- 2.3. Database Identifiers -- 2.4. Classes Of Identifiers -- 2.5. Rules For Regular Identifiers
505	8		\|a 2.6. One-Way Hash Function -- 2.7. De-Identification And Data Scrubbing -- 2.8. Concept Of De-Identification -- 2.9. The Process Of De-Identifications -- 2.10. Techniques Of De-Identification -- 2.11. Assessing The Risk Of Re-Identification -- 2.12. Case Study: Mastercard: Applying Social Media Research Insights For Better Business Decisions -- 2.13. Checkpoints -- Chapter 3 Improving the Quality of Big Data and Its Measurement -- 3.1. Data Scrubbing -- 3.2. Meaning of Bad Data -- 3.3. Common Approaches to Improve Data Quality -- 3.4. Measuring Big Data -- 3.5. How To Measure Big Data
505	8		\|a 3.6. Measuring Big Data Roi: A Sign of Data Maturity -- 3.7. The Interplay Of Hard And Soft Benefits -- 3.8. When Big Data Projects Require Big Investments -- 3.9. Real-Time, Real-World Roi -- 3.10. Case Study 2: Southwest Airlines: Big Data Pr Analysis Aids on-Time Performance -- 3.11. Checkpoints -- Chapter 4 Ontologies -- Introduction -- 4.1. Concept of Ontologies -- 4.2. Relation of Ontologies To Big Data Trend -- 4.3. Advantages And Limitations of Ontologies -- 4.4. Why Are Ontologies Developed? -- 4.5. Semantic Web -- 4.6. Major Components of Semantic Web -- 4.7. Checkpoints
505	8		\|a Chapter 5 Data Integration and Interoperability -- 5.1. What Is Data Integration? -- 5.2. Data Integration Areas -- 5.3. Types of Data Integration -- 5.4. Challenges of Data Integration and Interoperability in Big Data -- 5.5. Challenges of Big Data Integration And Interoperability -- 5.6. Immutability And Immortality -- 5.7. Data Types and Data Objects -- 5.8. Legacy Data -- 5.9. Data Born From Data -- 5.10. Reconciling Identifiers Across Institutions -- 5.11. Simple But Powerful Business Data Techniques -- 5.12. Association Rule Learning (ARL) -- 5.13. Classification Tree Analysis
505	8		\|a 5.14. Checkpoints -- Chapter 6 Clustering, Classification, and Reduction -- Introduction -- 6.1. Logistic Regression (Predictive Learning Model) -- 6.2. Clustering Algorithms -- 6.3. Data Reduction Strategies -- 6.4. Data Reduction Methods -- 6.5. Data Visualization: Data Reduction For Everyone -- 6.6. Case Study: Coca-Cola Enterprises (CCE) Case Study: The Thirst For Hr Analytics Grows -- 6.5. Checkpoints -- Chapter 7 Key Considerations in Big Data Analysis -- Introduction -- 7.1. Major Considerations For Big Data And Analytics -- 7.2. Overfitting -- 7.3. Bigness Bias
500			\|a 7.4. Step Wise Approach In Analysis of Big Data.
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contents	Cover -- Title Page -- Copyright -- ABOUT THE AUTHOR -- TABLE OF CONTENTS -- List of Abbreviations -- Preface -- Chapter 1 Introduction to Big Data -- 1.1. Introduction -- 1.2. Concept of Big Data -- 1.3. What is Data? -- 1.4. What is Big Data? -- 1.5. The Big Data Systems are Different -- 1.6. Big Data Analytics -- 1.7. Case Study: German Telecom Company -- 1.8. Checkpoints -- Chapter 2 Identifier Systems -- 2.1. Meaning Of Identifier System -- 2.2. Features Of An Identifier System -- 2.3. Database Identifiers -- 2.4. Classes Of Identifiers -- 2.5. Rules For Regular Identifiers 2.6. One-Way Hash Function -- 2.7. De-Identification And Data Scrubbing -- 2.8. Concept Of De-Identification -- 2.9. The Process Of De-Identifications -- 2.10. Techniques Of De-Identification -- 2.11. Assessing The Risk Of Re-Identification -- 2.12. Case Study: Mastercard: Applying Social Media Research Insights For Better Business Decisions -- 2.13. Checkpoints -- Chapter 3 Improving the Quality of Big Data and Its Measurement -- 3.1. Data Scrubbing -- 3.2. Meaning of Bad Data -- 3.3. Common Approaches to Improve Data Quality -- 3.4. Measuring Big Data -- 3.5. How To Measure Big Data 3.6. Measuring Big Data Roi: A Sign of Data Maturity -- 3.7. The Interplay Of Hard And Soft Benefits -- 3.8. When Big Data Projects Require Big Investments -- 3.9. Real-Time, Real-World Roi -- 3.10. Case Study 2: Southwest Airlines: Big Data Pr Analysis Aids on-Time Performance -- 3.11. Checkpoints -- Chapter 4 Ontologies -- Introduction -- 4.1. Concept of Ontologies -- 4.2. Relation of Ontologies To Big Data Trend -- 4.3. Advantages And Limitations of Ontologies -- 4.4. Why Are Ontologies Developed? -- 4.5. Semantic Web -- 4.6. Major Components of Semantic Web -- 4.7. Checkpoints Chapter 5 Data Integration and Interoperability -- 5.1. What Is Data Integration? -- 5.2. Data Integration Areas -- 5.3. Types of Data Integration -- 5.4. Challenges of Data Integration and Interoperability in Big Data -- 5.5. Challenges of Big Data Integration And Interoperability -- 5.6. Immutability And Immortality -- 5.7. Data Types and Data Objects -- 5.8. Legacy Data -- 5.9. Data Born From Data -- 5.10. Reconciling Identifiers Across Institutions -- 5.11. Simple But Powerful Business Data Techniques -- 5.12. Association Rule Learning (ARL) -- 5.13. Classification Tree Analysis 5.14. Checkpoints -- Chapter 6 Clustering, Classification, and Reduction -- Introduction -- 6.1. Logistic Regression (Predictive Learning Model) -- 6.2. Clustering Algorithms -- 6.3. Data Reduction Strategies -- 6.4. Data Reduction Methods -- 6.5. Data Visualization: Data Reduction For Everyone -- 6.6. Case Study: Coca-Cola Enterprises (CCE) Case Study: The Thirst For Hr Analytics Grows -- 6.5. Checkpoints -- Chapter 7 Key Considerations in Big Data Analysis -- Introduction -- 7.1. Major Considerations For Big Data And Analytics -- 7.2. Overfitting -- 7.3. Bigness Bias
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spelling	Luna, Alvin Albuero De. Principles of Big Data [electronic resource] / Alvin Albuero De Luna. Ashland : Arcler Press, 2020. 1 online resource (200 pages) Cover -- Title Page -- Copyright -- ABOUT THE AUTHOR -- TABLE OF CONTENTS -- List of Abbreviations -- Preface -- Chapter 1 Introduction to Big Data -- 1.1. Introduction -- 1.2. Concept of Big Data -- 1.3. What is Data? -- 1.4. What is Big Data? -- 1.5. The Big Data Systems are Different -- 1.6. Big Data Analytics -- 1.7. Case Study: German Telecom Company -- 1.8. Checkpoints -- Chapter 2 Identifier Systems -- 2.1. Meaning Of Identifier System -- 2.2. Features Of An Identifier System -- 2.3. Database Identifiers -- 2.4. Classes Of Identifiers -- 2.5. Rules For Regular Identifiers 2.6. One-Way Hash Function -- 2.7. De-Identification And Data Scrubbing -- 2.8. Concept Of De-Identification -- 2.9. The Process Of De-Identifications -- 2.10. Techniques Of De-Identification -- 2.11. Assessing The Risk Of Re-Identification -- 2.12. Case Study: Mastercard: Applying Social Media Research Insights For Better Business Decisions -- 2.13. Checkpoints -- Chapter 3 Improving the Quality of Big Data and Its Measurement -- 3.1. Data Scrubbing -- 3.2. Meaning of Bad Data -- 3.3. Common Approaches to Improve Data Quality -- 3.4. Measuring Big Data -- 3.5. How To Measure Big Data 3.6. Measuring Big Data Roi: A Sign of Data Maturity -- 3.7. The Interplay Of Hard And Soft Benefits -- 3.8. When Big Data Projects Require Big Investments -- 3.9. Real-Time, Real-World Roi -- 3.10. Case Study 2: Southwest Airlines: Big Data Pr Analysis Aids on-Time Performance -- 3.11. Checkpoints -- Chapter 4 Ontologies -- Introduction -- 4.1. Concept of Ontologies -- 4.2. Relation of Ontologies To Big Data Trend -- 4.3. Advantages And Limitations of Ontologies -- 4.4. Why Are Ontologies Developed? -- 4.5. Semantic Web -- 4.6. Major Components of Semantic Web -- 4.7. Checkpoints Chapter 5 Data Integration and Interoperability -- 5.1. What Is Data Integration? -- 5.2. Data Integration Areas -- 5.3. Types of Data Integration -- 5.4. Challenges of Data Integration and Interoperability in Big Data -- 5.5. Challenges of Big Data Integration And Interoperability -- 5.6. Immutability And Immortality -- 5.7. Data Types and Data Objects -- 5.8. Legacy Data -- 5.9. Data Born From Data -- 5.10. Reconciling Identifiers Across Institutions -- 5.11. Simple But Powerful Business Data Techniques -- 5.12. Association Rule Learning (ARL) -- 5.13. Classification Tree Analysis 5.14. Checkpoints -- Chapter 6 Clustering, Classification, and Reduction -- Introduction -- 6.1. Logistic Regression (Predictive Learning Model) -- 6.2. Clustering Algorithms -- 6.3. Data Reduction Strategies -- 6.4. Data Reduction Methods -- 6.5. Data Visualization: Data Reduction For Everyone -- 6.6. Case Study: Coca-Cola Enterprises (CCE) Case Study: The Thirst For Hr Analytics Grows -- 6.5. Checkpoints -- Chapter 7 Key Considerations in Big Data Analysis -- Introduction -- 7.1. Major Considerations For Big Data And Analytics -- 7.2. Overfitting -- 7.3. Bigness Bias 7.4. Step Wise Approach In Analysis of Big Data. Description based upon print version of record. Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Database management. http://id.loc.gov/authorities/subjects/sh85035848 Données volumineuses. Bases de données Gestion. Big data fast Database management fast Print version: Luna, Alvin Albuero De Principles of Big Data Ashland : Arcler Press,c2020 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2725218 Volltext
spellingShingle	Luna, Alvin Albuero De Principles of Big Data Cover -- Title Page -- Copyright -- ABOUT THE AUTHOR -- TABLE OF CONTENTS -- List of Abbreviations -- Preface -- Chapter 1 Introduction to Big Data -- 1.1. Introduction -- 1.2. Concept of Big Data -- 1.3. What is Data? -- 1.4. What is Big Data? -- 1.5. The Big Data Systems are Different -- 1.6. Big Data Analytics -- 1.7. Case Study: German Telecom Company -- 1.8. Checkpoints -- Chapter 2 Identifier Systems -- 2.1. Meaning Of Identifier System -- 2.2. Features Of An Identifier System -- 2.3. Database Identifiers -- 2.4. Classes Of Identifiers -- 2.5. Rules For Regular Identifiers 2.6. One-Way Hash Function -- 2.7. De-Identification And Data Scrubbing -- 2.8. Concept Of De-Identification -- 2.9. The Process Of De-Identifications -- 2.10. Techniques Of De-Identification -- 2.11. Assessing The Risk Of Re-Identification -- 2.12. Case Study: Mastercard: Applying Social Media Research Insights For Better Business Decisions -- 2.13. Checkpoints -- Chapter 3 Improving the Quality of Big Data and Its Measurement -- 3.1. Data Scrubbing -- 3.2. Meaning of Bad Data -- 3.3. Common Approaches to Improve Data Quality -- 3.4. Measuring Big Data -- 3.5. How To Measure Big Data 3.6. Measuring Big Data Roi: A Sign of Data Maturity -- 3.7. The Interplay Of Hard And Soft Benefits -- 3.8. When Big Data Projects Require Big Investments -- 3.9. Real-Time, Real-World Roi -- 3.10. Case Study 2: Southwest Airlines: Big Data Pr Analysis Aids on-Time Performance -- 3.11. Checkpoints -- Chapter 4 Ontologies -- Introduction -- 4.1. Concept of Ontologies -- 4.2. Relation of Ontologies To Big Data Trend -- 4.3. Advantages And Limitations of Ontologies -- 4.4. Why Are Ontologies Developed? -- 4.5. Semantic Web -- 4.6. Major Components of Semantic Web -- 4.7. Checkpoints Chapter 5 Data Integration and Interoperability -- 5.1. What Is Data Integration? -- 5.2. Data Integration Areas -- 5.3. Types of Data Integration -- 5.4. Challenges of Data Integration and Interoperability in Big Data -- 5.5. Challenges of Big Data Integration And Interoperability -- 5.6. Immutability And Immortality -- 5.7. Data Types and Data Objects -- 5.8. Legacy Data -- 5.9. Data Born From Data -- 5.10. Reconciling Identifiers Across Institutions -- 5.11. Simple But Powerful Business Data Techniques -- 5.12. Association Rule Learning (ARL) -- 5.13. Classification Tree Analysis 5.14. Checkpoints -- Chapter 6 Clustering, Classification, and Reduction -- Introduction -- 6.1. Logistic Regression (Predictive Learning Model) -- 6.2. Clustering Algorithms -- 6.3. Data Reduction Strategies -- 6.4. Data Reduction Methods -- 6.5. Data Visualization: Data Reduction For Everyone -- 6.6. Case Study: Coca-Cola Enterprises (CCE) Case Study: The Thirst For Hr Analytics Grows -- 6.5. Checkpoints -- Chapter 7 Key Considerations in Big Data Analysis -- Introduction -- 7.1. Major Considerations For Big Data And Analytics -- 7.2. Overfitting -- 7.3. Bigness Bias Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Database management. http://id.loc.gov/authorities/subjects/sh85035848 Données volumineuses. Bases de données Gestion. Big data fast Database management fast
subject_GND	http://id.loc.gov/authorities/subjects/sh2012003227 http://id.loc.gov/authorities/subjects/sh85035848
title	Principles of Big Data
title_auth	Principles of Big Data
title_exact_search	Principles of Big Data
title_full	Principles of Big Data [electronic resource] / Alvin Albuero De Luna.
title_fullStr	Principles of Big Data [electronic resource] / Alvin Albuero De Luna.
title_full_unstemmed	Principles of Big Data [electronic resource] / Alvin Albuero De Luna.
title_short	Principles of Big Data
title_sort	principles of big data
topic	Big data. http://id.loc.gov/authorities/subjects/sh2012003227 Database management. http://id.loc.gov/authorities/subjects/sh85035848 Données volumineuses. Bases de données Gestion. Big data fast Database management fast
topic_facet	Big data. Database management. Données volumineuses. Bases de données Gestion. Big data Database management
url	https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2725218
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