Principles of Big Data:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Ashland :
Arcler Press,
2020.
|
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Item Description: | 7.4. Step Wise Approach In Analysis of Big Data. |
| Physical Description: | 1 online resource (200 pages) |
| ISBN: | 1774078147 9781774078143 |
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| 245 | 1 | 0 | |a Principles of Big Data |h [electronic resource] / |c Alvin Albuero De Luna. |
| 260 | |a Ashland : |b Arcler Press, |c 2020. | ||
| 300 | |a 1 online resource (200 pages) | ||
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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. | ||
| 588 | 0 | |a Description based upon print version of record. | |
| 650 | 0 | |a Big data. |0 http://id.loc.gov/authorities/subjects/sh2012003227 | |
| 650 | 0 | |a Database management. |0 http://id.loc.gov/authorities/subjects/sh85035848 | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 6 | |a Bases de données |x Gestion. | |
| 650 | 7 | |a Big data |2 fast | |
| 650 | 7 | |a Database management |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Luna, Alvin Albuero De |t Principles of Big Data |d Ashland : Arcler Press,c2020 |
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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 |
| ctrlnum | (OCoLC)1231606332 |
| dewey-full | 005.7 |
| dewey-hundreds | 000 - Computer science, information, general works |
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| id | ZDB-4-EBA-on1231606332 |
| illustrated | Not Illustrated |
| indexdate | 2025-04-11T08:47:35Z |
| institution | BVB |
| isbn | 1774078147 9781774078143 |
| language | English |
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| record_format | marc |
| spelling | Luna, Alvin Albuero De. Principles of Big Data [electronic resource] / Alvin Albuero De Luna. Ashland : Arcler Press, 2020. 1 online resource (200 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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 |
| 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 |
| work_keys_str_mv | AT lunaalvinalbuerode principlesofbigdata |