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BL
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| Record Number
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872526
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| Main Entry
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Berman, Jules J.
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| Title & Author
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Principles and practice of big data : : preparing, sharing, and analyzing complex information /\ Jules J. Berman.
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| Edition Statement
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Second edition.
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| Publication Statement
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London :: Academic Press,, [2018]
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, ©2018
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| Page. NO
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1 online resource
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| ISBN
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0128156104
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: 9780128156100
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0128156090
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9780128156094
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Front Cover; Principles and Practice of Big Data: Preparing, sharing, and analyzing complex information; Copyright; Other Books by Jules J. Berman; Dedication; Contents; About the Author; Author's Preface to Second Edition; Author's Preface to First Edition; References; Chapter 1: Introduction; Section 1.1. Definition of Big Data; Section 1.2. Big Data Versus Small Data; Section 1.3. Whence Comest Big Data?; Section 1.4. The Most Common Purpose of Big Data Is to Produce Small Data; Section 1.5. Big Data Sits at the Center of the Research Universe; Glossary; References
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Chapter 2: Providing Structure to Unstructured Data; Section 2.1. Nearly All Data Is Unstructured and Unusable in Its Raw Form; Section 2.2. Concordances; Section 2.3. Term Extraction; Section 2.4. Indexing; Section 2.5. Autocoding; Section 2.6. Case Study: Instantly Finding the Precise Location of Any Atom in the Universe (Some Assembly Required); Section 2.7. Case Study (Advanced): A Complete Autocoder (in 12 Lines of Python Code); Section 2.8. Case Study: Concordances as Transformations of Text; Section 2.9. Case Study (Advanced): Burrows Wheeler Transform (BWT); Glossary; References
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Chapter 3: Identification, Deidentification, and Reidentification; Section 3.1. What Are Identifiers?; Section 3.2. Difference Between an Identifier and an Identifier System; Section 3.3. Generating Unique Identifiers; Section 3.4. Really Bad Identifier Methods; Section 3.5. Registering Unique Object Identifiers; Section 3.6. Deidentification and Reidentification; Section 3.7. Case Study: Data Scrubbing; Section 3.8. Case Study (Advanced): Identifiers in Image Headers; Section 3.9. Case Study: One-Way Hashes; Glossary; References; Chapter 4: Metadata, Semantics, and Triples
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Section 4.1. Metadata; Section 4.2. eXtensible Markup Language; Section 4.3. Semantics and Triples; Section 4.4. Namespaces; Section 4.5. Case Study: A Syntax for Triples; Section 4.6. Case Study: Dublin Core; Glossary; References; Chapter 5: Classifications and Ontologies; Section 5.1. It's All About Object Relationships; Section 5.2. Classifications, the Simplest of Ontologies; Section 5.3. Ontologies, Classes With Multiple Parents; Section 5.4. Choosing a Class Model; Section 5.5. Class Blending; Section 5.6. Common Pitfalls in Ontology Development
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Section 5.7. Case Study: An Upper Level Ontology; Section 5.8. Case Study (Advanced): Paradoxes; Section 5.9. Case Study (Advanced): RDF Schemas and Class Properties; Section 5.10. Case Study (Advanced): Visualizing Class Relationships; Glossary; References; Chapter 6: Introspection; Section 6.1. Knowledge of Self; Section 6.2. Data Objects: The Essential Ingredient of Every Big Data Collection; Section 6.3. How Big Data Uses Introspection; Section 6.4. Case Study: Time Stamping Data; Section 6.5. Case Study: A Visit to the TripleStore
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| Abstract
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Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition updates and expands on the first edition, bringing a set of techniques and algorithms that are tailored to Big Data projects. The book stresses the point that most data analyses conducted on large, complex data sets can be achieved without the use of specialized suites of software (e.g., Hadoop), and without expensive hardware (e.g., supercomputers). The core of every algorithm described in the book can be implemented in a few lines of code using just about any popular programming language (Python snippets are provided). Through the use of new multiple examples, this edition demonstrates that if we understand our data, and if we know how to ask the right questions, we can learn a great deal from large and complex data collections. The book will assist students and professionals from all scientific backgrounds who are interested in stepping outside the traditional boundaries of their chosen academic disciplines.
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| Subject
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Big data.
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| Subject
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Big data.
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| Subject
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COMPUTERS-- Databases-- Data Mining.
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| Dewey Classification
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005.7
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| LC Classification
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QA76.9.B45B47 2018eb
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