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Information Technology

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B2B & B2C

Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators.

Principles of Big Data

POST SECONDARY FOUR YEAR

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PRINCIPLES OF BIG DATA

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Dedication.
Contents.
Acknowledgments.
1: Author Biography.
Preface.
Introduction.
2: Providing Structure to Unstructured Data.
3: Identification, Deidentification, and Reidentification.
4: Ontologies and Semantics.
5: Introspection.
6: Data Integration and Software Interoperability.
7: Immutability and Immortality.
8: Measurement.
9: Simple But Powerful Big Data Techniques.
10: Analysis.
11: Special Considerations in Big Data Analysis.
12: Stepwise Approach to Big Data Analysis.
13: Failure.
14: Legalities.
15: Societal Issues.
16: The Future.
Glossary.
References.
Index.

Principles of Big Data.css-1p8rvzy{position:absolute;padding-left:8px;}

Principles of Big Data