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    Principles of Big Data

    Principles of Big Data

    - Preparing, Sharing, and Analyzing Complex Information

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    • Format
    • Bog, paperback
    • Engelsk

    Beskrivelse

    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.

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  • Vægt580 g
    • coffee cup img
    10 cm
    book img
    19,1 cm
    23,5 cm

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    Metadata Classification Copyright Data protection Identification Informed Consent. Infrastructure Intellectual property Nomenclature Ontology. Open Source Terminology Verification Supercomputers Introspection Failure Indexing Costs Machine learning Parsing Programming Immutability Patents Validity Standard Statistical analysis Validation Bias Combinatorics Data curation Artificial intelligence Machine translation Reflection Triple Overconfidence Class Freedom of Information Act Data Sharing Workforce Data objects Anonymization Big Data Principles Fair Use Interoperability Data Classification Versioning Clustering University training Accuracy Complexity Collective intelligence Specification Vulnerability Precision Unstructured Data Data organization Overfitting Self-description Data Integration Estimation 'predictive analytics' Classifying Meaning Open Access Tort Professional skills Infringement Instances Abandoned data advanced training Benefits class hierarchy Autocoding Class object Curators Data Ownership Data review data values deidentification formulating a question data managers data properties Data Quality Act data uniqueness integration of information systems identifier Inc. v. Rural Telephone Service Co. legacy data long-term efforts Feist Publishing mapping between standards limited data use agreements free-lance data analysts Hash Collision ontologic competence human evaluation of Big Data Random character string Retrospective data Reidentification Serious Big Data subclass time-window bias Superclass Variable reduction recommendation techniques one-way hash verification of data validation of assertions
    Machine Name: SAXO082