Principles of big data : preparing, sharing, and analyzing complex information.

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Berman, Jules J.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: BOSTON MORGAN KAUFMANN 2013.
Temas:
Big data.
Database management.
Données volumineuses.
Bases de données > Gestion.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

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100 1 |a Berman, Jules J. 
245 1 0 |a Principles of big data :  |b preparing, sharing, and analyzing complex information. 
260 |a BOSTON  |b MORGAN KAUFMANN  |c 2013. 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
505 0 0 |g Machine generated contents note:  |g 1.  |t Providing Structure to Unstructured Data --  |t Background --  |t Machine Translation --  |t Autocoding --  |t Indexing --  |t Term Extraction --  |g 2.  |t Identification, Deidentification, and Reidentification --  |t Background --  |t Features of an Identifier System --  |t Registered Unique Object Identifiers --  |t Really Bad Identifier Methods --  |t Embedding Information in an Identifier: Not Recommended --  |t One-Way Hashes --  |t Use Case: Hospital Registration --  |t Deidentification --  |t Data Scrubbing --  |t Reidentification --  |t Lessons Learned --  |g 3.  |t Ontologies and Semantics Background --  |t Classifications, the Simplest of Ontologies --  |t Ontologies, Classes with Multiple Parents --  |t Choosing a Class Model --  |t Introduction to Resource Description Framework Schema --  |t Common Pitfalls in Ontology Development --  |g 4.  |t Introspection --  |t Background --  |t Knowledge of Self --  |t eXtensible Markup Language --  |t Introduction to Meaning --  |t Namespaces and the Aggregation of Meaningful Assertions --  |t Resource Description Framework Triples --  |t Reflection --  |t Use Case: Trusted Time Stamp --  |t Summary --  |g 5.  |t Data Integration and Software Interoperability --  |t Background --  |t Committee to Survey Standards --  |t Standard Trajectory --  |t Specifications and Standards --  |t Versioning --  |t Compliance Issues --  |t Interfaces to Big Data Resources --  |g 6.  |t Immutability and Immortality --  |t Background --  |t Immutability and Identifiers --  |t Data Objects --  |t Legacy Data --  |t Data Born from Data --  |t Reconciling Identifiers across Institutions --  |t Zero-Knowledge Reconciliation --  |t Curator's Burden --  |g 7.  |t Measurement --  |t Background --  |t Counting --  |t Gene Counting --  |t Dealing with Negations --  |t Understanding Your Control --  |t Practical Significance of Measurements --  |t Obsessive-Compulsive Disorder: The Mark of a Great Data Manager --  |g 8.  |t Simple but Powerful Big Data Techniques --  |t Background --  |t Look at the Data --  |t Data Range --  |t Denominator --  |t Frequency Distributions --  |t Mean and Standard Deviation --  |t Estimation Only Analyses --  |t Use Case: Watching Data Trends with Google Ngrams --  |t Use Case: Estimating Movie Preferences --  |g 9.  |t Analysis --  |t Background --  |t Analytic Tasks --  |t Clustering, Classifying, Recommending, and Modeling --  |t Data Reduction --  |t Normalizing and Adjusting Data --  |t Big Data Software: Speed and Scalability --  |t Find Relationships, Not Similarities --  |g 10.  |t Special Considerations in Big Data Analysis --  |t Background --  |t Theory in Search of Data --  |t Data in Search of a Theory --  |t Overfitting --  |t Bigness Bias --  |t Too Much Data --  |t Fixing Data --  |t Data Subsets in Big Data: Neither Additive nor Transitive --  |t Additional Big Data Pitfalls --  |g 11.  |t Stepwise Approach to Big Data Analysis --  |t Background --  |g Step 1  |t Question Is Formulated --  |g Step 2  |t Resource Evaluation --  |g Step 3  |t Question Is Reformulated --  |g Step 4  |t Query Output Adequacy --  |g Step 5  |t Data Description --  |g Step 6  |t Data Reduction --  |g Step 7  |t Algorithms Are Selected, If Absolutely Necessary --  |g Step 8  |t Results Are Reviewed and Conclusions Are Asserted --  |g Step 9  |t Conclusions Are Examined and Subjected to Validation --  |g 12.  |t Failure --  |t Background --  |t Failure Is Common --  |t Failed Standards --  |t Complexity --  |t When Does Complexity Help? --  |t When Redundancy Fails --  |t Save Money; Don't Protect Harmless Information --  |t After Failure --  |t Use Case: Cancer Biomedical Informatics Grid, a Bridge Too Far --  |g 13.  |t Legalities --  |t Background --  |t Responsibility for the Accuracy and Legitimacy of Contained Data --  |t Rights to Create, Use, and Share the Resource --  |t Copyright and Patent Infringements Incurred by Using Standards --  |t Protections for Individuals --  |t Consent --  |t Unconsented Data --  |t Good Policies Are a Good Policy --  |t Use Case: The Havasupai Story --  |g 14.  |t Societal Issues --  |t Background --  |t How Big Data Is Perceived --  |t Necessity of Data Sharing, Even When It Seems Irrelevant --  |t Reducing Costs and Increasing Productivity with Big Data --  |t Public Mistrust --  |t Saving Us from Ourselves --  |t Hubris and Hyperbole --  |g 15.  |t Future --  |t Background --  |t Last Words. 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Big data. 
650 0 |a Database management. 
650 6 |a Données volumineuses. 
650 6 |a Bases de données  |x Gestion. 
650 7 |a Big data.  |2 fast  |0 (OCoLC)fst01892965 
650 7 |a Database management.  |2 fast  |0 (OCoLC)fst00888037 
776 0 8 |i Print version:  |a Berman, Jules J.  |t Principles of big data.  |d Amsterdam : Elsevier, Morgan Kaufmann, [2013]  |z 9780124045767  |w (DLC) 2013006421  |w (OCoLC)841050173 
856 4 0 |u https://learning.oreilly.com/library/view/~/9780124045767/?ar  |z Texto completo (Requiere registro previo con correo institucional) 
938 |a Books 24x7  |b B247  |n bks00054056 
938 |a YBP Library Services  |b YANK  |n 10745431 
994 |a 92  |b IZTAP 

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