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Data architecture : a primer for the data scientist : big data, data warehouse and data vault / / W. H. Inmon, Dan Linstedt ; Steven Elliot, executive editor ; Mark Rogers, designer
| Data architecture : a primer for the data scientist : big data, data warehouse and data vault / / W. H. Inmon, Dan Linstedt ; Steven Elliot, executive editor ; Mark Rogers, designer |
| Autore | Inmon W. H. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Amsterdam, Netherlands : , : Morgan Kaufmann, , 2015 |
| Descrizione fisica | 1 online resource (378 p.) |
| Disciplina | 005.745 |
| Soggetto topico |
Data warehousing
Big data |
| ISBN | 0-12-802091-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Dedication; Contents; Preface; About the authors; 1.1 - Corporate data; The Totality of Data Across the Corporation; Dividing Unstructured Data; Business Relevancy; Big Data; The Great Divide; The Continental Divide; The Complete Picture; 1.2 - The data infrastructure; Two Types of Repetitive Data; Repetitive Structured Data; Repetitive Big Data; The Two Infrastructures; What's being Optimized?; Comparing the Two Infrastructures; 1.3 - The "great divide"; Classifying Corporate Data; The "Great Divide"; Repetitive Unstructured Data; Nonrepetitive Unstructured Data
Different Worlds1.4 - Demographics of corporate data; 1.5 - Corporate data analysis; 1.6 - The life cycle of data - understanding data over time; 1.7 - A brief history of data; Paper Tape and Punch Cards; Magnetic Tapes; Disk Storage; Database Management System; Coupled Processors; Online Transaction Processing; Data Warehouse; Parallel Data Management; Data Vault; Big Data; The Great Divide; 2.1 - A brief history of big data; An Analogy - Taking the High Ground; Taking the High Ground; Standardization with the 360; Online Transaction Processing Enter Teradata and Massively Parallel ProcessingThen Came Hadoop and Big Data; IBM and Hadoop; Holding the High Ground; 2.2 - What is big data?; Another Definition; Large Volumes; Inexpensive Storage; The Roman Census Approach; Unstructured Data; Data in Big Data; Context in Repetitive Data; Nonrepetitive Data; Context in Nonrepetitive Data; 2.3 - Parallel processing; 2.4 - Unstructured data; Textual Information Everywhere; Decisions Based on Structured Data; The Business Value Proposition; Repetitive and Nonrepetitive Unstructured Information; Ease of Analysis; Contextualization Some Approaches to ContextualizationMapReduce; Manual Analysis; 2.5 - Contextualizing repetitive unstructured data; Parsing Repetitive Unstructured Data; Recasting the Output Data; 2.6 - Textual disambiguation; From Narrative into an Analytical Database; Input into Textual Disambiguation; Mapping; Input/Output; Document Fracturing/Named Value Processing; Preprocessing a Document; Emails - A Special Case; Spreadsheets; Report Decompilation; 2.7 - Taxonomies; Data Models and Taxonomies; Applicability of Taxonomies; What is a Taxonomy?; Taxonomies in Multiple Languages Dynamics of Taxonomies and Textual DisambiguationTaxonomies and Textual Disambiguation - Separate Technologies; Different Types of Taxonomies; Taxonomies - Maintenance Over Time; 3.1 - A brief history of data warehouse; Early Applications; Online Applications; Extract Programs; 4GL Technology; Personal Computers; Spreadsheets; Integrity of Data; Spider-Web Systems; The Maintenance Backlog; The Data Warehouse; To an Architected Environment; To the CIF; DW 2.0; 3.2 - Integrated corporate data; Many Applications; Looking Across the Corporation; More Than One Analyst; ETL Technology The Challenges of Integration |
| Record Nr. | UNINA-9910787905603321 |
Inmon W. H.
|
||
| Amsterdam, Netherlands : , : Morgan Kaufmann, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data architecture : a primer for the data scientist : big data, data warehouse and data vault / / W. H. Inmon, Dan Linstedt ; Steven Elliot, executive editor ; Mark Rogers, designer
| Data architecture : a primer for the data scientist : big data, data warehouse and data vault / / W. H. Inmon, Dan Linstedt ; Steven Elliot, executive editor ; Mark Rogers, designer |
| Autore | Inmon W. H. |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Amsterdam, Netherlands : , : Morgan Kaufmann, , 2015 |
| Descrizione fisica | 1 online resource (378 p.) |
| Disciplina | 005.745 |
| Soggetto topico |
Data warehousing
Big data |
| ISBN | 0-12-802091-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Dedication; Contents; Preface; About the authors; 1.1 - Corporate data; The Totality of Data Across the Corporation; Dividing Unstructured Data; Business Relevancy; Big Data; The Great Divide; The Continental Divide; The Complete Picture; 1.2 - The data infrastructure; Two Types of Repetitive Data; Repetitive Structured Data; Repetitive Big Data; The Two Infrastructures; What's being Optimized?; Comparing the Two Infrastructures; 1.3 - The "great divide"; Classifying Corporate Data; The "Great Divide"; Repetitive Unstructured Data; Nonrepetitive Unstructured Data
Different Worlds1.4 - Demographics of corporate data; 1.5 - Corporate data analysis; 1.6 - The life cycle of data - understanding data over time; 1.7 - A brief history of data; Paper Tape and Punch Cards; Magnetic Tapes; Disk Storage; Database Management System; Coupled Processors; Online Transaction Processing; Data Warehouse; Parallel Data Management; Data Vault; Big Data; The Great Divide; 2.1 - A brief history of big data; An Analogy - Taking the High Ground; Taking the High Ground; Standardization with the 360; Online Transaction Processing Enter Teradata and Massively Parallel ProcessingThen Came Hadoop and Big Data; IBM and Hadoop; Holding the High Ground; 2.2 - What is big data?; Another Definition; Large Volumes; Inexpensive Storage; The Roman Census Approach; Unstructured Data; Data in Big Data; Context in Repetitive Data; Nonrepetitive Data; Context in Nonrepetitive Data; 2.3 - Parallel processing; 2.4 - Unstructured data; Textual Information Everywhere; Decisions Based on Structured Data; The Business Value Proposition; Repetitive and Nonrepetitive Unstructured Information; Ease of Analysis; Contextualization Some Approaches to ContextualizationMapReduce; Manual Analysis; 2.5 - Contextualizing repetitive unstructured data; Parsing Repetitive Unstructured Data; Recasting the Output Data; 2.6 - Textual disambiguation; From Narrative into an Analytical Database; Input into Textual Disambiguation; Mapping; Input/Output; Document Fracturing/Named Value Processing; Preprocessing a Document; Emails - A Special Case; Spreadsheets; Report Decompilation; 2.7 - Taxonomies; Data Models and Taxonomies; Applicability of Taxonomies; What is a Taxonomy?; Taxonomies in Multiple Languages Dynamics of Taxonomies and Textual DisambiguationTaxonomies and Textual Disambiguation - Separate Technologies; Different Types of Taxonomies; Taxonomies - Maintenance Over Time; 3.1 - A brief history of data warehouse; Early Applications; Online Applications; Extract Programs; 4GL Technology; Personal Computers; Spreadsheets; Integrity of Data; Spider-Web Systems; The Maintenance Backlog; The Data Warehouse; To an Architected Environment; To the CIF; DW 2.0; 3.2 - Integrated corporate data; Many Applications; Looking Across the Corporation; More Than One Analyst; ETL Technology The Challenges of Integration |
| Record Nr. | UNINA-9910816227103321 |
|
Inmon W. H.
|
||
| Amsterdam, Netherlands : , : Morgan Kaufmann, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||