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Repurposing legacy data : innovative case studies /
Repurposing Legacy Data: Innovative Case Studies takes a look at how data scientists have re-purposed legacy data, whether their own, or legacy data that has been donated to the public domain. Most of the data stored worldwide is legacy data-data created some time in the past, for a particular purpo...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam ; Boston :
Elsevier,
[2015]
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| Colección: | Computer science reviews and trends.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Repurposing Legacy Data; Copyright Page; Contents; Author Biography; 1 Introduction; 1.1 Why Bother?; 1.2 What Is Data Repurposing?; 1.3 Data Worth Preserving; 1.4 Basic Data Repurposing Tools; 1.4.1 A Simple Text Editor; 1.4.2 Simple Programming Skills; 1.4.3 Data Visualization Utilities; 1.5 Personal Attributes of Data Repurposers; 1.5.1 Data Organization Methods; 1.5.2 Ability to Develop a Clear Understanding of the Goals of a Project; References; 2 Learning from the Masters; 2.1 New Physics from Old Data; 2.2 Repurposing the Physical and Abstract Property of Uniqueness.
- 2.3 Repurposing a 2,000-Year-Old Classification2.4 Decoding the Past; 2.5 What Makes Data Useful for Repurposing Projects?; References; 3 Dealing with Text; 3.1 Thus It Is Written; 3.2 Search and Retrieval; 3.3 Indexing Text; 3.4 Coding Text; References; 4 New Life for Old Data; 4.1 New Algorithms; 4.2 Taking Closer Looks; 4.3 Crossing Data Domains; References; 5 The Purpose of Data Analysis Is to Enable Data Reanalysis; 5.1 Every Initial Data Analysis on Complex Datasets Is Flawed; 5.2 Unrepeatability of Complex Analyses; 5.3 Obligation to Verify and Validate.
- 5.4 Asking What the Data Really MeansReferences; 6 Dark Legacy: Making Sense of Someone Else's Data; 6.1 Excavating Treasures from Lost and Abandoned Data Mines; 6.2 Nonstandard Standards; 6.3 Specifications, Not Standards; 6.4 Classifications and Ontologies; 6.5 Identity and Uniqueness; 6.6 When to Terminate (or Reconsider) a Data Repurposing Project; References; 7 Social and Economic Issues; 7.1 Data Sharing and Reproducible Research; 7.2 Acquiring and Storing Data; 7.3 Keeping Your Data Forever; 7.4 Data Immutability; 7.5 Privacy and Confidentiality; 7.6 The Economics of Data Repurposing.