Cargando…
Big data : algorithms, analytics, and applications /
Presenting the contributions of leading experts in their respective fields, Big Data: Algorithms, Analytics, and Applications bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues about Big Data, i...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton [Florida] :
CRC Press, Taylor & Francis Group,
[2015]
Boston, Massachusetts : Credo Reference, 2016. |
| Edición: | [Enhanced Credo edition]. |
| Colección: | Chapman & Hall/CRC big data series.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Foreword / by Jack Dongarra
- Foreword / by Dr. Yi Pan
- Foreword / by D. Frank Hsu
- Preface
- Editors
- Contributors
- Section I. Big data management: Chapter 1. Scalable indexing for big data processing; Chapter 2. Scalability and cost evaluation of incremental data processing using Amazon's Hadoop Service; Chapter 3. Singular value decomposition, clustering, and indexing for similarity search for large data sets in high-dimensional spaces; Chapter 4. Multiple sequence alignment and clustering with dot matrices, entropy, and genetic algorithms
- Section II. Big data processing: Chapter 5. Approaches for high-performance big data processing: applications and challenges; Chapter 6. The art of scheduling for big data science; Chapter 7. Time-space scheduling in the MapReduce framework; Chapter 8. GEMS: graph database engine for multithreaded systems. Chapter 9. KSC-net: community detection for big data networks. Chapter 10. Making big data transparent to the software developers' community
- Section III. Big data stream techniques and algorithms: Chapter 11. Key technologies for big data stream computing; Chapter 12. Streaming algorithms for big data processing on multicore architecture; Chapter 13. Organic streams: a unified framework for personal big data integration and organization towards social sharing and individualized sustainable use; Chapter 14. Managing big trajectory data: online processing of positional streams.
- Section IV. Big data privacy: Chapter 15. Personal data protection aspects of big data; Chapter 16. Privacy-preserving big data management: the case of OLAP
- Section V. Big data applications: Chapter 17. Big data in finance; Chapter 18. Semantic-based heterogeneous multimedia big data retrieval; Chapter 19. Topic modeling for large-scale multimedia analysis and retrieval; Chapter 20. Big data biometrics processing: a case study of an iris matching algorithm on Intel Xeon Phi; Chapter 21. Storing, managing, and analyzing big satellite data: experiences and lessons learned from a real-world application; Chapter 22. Barriers to the adoption of big data applications in the social sector.