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Data Science and Big Data Computing Frameworks and Methodologies /
This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by an authoritative collection of thirty-six researchers and practitioners from around the world, discussing research developments an...
| Clasificación: | Libro Electrónico |
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| Autor Corporativo: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2016.
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| Edición: | 1st ed. 2016. |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- Part I: Data Science Applications and Scenarios
- An Interoperability Framework and Distributed Platform for Fast Data Applications
- Complex Event Processing Framework for Big Data Applications
- Agglomerative Approaches for Partitioning of Networks in Big Data Scenarios
- Identifying Minimum-Sized Influential Vertices on Large-Scale Weighted Graphs: A Big Data Perspective
- Part II: Big Data Modelling and Frameworks
- A Unified Approach to Data Modelling and Management in Big Data Era
- Interfacing Physical and Cyber Worlds: A Big Data Perspective
- Distributed Platforms and Cloud Services: Enabling Machine Learning for Big Data
- An Analytics Driven Approach to Identify Duplicate Bug Records in Large Data Repositories
- Part III: Big Data Tools and Analytics
- Large Scale Data Analytics Tools: Apache Hive, Pig and HBase
- Big Data Analytics: Enabling Technologies and Tools
- A Framework for Data Mining and Knowledge Discovery in Cloud Computing
- Feature Selection for Adaptive Decision Making in Big Data Analytics
- Social Impact and Social Media Analysis Relating to Big Data.