Big Data
Recent advances in digital sensors, networks, storage, and computation, along with their low cost availability, is creating huge collections of data known as "Big Data." This has led to the emergence of a new computing paradigm that impacts society as a whole, changing… Celý popis ↓
Porovnanie cien Big Data
Popis Big Data
Big Data
Recent advances in digital sensors, networks, storage, and computation, along with their low cost availability, is creating huge collections of data known as "Big Data." This has led to the emergence of a new computing paradigm that impacts society as a whole, changing the way business, science, academia, and governments work. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications. Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data.
These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, as well as the privacy and ethical issues. To help realize Big Data’s full potential, Big Data: Principles and Paradigms addresses numerous challenges and offers the conceptual and technological solutions for tackling them. Covers computational platforms supporting Big Data applicationsAddresses key principles underlying Big Data computingExamines key developments supporting next generation Big Data platformsExplores the challenges in Big Data computing and ways they to overcome themExpert contributors from both academia and industry
Parametre
- Výrobca
- Elsevier Science & Technology
- Autor
- Buyya, R.
- Počet strán
- 494
Podobné produkty
Big Data
Big Data
Big data
Trouble With Big Data
Scalable Big Data Architecture
Big Data Leclerc Benoit
Modern Big Data Architectures
Big Data at Work
Big Data - Bill Schmarzo
Big Data-Driven Business
Big Data Pietsch Wolfgang
Big Data Harkness Timandra
Big Data Clegg Brian
