By Ali Emrouznejad
The major target of this publication is to supply the mandatory historical past to paintings with tremendous info by means of introducing a few novel optimization algorithms and codes in a position to operating within the great facts environment in addition to introducing a few purposes in monstrous information optimization for either lecturers and practitioners , and to learn society, undefined, academia, and executive. providing purposes in quite a few industries, this e-book should be worthwhile for the researchers aiming to analyses huge scale info. numerous optimization algorithms for giant facts together with convergent parallel algorithms, constrained reminiscence package set of rules, diagonal package deal process, convergent parallel algorithms, community analytics, and plenty of extra were explored during this book.
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Extra info for Big Data Optimization: Recent Developments and Challenges
Big Data Benchmarks are a good way to optimize and ﬁne-tune the performance in terms of processing speed, execution time or throughput of the big data system. A benchmark can also be used to evaluate the availability and fault-tolerance of a big data system. Especially for distributed big data systems a high availability is an important requirement. While some benchmarks are developed to test particular software platforms, others are technology independent and can be implemented for multiple platforms.
This high level of distribution is possible due to the use of a simple data model. Cassandra uses a very unique tunable consistency model with different consistency levels where the database administrator can set the number of replica and favor consistency over availability, or vice versa. In practice this results in a tradeoff between data accuracy and response times. Cassandra has a ﬂexible schema and comes with its own query language called Cassandra Query Language (CQL) . The simple key-value data model allows for a very fast read/write performance and good scalability over various servers.