In-Memory Analytics

In-Memory Analytics

Jorge Manjarrez Sánchez (Instituto Tecnologico Superior de Jerez, Mexico)
DOI: 10.4018/978-1-5225-2255-3.ch157

Abstract

Analytics is the processing of data for information discovery. In-memory implementation of machine learning and statistical algorithms enable the fast processing of data for descriptive, diagnostic, predictive and prescriptive analytics. In this chapter we first present some concepts and challenges for fast analytics, then we discuss some of the most relevant proposals and data management structures for in-memory data analytics in centralized, parallel and distributed settings. Finally, we offer further research directions and some concluding remarks.
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Fast Analytics

In this section we address the speed challenge. When the speed of results is a factor to consider for efficient analytics, and taking into consideration that it must be used as much data as possible for the abovementioned reasons, then one must reduce or eliminate processing bottlenecks. They can be the IO latency and CPU usage. CPU is good at computing mathematical operations and multicore CPU’s enhance their performance. IO depends on the type of disk and RAM. Solid state disks are faster than hard disk drives, but random access memory is faster. A typical hard drive has a latency of 5 ms while RAM has only 100 ns, it is 50000 times faster. But also the faster the more expensive. There is one more rapid type of memory: cache memory, which is a small and expensive memory within the CPU die. Main memory or In-memory refers to the processing of data performed in RAM.

Key Terms in this Chapter

In-Memory Computing: Computer processing performed by loading all data in RAM in order to speed up data access.

Predictive Analytics: The analytics of data for forecasting outcomes, provides an answer to what will happen, based on estimates issued from probabilities and statistical analyses.

Machine Learning: The science of developing techniques to give the computer inference and deduction capabilities to achieve diverse processing tasks autonomously.

Descriptive Analytics: The gathering and processing of data to determine and understand the state of things in an enterprise, gives an answer to what happened?

Data Analytics: The processing of data for information and facts discovery.

Prescriptive Analytics: After knowing the current state and the likelihood of the outcomes, prescriptive analytics concern is to provide guidance or advise on what an enterprise should do.

Scalability: It is the capability of a system to handle bigger workloads without compromising performance.

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