The more data an association has, the more troublesome it is to process, store, and break down; however, on the other hand, the more data the association has, the more precise its expectations can be. Too Big Data accompanies big duty. Big Data computing is typically classified into two sorts based on the process necessities, which are Big Data batch computing and Big Data stream computing. Big Data requires military-grade encryption keys to keep data sheltered and private. This is the place data science comes in. Numerous associations, confronted with the issue of having the option to gauge, channel, and dissect data, are going to data science for arrangements - recruiting data researchers, individuals who are authorities in seeming well and good out of a tremendous measure of data. By and large, this implies utilizing measurable models to make calculations to sort, characterize, and process data. In this paper, an audit of different algorithms essential for dealing with such enormous data streams for classification and clustering is given. These algorithms give us different techniques executed to deal with Big Data.

Chapter Contents:

• Abstract
• 3.1 Introduction
• 3.1.1 What is streaming data?
• 3.1.2 Significance of data streams
• 3.2 Streaming analytics: why and how
• 3.2.1 Advantages of streaming data
• 3.2.2 Difference between streaming data and traditional data
• 3.3 Streaming data technologies
• 3.3.1 Column-oriented databases
• 3.3.2 Schema-less databases
• 3.3.3 Hadoop
• 3.3.4 Hive
• 3.4 Algorithms to handle stream data
• 3.4.1 Classification algorithms
• 3.4.2 Supervised methods
• 3.4.3 Analysis of classification
• 3.4.4 Analysis of regression
• 3.4.5 Unsupervised methods
• 3.4.6 Introduction
• 3.5 Semi-supervised algorithms
• 3.5.1 Self-training
• 3.5.2 Graph-based semi-supervised machine learning
• 3.6 Open challenges in data stream
• 3.7 Efficiency of clustering algorithms in stream data
• 3.8 Findings and recommendations
• 3.9 Conclusion
• References

Inspec keywords: Big Data; unsupervised learning; private key cryptography; supervised learning; military computing

Other keywords: supervised algorithmic technique; military-grade encryption keys; Big Data batch computing; enormous data streams; data science; Big Data stream computing; unsupervised algorithmic technique

Subjects: Unsupervised learning; Data security; Supervised learning; Cryptography