Big Data Analytics for Anna University R17 CBCS From Technical Publication

Unit I Introduction to Big Data Evolution of Big data – Best Practices for Big data Analytics – Big data characteristics – Validating – The Promotion of the Value of Big Data – Big Data Use Cases- Characteristics of Big Data Applications – Perception and Quantification of Value -Understanding Big Data Storage – A General Overview of High-Performance Architecture – HDFS – MapReduce and YARN – Map Reduce Programming Model. (Chapter – 1) Unit II Clustering and Classification Advanced Analytical Theory and Methods: Overview of Clustering – K-means – Use Cases – Overview of the Method – Determining the Number of Clusters – Diagnostics – Reasons to Choose and Cautions .- Classification: Decision Trees – Overview of a Decision Tree – The General Algorithm – Decision Tree Algorithms – Evaluating a Decision Tree – Decision Trees in R – Na?ve Bayes – Bayes? Theorem – Na?ve Bayes Classifier. (Chapter – 2) Unit III Association and Recommendation System Advanced Analytical Theory and Methods: Association Rules – Overview – Apriori Algorithm – Evaluation of Candidate Rules – Applications of Association Rules – Finding Association & finding similarity – Recommendation System: Collaborative Recommendation- Content Based Recommendation – Knowledge Based Recommendation- Hybrid Recommendation Approaches. (Chapter – 3) Unit IV Stream Memory Introduction to Streams Concepts – Stream Data Model and Architecture – Stream Computing, Sampling Data in a Stream – Filtering Streams – Counting Distinct Elements in a Stream – Estimating moments . Counting oneness in a Window – Decaying Window – Real time Analytics Platform (RTAP) applications – Case Studies – Real Time Sentiment Analysis, Stock Market Predictions – Using Graph Analytics for Big Data : Graph Analytics. (Chapter – 4) Unit V NoSQL Data Management for Big Data and Visualization NoSQL Databases : Schema-less Models : Increasing Flexibility for Data Manipulation-Key Value Stores- Document Stores – Tabular Stores – Object Data Stores – Graph Databases Hive – Sharding – Hbase – Analyzing big data with twitter – Big data for E-Commerce Big data for blogs – Review of Basic Data Analytic Methods using R. (Chapter – 5)