Recent Trends In IT

Unit

Contents

No. of Lectures

1

Introduction to recent trends

1.1 Artificial Intelligence

1.2 Data Warehouse

1.3 Data Mining

1.4 Spark

02

2

Artificial Intelligence

2.1 Introduction& Concept of AI

2.2 Applications of AI

2.3 Artificial Intelligence, Intelligent Systems, Knowledge –based Systems, AI Techniques

2.4 Early work in AI & related fields.

2.5 Defining AI problems as a State Space Search

2.6 Search and Control Strategies

2.7 Problem Characteristics

2.8 AI Problem: Water Jug Problem, Tower of Hanoi, Missionaries & Cannibal Problem

08

3

AI Search Techniques

3.1 Blind Search Techniques:

BFS, DFS, DLS, Iterative deepening Search, Bidirectional Search, and Uniform cost Search

3.2 Heuristic search techniques:

Generate and test, Hill Climbing, Best First search, Constraint Satisfaction, Mean-End Analysis, A*, AO*

08

4

Data Warehousing

4.1 Introduction to Data warehouse

4.2 Structure of Data Warehouse

4.3 Advantages & uses of Data Warehouse

4.4 Architecture of Data Warehouse

4.5 Multidimensional data model

4.6 OLAP Vs. OLTP

4.7 OLAP Operations

4.8 Types of OLAP Servers: ROLAP versus MOLAP versus HOLAP

08

5

Data Mining

5.1 Introduction to Data Mining

5.2 Data mining Task

5.3 Data mining issues

5.4 Data Mining versus Knowledge Discovery in Databases

5.5 Data Mining Verification vs. Discovery

5.6 Data Pre-processing – Need, Data Cleaning, Data Integration & Transformation, Data Reduction

5.7 Accuracy Measures: Precision, recall, F-measure, confusion matrix, cross-validation, bootstrap

5.8 Data Mining Techniques

5.9 Frequent item-sets and Association rule mining: Apriori algorithm, FP tree algorithm

5.10 Graph Mining: Frequent sub-graph mining

5.11 Software for data mining : R, Weka, Sample applications of data mining

5.12 Introduction to Text Mining, Web Mining, Spatial Mining, Temporal Mining

12

6

Spark

6.1 Introduction to Apache Spark

6.2 Spark Installation

6.3 Apache Spark Architecture

6.4 Components of Spark

6.5 Spark RDDs

6.6 RDD Operations: Transformation & Actions

6.7 Spark SQL and Data Frames

6.8 Introduction to Kafka for Spark Streaming

10