Advanced Database Management Concepts
  • Advanced Database Management Concepts

Advanced Database Management Concepts

₹125.00
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Advanced Database Management Concepts

Author: Prashant Sinalker

Price: Rs. 125

ISBN: 978-93-5016-428-0

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1. OODBMS and ORDBMS

Introduction

1.1 Overview of Object-Oriented Concepts and Characteristics

1.1.1 Object-Oriented Data Model

1.1.2 Object-Relational Data Model

1.1.3 Object-Relational Database Systems

1.1.4 Persistent Programming Language

1.1.5 Object-Oriented Database Systems

1.1.6 Characteristics of Object-Oriented Databases

1.1.7 Concept of Object-Oriented Database

1.2 Objects, OIDs and Reference Types

1.2.1 Notions of Equality

1.2.2 Dereferencing Reference Types

1.2.3 URLs and OIDs in SQL: 1999

1.2.4 Objects

1.3 Database Design for ORDBMS

1.3.1 Object Identity

1.3.2 Object Identity versus Foreign Keys

1.3.3 Collection Types and ADTs

1.3.4 Extending the ER Model

1.3.5 Using Nested Collections

1.4 Comparing RDBMS, OODBMS, and ORDBMS

1.4.1 RDBMS versus ORDBMS

1.4.2 OODBMS versus ORDBMS

2. Advance Database Management System – Concept and Structure

Introduction

2.1 Spatial Data Management

2.1.1 Types of Spatial Data and Queries

2.1.2 Applications Involving Spatial Data

2.1.3 Introduction to Spatial Indexes

2.1.4 Indexing based on Space-Filling Curve

2.1.5 Grid Files

2.1.6 R Trees: Point and Region Data

2.2 Web Based Systems

2.2.1 Overview of Client Server Architecture

2.2.2 Business Logic – SOAP

2.3 Multimedia Databases

2.4 Mobile Databases

3. Parallel Database

Introduction

3.1 Introduction to Parallel Databases

3.2 Parallel Database Architecture

3.3 Input-Output Parallelism

3.3.1 Partitioning Techniques

3.3.2 Handling of Skew

3.4 Interquery and Intraquery Parallelism, Interoperational and Intraoperational Parallelism

3.4.1 Interquery Parallelism

3.4.2 Intraquery Parallelism

3.4.3 Intraoperation Parallelism

3.4.4 Interoperation Parallelism

3.5   Design of Parallel Systems

4. Distributed Database

Introduction

4.1 Introduction to Distributed Databases

4.2 Distributed DBMS Architectures

4.3 Homogeneous and Heterogeneous Databases

4.3.1 Homogeneous Distributed Database

4.3.2 Heterogeneous Distributed Database

4.4 Distributed Data Storage

4.4.1 Data Replication

4.4.2 Data Fragmentation

4.4.3 Transparency

4.5 Distributed Transactions

4.5.1 System Structure

4.5.2 System Failure Modes

4.6 Commit Protocols

4.6.1 Two-Phase Commit

4.7 Availability

4.7.1 Majority-Based Approach

4.7.2 Read One, Write All Available Approach

4.7.3 Site Reintegration

4.7.4 Comparison with Remote Backup

4.7.5 Coordinator Selection

4.8 Concurrency Control and Recovery in Distributed Databases

4.8.1 Concurrency Control

4.8.2 Recovery

4.9 Directory Systems

5. Knowledge Base System

Introduction

5.1 Integration of Expert in Database Application

5.1.1 Knowledge Discovery in Databases (KDD)

5.1.2 Data Warehouse

5.1.3 Data Marts

5.1.4 Data Mining

5.1.5 On-Line Transaction Processing (OLTP)

5.1.6 On-Line Analytical Processing (OLAP)

5.1.7 Customer Relationship Management (CRM)

5.1.8 Decision Support System (DSS)

6. Data Warehousing

Introduction

6.1 Introduction to Data Warehousing

6.1.1 Data Warehousing

6.1.2 Use of Data Warehousing

6.1.3 Differences between Operational Database Systems and Data Warehouse

6.1.4 Need of a Separate Data Warehouse

6.2 Architecture

6.2.1 Steps for the Design and Construction of Data Warehouses

6.2.2 The Process of Data Warehouse Design

6.2.3 A Three-Tier Data Warehouse Architecture

6.2.4 Data Warehouse Models

6.2.5 Data Warehouse Development

6.2.6 Data Warehouse Back-End Tools and Utilities

6.2.7 Metadata Repository

6.3 Dimensional Data Modeling

6.3.1 Star Schema

6.3.2 Snowflake Schema

6.3.3 Fact Constellation

6.4 OLAP

6.5 OLAP and Data Cubes

6.5.1 OLAP

6.5.2 Data Cubes

6.5.3 Operations of Data Cubes

6.6 Data Preprocessing

6.6.1 Need for Preprocessing

6.6.2 Data Cleaning

6.6.3 Data Integration

6.6.4 Data Transformation

6.6.5 Data Reduction

7. Data Mining

Introduction

7.1 Introduction to Data Mining

7.1.1 Knowledge Discovery from Data (KDD)

7.1.2 Data Mining Architecture

7.2 Introduction to Machine Learning

7.2.1 The Scope of Data Mining

7.2.2 Machine Learning

7.3 Descriptive and Predictive Data Mining

7.3.1 Descriptive Data Mining

7.3.2 Predictive Data Mining

7.4 Outlier Analysis, Clustering

7.4.1 Outlier Analysis

7.4.2 Clustering

7.5 K Means Algorithm

7.6 Classification

7.6.1 Decision Tree

7.6.2 Association Rules

7.6.3 Apriori Algorithm

7.7 Introduction to Text, Bayesian Classifiers

8. Information Retrieval and XML Data

Introduction

8.1 Introduction to Information Retrieval

8.1.1 Vector Space Model

8.1.2 TF/IDF Weighting of Terms

8.1.3 Ranking Document Similarity

8.1.4 Measuring Success: Precision and Recall

8.2 Indexing for Text Search

8.2.1 Inverted Indexes

8.2.2 Signature Files

8.3 Web Search Engines

8.3.1 Search Engine Architecture

8.3.2 Using Link Information

8.3.3 HITS Algorithm

8.4 Managing Text in DBMS

8.4.1 Loosely Coupled Inverted Index

8.5 Data Model for XML

8.5.1 Motivation for Loose Structure

8.5.2 A Graph Model

8.6 Querying XML Data

8.6.1 Path Expression