Database Systems

The ISE1202 Database Systems course equips students with a solid understanding of relational database fundamentals, including data modeling, logical design, and SQL-based manipulation. Learners will develop skills in designing and creating databases using entity-relationship diagrams and relational DBMS tools, while analyzing data requirements and relationships. The course also emphasizes data integrity, security, and ethical practices in database implementation.

SQL CODES

SELECT, INSERT, UPDATE, DELETE, CREATE, ALTER, DROP, TRUNCATE, RENAME, GRANT, REVOKE, COMMIT, ROLLBACK, SAVEPOINT, BEGIN TRANSACTION, END TRANSACTION, DESCRIBE, SHOW, EXPLAIN, USE, SET, DROP DATABASE, CREATE DATABASE, USE DATABASE, VACUUM, ANALYZE.

File Systems, Database approach.

File systems manage data storage at the level of individual files, while the database approach organizes data into structured formats, optimizing for retrieval and manipulation through a database management system (DBMS).

Fundamental Concepts of DBMS

Fundamental concepts of a Database Management System (DBMS) include data modeling, data integrity, data security, transaction management, and query processing, which collectively ensure effective management and retrieval of data.

Components of database systems

The essential elements that make up a database system, including the database schema, DBMS, applications, and data models that work together to manage data effectively.

Roles in the database environment.

Roles in the database environment refer to the various responsibilities and functions of individuals or systems involved in managing databases, such as database administrators, developers, and users, each playing a key role in ensuring data integrity, security, and efficiency.

DBMS functions and components.

Functions: Data storage, retrieval, and update, user access control, data integrity and security, transaction management, backup and recovery, data dictionary management, concurrency control.

Components: Database engine, database schema, query processor, storage manager, transaction manager, metadata catalog, user interface.

Advantages and disadvantages of using databases.

The advantages of using databases include improved data management, increased efficiency, enhanced security, and better data integrity. Disadvantages can include complexity in setup and maintenance, potential performance issues, and costs associated with database management systems.

Data Models, Schemas, and Instances.

Data models define the structure of data, schemas represent the organization of data within a database, and instances are the actual data stored that conforms to both the model and schema.

DBMS Architecture and Data Independence.

DBMS architecture refers to the structure of a database management system, outlining the types and levels of data abstraction. Data independence means that changes in data storage or structure do not affect the application programs that manipulate the data.

Database Languages and Interfaces.

Database languages includes SQL, which is used to interact with data through queries, while interfaces provide ways for users and applications to access and manipulate data within a database.

The Database System Environment.

The database system environment refers to the overall setting in which a database operates, including hardware, software, data, procedures, and users that interact with the database system.

Classification of Database Management Systems.

Classification of database management systems involves categorizing them based on various criteria such as data model (relational, hierarchical, object-oriented), use case (transactional, analytical), and scale (centralized, distributed). This classification helps users choose the right system for specific needs.

Using High-Level Conceptual Data Models for Database Design.

This approach involves creating abstract representations of data using models like Entity-Relationship (ER) diagrams, which help in visualizing and structuring the data requirements before implementing the database.

An Example Database Application.

An example database application is a software program that utilizes a database management system to manage data. These applications can range from simple contact management systems to complex enterprise resource planning (ERP) systems that handle a wide variety of business processes.

Database application life cycle.

The database application life cycle refers to the series of stages involved in the development and management of a database application, including requirements gathering, design, implementation, testing, deployment, and maintenance.

Overview of the design process.

The overview of the design process involves a systematic approach to creating a database, including initial planning, conceptual design, logical design, physical design, and implementation, ensuring that the final database meets the user requirements.

Logical and physical database design.

Logical database design focuses on the structure of the database including tables, columns, and relationships, while physical database design involves the actual storage and access methods used on the database management system.

Design strategies and methodologies.

Top-down design, bottom-up design, centralized design, distributed design, conceptual design, logical design, physical design, structured methodology, object-oriented methodology, agile methodology, prototyping, data-driven design.

Entity Types, Entity Sets, Attributes, and Keys.

Entity types represent categories of objects in the database, entity sets are collections of similar entities, attributes are the properties that define entities, and keys are attributes or sets of attributes that uniquely identify an entity within its entity set.

UML design and UML Notation.

UML (Unified Modeling Language) design is a standardized modeling language used to visualize the design of a system. UML notation encompasses a set of diagrams that represent the structure and behavior of systems.

Super class and sub class, Inheritance.

Super Class: A general entity that contains common attributes and relationships shared by multiple sub classes.

Sub Class: A more specific entity that inherits attributes and relationships from a super class and may also have additional, unique properties.

Inheritance: A concept where a sub class automatically acquires the attributes and relationships of its super class, promoting reusability and a hierarchical structure in data modeling.

Relationships, relationship Types, Roles, and Structural Constraints.

Relationships: Associations between two or more entities that define how they are connected in a database.

Relationship Types: The category of a relationship, such as one-to-one (1:1), one-to-many (1:M), and many-to-many (M:M), based on how entities are linked.

Roles: The part each entity plays in a relationship, often labeled to clarify meaning (e.g., "student" and "advisor" in a student-advisor relationship).

Structural Constraints: Rules that limit the relationship, including cardinality (number of instances) and participation (whether entity participation is optional or mandatory).

Weak Entity Types, Refining the ER Design for the COMPANY Database.

Weak Entity Types: Entities that do not have a primary key of their own and rely on a related strong entity (via a foreign key and a partial key) for identification. They are dependent on a strong entity and are typically connected by an identifying relationship.

Refining the ER Design for the COMPANY Database: The process of improving the initial entity-relationship (ER) diagram by identifying weak entities, resolving redundant or unclear relationships, ensuring proper use of generalization/specialization, and optimizing attributes and constraints for accuracy and efficiency in representing real-world business requirements.

ER Diagrams, Naming Conventions, and Design Issues.

ER Diagrams (Entity-Relationship Diagrams): Visual representations of entities, their attributes, and relationships in a database system, used to design and model data structures.

Naming Conventions: Standard rules for naming entities, attributes, and relationships (e.g., using singular nouns, capitalizing entity names, and avoiding special characters) to maintain clarity and consistency.

Design Issues: Common challenges in ER modeling such as handling redundant data, representing complex relationships, choosing between attributes vs. entities, generalization vs. specialization, and ensuring normalization and data integrity.

Relational Model Concepts, Relational Constraints and Relational Database Schemas.

Relational Model Concepts: A framework for organizing data into tables (relations) consisting of rows (tuples) and columns (attributes), where each table represents an entity type.

Relational Constraints: Rules enforced on data in relational tables, including domain constraints (valid data types), key constraints (primary and foreign keys), entity integrity (no null primary keys), and referential integrity (foreign keys must match primary keys in related tables).

Relational Database Schemas: The logical structure of a relational database, defining tables, their attributes, keys, and relationships, serving as a blueprint for how data is organized and accessed.

Mapping of ER models to Relational Models.

The process of translating an entity-relationship (ER) schema into a relational schema, which involves creating tables for entities and relationships, defining primary and foreign keys, and ensuring the integrity of the data model.

Update Operations and Dealing with Constraint Violations.

Update operations in databases involve modifying existing data records, which can lead to constraint violations when changes do not adhere to set rules and relationships, necessitating checks for integrity and validity.

Basic Relational Algebra Operations (unary, set, join, division).

A set of operations that manipulate relations in a relational database, including basic operations such as selection, projection, union, intersection, and most importantly, join and division, used to retrieve and combine data.

The importance of a good schema design.

A good schema design is crucial for ensuring data integrity, optimizing query performance, and facilitating ease of maintenance and scalability in database systems.

Problems encountered with bad schema designs.

Problems with bad schema designs can include data redundancy, inefficient queries, difficulty in maintenance, and increased risk of inconsistencies, which ultimately hinder the performance and integrity of the database.

Update, deletion and insertion anomalies

These are issues that occur when a database schema is poorly designed, leading to redundancy and inconsistency. They can cause problems during data modification, as changes may not propagate correctly or may result in the loss of data integrity.

Motivation for normal forms, Functional Dependencies.

Are principles used to organize databases effectively to minimize redundancy and enhance integrity. Normal forms aim to eliminate anomalies that can arise from poor schema design by structuring data based on functional dependencies.

Normal Forms Based on Primary Keys, General Definitions of Second and Third Normal Forms, BCNF.

Normal Forms Based on Primary Keys: Guidelines for organizing data within relational databases to reduce redundancy and dependency, ensuring efficient storage and data integrity.

Second Normal Form (2NF): A table is in 2NF if it is in 1NF (First Normal Form) and all non-key attributes are fully functionally dependent on the primary key, eliminating partial dependency.

Third Normal Form (3NF): A table is in 3NF if it is in 2NF and all attributes are functionally dependent only on the primary key, eliminating transitive dependency.

Boyce-Codd Normal Form (BCNF): A stricter version of 3NF, where every determinant (an attribute or set of attributes that determines another attribute) is a candidate key, ensuring no exceptions to the rules of functional dependencies.

Overview of database languages, Data Definition, Constraints, and schema changes on SQL.

Database Languages: SQL (Structured Query Language) is the standard language used to interact with relational databases, comprising different components like DDL (Data Definition Language), DML (Data Manipulation Language), DCL (Data Control Language), and TCL (Transaction Control Language).

Data Definition (DDL): SQL commands used to define and manage database schema objects, such as creating, altering, and deleting tables and other database structures (e.g., CREATE, ALTER, DROP).

Constraints: Rules applied to data in SQL to ensure data integrity, including primary key, foreign key, unique, check, and not null constraints, which govern how data can be entered and related.

Schema Changes in SQL: Modifying the database schema by adding or removing tables, altering columns, and updating constraints, typically done with SQL commands like ALTER TABLE or DROP COLUMN to reflect evolving requirements.

Data Definition Language, Data Manipulation Language and Data Control Language

Three primary components of SQL used for managing database structures, manipulating data, and controlling access permissions. DDL is used for defining schema, DML is used for data operations, and DCL governs access rights.

Basic Queries in SQL; More Complex SQL Queries; Insert, Delete, and update Statements in SQL.

Basic Queries in SQL: Simple commands used to retrieve data from a database, typically using the SELECT statement to filter and display specific records.
More Complex SQL Queries: Advanced queries that involve joining multiple tables, subqueries, and aggregate functions to perform intricate data analysis.
Insert, Delete, and Update Statements in SQL: SQL commands used to modify data within tables; INSERT adds new records, DELETE removes existing records, and UPDATE alters existing record values.

Views (Virtual tables) in SQL; Specifying General Constrains as Assertion; Additional Features of SQL.

Database objects that present data from one or more tables based on a predefined query, allowing users to simplify complex queries and enhance security by restricting access to original table data. General constraints can be specified using assertions to enforce rules on the data integrity across multiple tables.

Introduction to transaction processing, integrity constraints.

Fundamental concepts in database management that ensure data validity and consistency. Transaction processing involves managing changes made by transactions, while integrity constraints enforce rules that maintain the accuracy and reliability of data.

ACID properties of a transaction.

The ACID properties of a transaction refer to the set of principles that guarantee reliable processing of database transactions. This includes Atomicity (transactions are all-or-nothing), Consistency (transactions bring the database from one valid state to another), Isolation (transactions do not interfere with each other), and Durability (once a transaction is committed, it remains so, even in the event of a system failure).

Transaction and system concepts.

The set of principles that ensure reliable processing of database transactions, including Atomicity, Consistency, Isolation, and Durability, which together guarantee that transactions are processed reliably and any errors can be managed effectively.

Transaction properties, Concurrency and integrity controls, Locking Techniques for Concurrency Control.

These concepts refer to the mechanisms and principles used in database management systems to ensure safe and effective transaction processing while allowing multiple transactions to occur simultaneously without compromising data integrity.

Recovery concepts, Recovery Techniques.

Recovery concepts in database systems refer to the strategies and methods used to restore a database to a consistent state after a failure. Recovery techniques include logging, shadow paging, and checkpointing, which help ensure data integrity and availability.

Recoverability, concurrency management, recovery facilities, transaction logs and recovery techniques.

These refer to features and methods in database systems that ensure transactions are processed in an error-free manner while allowing multiple concurrent transactions. This includes the use of recovery techniques such as transaction logs and concurrency management strategies to maintain data integrity.

Types of System Failures.

Hardware failures, software failures, network failures, human errors, power failures, security failures.

Security, Audit trail and encryption.

These are methods used to protect database integrity and privacy. Security measures prevent unauthorized access, audit trails track changes to data, and encryption secures data from being read by unauthorized users.

Basic Concepts and design.

These refer to the fundamental principles and structured approaches in database system architecture, including data modeling, normalization, and the relationship between different data entities.

The DDBMS and its facilities.

A Distributed Database Management System (DDBMS) allows data storage and management across multiple locations while providing users with a unified view of the data. It includes facilities for data distribution, replication, and transaction management to ensure consistency and reliability.

Data distribution in a DDBMS.

The method of storing and managing database data across multiple locations or nodes in a Distributed Database Management System (DDBMS), enhancing performance and availability.

Distributed transactions and distributed transaction management.

These are processes that ensure the coordination and execution of transactions across multiple distributed databases. They maintain consistency and integrity of data by managing commits and rollbacks effectively.

Distributed deadlock management.

The process of identifying and resolving deadlocks that occur in distributed transactions within a DDBMS. It ensures that transactions do not remain indefinitely in a waiting state, thus maintaining system performance and reliability.

Distributed concurrency control and distributed database recovery.

These processes help manage simultaneous operations across multiple databases to maintain data integrity and availability. They ensure that recoveries from failures or conflicts are handled systematically without compromising data consistency.

Security issues in DDBMS.

Security issues in distributed database management systems (DDBMS) involve challenges related to unauthorized access, data breaches, and maintaining data privacy across various networked locations. These issues require robust authentication, encryption, and regulatory compliance measures to safeguard sensitive information.

Seminars and case studies on emerging technologies related to the course.

Seminars and case studies on emerging technologies aim to explore the latest advancements and applications in database systems. They provide practical insights and promote an understanding of how these innovations can enhance data management and analysis.

Data warehousing and Data mining. Data Marts. Tools and technologies

These concepts involve the processes of collecting, storing, and analyzing large volumes of data to derive insights and support decision-making. Data warehousing refers to the centralized repositories, while data mining focuses on extracting patterns and knowledge through analytical methods.

Hadoop, Big Data and Big Data characteristics.

Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. The characteristics of Big Data include volume, velocity, variety, veracity, and value, which highlight the challenges in managing and analyzing massive and diverse data sources.

Other database types (No SQL, etc.)

Other database types refer to various data management systems that do not use the traditional relational database model. This includes NoSQL databases, which are designed to handle unstructured data, and provide scalability and flexibility for modern applications.