Enhanced Data Models

This topic covers Enhanced Data Models, which extend traditional relational databases to support advanced use cases such as temporal data, multimedia, deductive reasoning, XML-based storage, distributed storage, mobile applications, and specialized domains like GIS and genome data management. Below is an in-depth explanation of each concept.

1. Temporal Database Concepts

A temporal database stores time-varying data, meaning it maintains historical records along with current data.

Key Temporal Features:

Valid Time: The time period when a fact is true in the real world.
Transaction Time: The time when a fact is stored in the database.
Bitemporal Data: Stores both valid time and transaction time.

Applications of Temporal Databases:

Financial systems (historical stock prices)
Medical records (patient history)
Legal records (contract validity)

Example of Temporal Query (SQL)

SELECT EmployeeID, Name, Salary
FROM Employee_Salary
WHERE ValidTime BETWEEN '2023-01-01' AND '2023-12-31';

2. Multimedia Databases

Multimedia databases store and retrieve data types like images, audio, video, and animations.

Challenges:

Large storage requirements
Indexing and searching (e.g., reverse image search)
Real-time streaming performance

Storage and Retrieval Techniques:

Metadata indexing (e.g., tags, descriptions)
Content-based retrieval (e.g., image similarity using histograms)
Compression techniques (e.g., JPEG for images, MP4 for videos)

Example:

YouTube, Spotify, Google Photos use multimedia databases.

3. Deductive Databases

A deductive database applies logical rules to derive new facts from stored data.

Key Components:

Fact Base: Stores existing data.
Rule Base: Contains inference rules.
Inference Engine: Applies rules to derive new facts.

Example (Prolog-like Syntax in Datalog)

ancestor(X, Y) :- parent(X, Y).
ancestor(X, Z) :- parent(X, Y), ancestor(Y, Z).

This rule defines ancestor(X, Z) if X is a parent of Y and Y is an ancestor of Z.

Applications:

AI & Expert Systems (e.g., medical diagnosis)
Network Security (e.g., intrusion detection)

4. XML and Internet Databases

With the rise of web applications, databases must support semi-structured data like XML.

XML Features in Databases:

Hierarchical storage (tree-like structure)
Self-descriptive (tags define structure)
Supports queries using XQuery & XPath

Example XML Storage (SQL Server)

CREATE TABLE Products (
    ProductID INT PRIMARY KEY,
    ProductDetails XML
);

Use Cases:

E-commerce (storing product catalogs)
API communication (RESTful services)
Metadata storage (document databases like MongoDB)

5. Mobile Databases

A mobile database operates in a mobile computing environment with limited connectivity and battery constraints.

Characteristics:

Disconnection support (data caching)
Synchronization with central databases
Lightweight storage (SQLite, Firebase)

Example:

WhatsApp: Uses SQLite for local message storage and synchronizes data with cloud servers.

6. Geographic Information Systems (GIS)

A GIS database stores, manipulates, and analyzes spatial/geographical data.

Key Features:

Spatial indexing (R-trees, Quad-trees)
Geospatial queries (distance, boundaries, intersections)
Visualization on maps (Google Maps, ArcGIS)

Example Query (PostGIS in PostgreSQL)

SELECT name FROM Cities
WHERE ST_Distance(location, ST_GeomFromText('POINT(40.7128 -74.0060)')) < 50;

This finds cities within 50 km of New York.

7. Genome Data Management

A genome database stores and processes biological data such as DNA sequences.

Challenges:

Huge data size (billions of DNA base pairs)
Pattern matching for mutations
High-speed indexing (BLAST, FASTA)

Example Genome Databases:

NCBI GenBank: Stores publicly available DNA sequences.
Ensembl: Used for genome annotation.

8. Distributed Databases

A distributed database is spread across multiple networked sites to improve availability and fault tolerance.

Types of Distributed Databases:

Homogeneous: All nodes use the same DBMS.
Heterogeneous: Different DBMS at different nodes.

Key Features:

Data fragmentation (horizontal, vertical)
Replication (primary-replica, multi-master)
Distributed query processing

Example Systems:

Google Spanner
Apache Cassandra
Amazon DynamoDB

9. Client-Server Architectures

Client-server databases separate data storage (server) from application logic (client).

Types of Client-Server Architectures:

Two-Tier: Direct client-server communication.
Three-Tier: Middleware handles requests (e.g., REST API).

Example Client-Server Model

Client: Web browser (React, Angular)
Middleware: API (Node.js, Django)
Database Server: PostgreSQL, MySQL

Conclusion

Enhanced Database Model	Key Features	Use Cases
Temporal Database	Stores historical data with timestamps	Finance, Medical Records
Multimedia Database	Stores images, videos, audio	YouTube, Spotify
Deductive Database	Uses logic rules to derive new facts	AI, Expert Systems
XML Database	Stores semi-structured XML data	APIs, Web Services
Mobile Database	Works on mobile devices	WhatsApp, Firebase
GIS Database	Handles spatial data	Google Maps, ArcGIS
Genome Database	Stores DNA and biological data	GenBank, Ensembl
Distributed Database	Spans multiple servers	Google Spanner, Cassandra
Client-Server Model	Separates client from database	Web & Mobile Apps

Each of these database models enhances traditional relational databases to support modern computing needs in various domains.

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