Evolution of Data Management Systems

Storage that keeps objects' existence independent of the program.

Unique identifier for each object, ensuring it stays the same despite changes.

Unique, system-generated identity for an object in an object-oriented database.

An OID that cannot be changed after creation.

Attribute values without their own OIDs; embedded in objects rather than referenced.

Simple, indivisible data types like integers, strings, Booleans, etc.

Structured data type grouping multiple components (like a composite attribute).

Data structures that hold multiple values such as Set, Bag, List, Array, Dictionary

External programs communicate with objects using messages containing the operation name and parameters.

Attributes directly accessible to users via query languages.

Attributes accessible only through predefined operations, ensuring data protection and integrity.

Objects existing beyond the program's lifespan, stored in the database for future retrieval.

Objects become persistent by associating with existing persistent objects.

Creating new data types based on existing types, promoting reusability in OO databases.

A mechanism where subtypes inherit attributes and operations from supertypes, enabling code reuse.

A Rectangle class can reuse Color attributes from a Shape superclass, inheriting both structure and behavior.

A blueprint for objects, defining both the structure (attributes) and operations that objects of that type can perform.

The description of an object's internal structure, including its attributes and data types.

Defining the actions an object can perform, including their names and how they interact with data.

An operation that creates new instances of an object based on its class definition.

An operation that removes or destroys an object from the system.

Operations that change the values of an object's attributes, affecting its state.

Operations that access and retrieve information stored within an object.

A mechanism that binds together an object's data (attributes) and the operations that manipulate it, protecting internal data and controlling access.

HTML is good for web page formatting, but struggles with managing structured data, especially when this data comes from databases and needs to be dynamically accessed.

XML (Extensible Markup Language) was created to solve the problem of handling structured data in web applications. It allows for the transfer and description of structured data using self-describing tags.

XML documents include attribute names alongside the data, making them understandable even without prior knowledge of the data structure.

Both XML and JSON help structure and exchange data on the web, but differ in syntax and usage. JSON is a simpler alternative to XML, often preferred for its ease of use.

XML documents themselves don't define formatting. Technologies like XSL (Extensible Stylesheet Language) and XSLT (XSL Transformations) handle presentation and style.

XML plays a crucial role in dynamic web applications. It can efficiently transfer information between the user, the web server, and the database, enabling interactive data retrieval and display.

Structured data is organized according to a defined schema, typically stored in relational databases.

Structured data has a defined schema, ensuring consistent data storage with each record following the same structure. Examples include employee databases, financial records, and product inventories.

The conflict between object-oriented programming models and relational database structures, making it difficult to map data between them.

A technique that bridges the gap between object-oriented programming and relational databases, allowing developers to interact with data using object-oriented concepts.

Two strategies for handling increasing data. Scaling up involves using larger machines, while scaling out uses multiple smaller machines.

Distributing data across multiple servers in a database system to improve performance and scalability.

A category of non-relational databases designed for handling large, complex datasets and high user loads, often used in distributed environments.

NoSQL databases typically do not rely on Structured Query Language (SQL) for their query language.

NoSQL databases are built to operate efficiently in clusters of machines, enabling scalability and fault tolerance.

NoSQL databases often allow flexibility in data structures, permitting fields to be added without predefined schemas.

Groups of data in NoSQL databases that are treated as single units, simplifying transactions by limiting them to these aggregates. This approach reduces complexity when working with distributed databases.

A simple data storage model where data is organized as key-value pairs. The key acts as a unique identifier, and the value holds the actual data associated with the key.

Key-value databases can create indexes based on the content of the value, enabling efficient search and retrieval of data based on specific criteria within the stored values.

Key-value stores support various data structures, like lists, sets, and hashes, allowing for flexible data representation and operations like range queries, set operations (union, intersection), and differences.

A popular key-value database known for its versatility. It supports multiple data structures and operations, making it suitable for a wide range of use cases beyond simple key-value storage.

Storing unique session data for web applications effectively using key-value stores. Each session is a single key-value pair, allowing for fast access and manipulation.

Storing user shopping cart data in key-value stores. Each cart is easily represented as a key-value pair, simplifying update and retrieval operations.

Utilizing key-value stores to store frequently accessed data, reducing latency and improving application performance by providing fast data retrieval.