Data Modeling Concepts

Data Models

• Also known as entity-based or object-based data models
• Three types of data models:
  • Implementation/representational/logical data models
  • Physical/low-level data models
  • Conceptual/external/internal data models

ANSI/SPARC Three-Schema Architecture

• Classified data models according to their degree of abstraction in the 1970s
• Three levels of abstraction:
  • Internal schema: describes physical storage structures and access paths
  • Conceptual schema: describes the structure and constraints for the whole database for a community of users
  • External schema: describes the various user views

Mappings among Schema Levels

• Needed to transform requests and data
• Programs refer to an external schema and are mapped by the DBMS to the internal schema for execution

Object-Relational Data Models

• Extend the relational data model by including object orientation and constructs to deal with added data types
• Allow attributes of tuples to have complex types, including non-atomic values such as nested relations
• Preserve relational foundations, in particular the declarative access to data, while extending modeling power
• Provide upward compatibility with existing relational languages

Emerging Data Models: Big Data and NoSQL

• Big Data: find new and better ways to manage large amounts of Web-generated data and derive business insight from it
• Characteristics of Big Data:
  • High performance and scalability at a reasonable cost
  • Relational approach may not always match the needs of organizations with Big Data challenges
• NoSQL databases:
  • Not based on the relational model
  • Support distributed database architectures
  • Provide high scalability, high availability, and fault tolerance
  • Support very large amounts of sparse data
  • Geared toward performance rather than transaction consistency

Key-Value Data Model

• Two data elements: key and value
• Every key has a corresponding value or set of values

Sparse Data

• Number of attributes is very large
• Number of actual data instances is low

Eventual Consistency

• Updates will propagate through the system; eventually all data copies will be consistent

Evolution of Data Models

• Each new data model capitalized on the shortcomings of previous models
• Common characteristics:
  • Conceptual simplicity without compromising the semantic completeness of the database
  • Represent the real world as closely as possible
  • Representation of real-world transformations (behavior) must be in compliance with consistency and integrity characteristics of any data model

Data Models and Internet

• Using internet as a prime business tool has drastically changed the role and scope of the database market
• Characteristics of successful “Internet age” databases:
  • Flexible, efficient, and secure Internet access
  • Support for complex data types (video, audio) and relationships
  • Interfacing with multiple data sources and structures
  • Relative conceptual simplicity to make database design and implementation less cumbersome
  • A powerful DBMS graphical user interface (GUI) to help make the DBA’s job easier

Categories of Data Models and Data Abstraction Levels

• Categories include:
  • Conceptual/high-level/semantic data models
  • Network model
  • Hierarchical model

Conceptual Data Models

• Provide concepts that are close to the way many users perceive data
• Created for the following objectives:
  • Represent complex data relationships more effectively than the hierarchical model
  • Improve database performance
  • Imposes a database standard

Network Model

• Resembles hierarchical model
• Collection of records in 1:M relationships
• Relationship is called a Set: Set is composed of at least two record types
• Owner: Equivalent to the hierarchical model’s parent
• Member: Equivalent to the hierarchical model’s child