2 - Data Models

Database design

focuses on how the database structure will be used to store and manage end-user data.

Data Modeling

the first step in designing a database, refers to the process of creating a specific data model for a determined problem domain.

Data model

is relatively simple representation, usually graphical, of more complex real-world data structures.

Basic building blocks for data model are the following:

• Entity

• Attribute

• Relationship

Entity

It is a person, place, thing, or event about which data will be collected and stored.

Attribute

It is a characteristic of an entity.

Relationship

It describes an association among entities.

Three (3) types of relationships:

• One-to-one (1:1) relationship

• One-to-many (1:M) relationship

• Many-to-many (M:M) relationship

The types of structures that it should use, and the technology that would be used to implement these structures:

1. Hierarchical Model

2. Network Model

3. Relational Model

4. Entity Relationship Model

Hierarchical Model

- It was developed in the 1960s to manage large amounts of data for complex manufacturing projects.

- The model's basic logical structure is represented by an upside-down tree. It contains levels, or segments. Segment is the equivalent of a file system's record type.

Network Model

It was created to represent complex data relationships more effectively than the hierarchical model, to improve database performance, and to impose a database standard.

the standard database concepts that emerged with the network model are still used by modern data models:

o Schema

o Subschema

o Data Manipulation Language (DML)

o Data Definition Language (DDL)

Schema

It is the conceptual organization of the entire database as viewed by the database administrator.

Subschema

It defines the portion of the database by the application programs that actually produce the desired information from the data in the database

Data Manipulation Language (DML)

It defines the environment in which data can be managed.

Data Definition Language (DDL)

It allows the database administrator to define the schema components.

Relational Model

- It was introduced in 1970 by E. F. Codd of IBM.

- The relational model represented a major breakthrough for both users and designers.

- The foundation of mathematical concept is known as a relation.

Entity Relationship Model

- It was introduced in 1976 by Peter Chen.

- The graphical representation of entities and their relationships in a database structure quickly became popular, because it complemented the relational data model concepts.

- The relational data model and ERM are combined to provide the foundation for tightly structured database design.

Object-Oriented Model

- Increasingly complex real-world problems demonstrated a need for a data model that more closely represented the real world. In the Object-Oriented Data Model (OODM), both data and its relationships are contained in a single structure known as an object. In turn, the OODM is the basis for the Object-Oriented Database Management System (OODBMS).

- The OODM is said to be a semantic data model because it indicates meaning.

Unified Modeling Language (UML)

is a language based on Object-Oriented concepts that describes a set of diagrams and symbols you can use to graphically model a system

Extensible Markup Language (XML)

A metalanguage used to represent and manipulate data elements.

Emerging Data Models:

Big Data and NoSQL

Big Data

It refers to a movement to find new and better ways to manage large amounts of web and sensor-generated data and derive business insight from it, while simultaneously providing high performance and scalability at a reasonable cost.

John Mashey, Silicon Graphics scientist

He seems to have been first used in a computing framework

Douglas Laney data analyst from the Gartner Group

who first described the basic characteristics of Big Data databases

Big Data databases:

o Volume

o Velocity

o Variety

Volume

It refer to the amounts of data being stored.

Velocity

It refers not only to the speed with which data grows but also to the need to process this data quickly in order to generate information and insight

Variety

It refers to the fact that the data being collected comes in multiple different data formats.

NoSQL

It is a large-scale distributed database system that stores structured and unstructured data in efficient ways

The following are the general characteristics of NoSQL databases:

o They are not based on the relational model and SQL, hence the name NoSQL.

o They support distributed database architectures.

o They provide high scalability, high availability, and fault tolerance.

o They support very large amounts of sparse data.

o They are geared toward performance rather than transaction consistency.

NoSQL supports distributed database architecture

One of the big advantages of NoSQL databases is that they generally use a distributed database node.

NoSQL supports very large amounts of sparse data

NoSQL databases can handle very high volumes of data

NoSQL provides high scalability, high availability, and fault tolerance

True to its web origins, NoSQL databases are designed to support web operations, such as the ability to add capacity in the form of nodes to the distributed database when the demand is high, and to do it transparently and without downtime

Most NoSQL databases are geared toward performance rather than transactions consistency

One of the biggest problems if very large distributed databases are enforcing data consistency.

American National Standards Institute (ANSI) Standards Planning and Requirements Committee (SPARC)

defined a framework for data modeling based on degrees of data abstraction

three (3) levels of data abstraction:

external, conceptual, and internal

External Model

- It is the end user's view of the data environment.

- It refers to people who use the application programs to manipulate the data and generate information.

- ER diagrams will be used to represent the external views.

external schema

A specific representation of an external view

Conceptual Model

- It represents a global view of the entire database by the entire organization.

Also known as a conceptual schema

it is the basis for the identification and high-level description of the main data objects.

Internal Model

- It is the representation of the database as "seen" by the DBMS.

- It requires the designer to match the conceptual model's characteristics and constraints to those of the selected implementation model.

Internal schema

depicts a specific representation of an internal model, using the database constructs supported by the chosen database.

Physical Model

operates at the lowest level of abstraction, describing the way data is saved on storage media such as magnetic, solid state, or optical media. requires the definition of both the physical storage devices and the (physical) access methods required to reach the data within those storage devices, making it both software and hardware dependent