Data Models and Data Modeling

• This lesson covers data models, data modeling, business rules, and how data models are classified by level of abstraction.

Data Modeling and Data Models

• Data models are simple representations of complex real-world data structures needed to support a problem domain.
• Data modeling is the iterative, progressive process of creating a specific data model for a problem domain.
• Data modeling is the first step in database design, serving as a bridge between real-world objects and the computer database.
• Model is an abstraction of a real-world object or event.
• Designers, programmers, and end-users see data differently, which can lead to database designs that don't meet end-user needs.
• Communication among designer, programmer, and end-users should be frequent to avoid failures.
• Data modeling clarifies communication by reducing the complexities of database design.

Importance of Data Models

• Data Models are a communications tool.
• Data Models give an overall view of the database.
• Data Models organize data for various users.
• Data Models are an abstraction for the creation of good database

Data Model Basic Building Blocks

• Entity: A unique and distinct object used to collect and store data.
• Attribute: A characteristic of an entity.
• Relationship: Describes an association among entities.
• One-to-many (1:M)
• Many-to-many (M:N or M:M)
• One-to-one (1:1)
• Constraint: A set of rules to ensure data integrity.

Naming Conventions of Entities and Attributes

• Entity names should be descriptive of the objects in the business environment and use terminology familiar to users.
• Attribute names should be descriptive of the data represented by the attribute.
• Proper naming facilitates communication between parties and promotes self-documentation.

Business Rules

• Business rules are a brief, precise description of a policy, procedure, or principle. They:
• Enable defining the basic building blocks.
• Describe the main and distinguishing characteristics of the data.
• Sources of business rules include company managers, policy makers, department managers, written documentation, and direct interviews with end users.
• Reasons for documenting business rules:
• Business rules help standardize a company's view of data.
• Business rules are a communications tool between users and designers.
• Business rules allow the designer to understand the nature, role, and scope of data and business processes.
• The designer uses the rules to develop appropriate relationship participation rules and constraints and create an accurate data model.
• Nouns translate into entities, and verbs translate into relationships among entities.
• Relationships are bidirectional.
• Asking how many instances of B are related to one instance of A, and vice-versa, helps identify the relationship type.

Standard Database Concepts

• Schema: A conceptual organization of the entire database as viewed by the database administrator.
• Subschema: The portion of the database seen by application programs that produce the desired information from the data within the database.
• Data Manipulation Langue (DML): and environment in which data can be managed and is used to work with the data in the database
• Schema Data Definition Language (DDL): enables the database administrator to define the schema components.

Evolution of Data Models

• Hierarchical data model (1960s)
• Difficult to represent M:N relationships (hierarchical only).
• Structural level dependency.
• No ad hoc queries (record-at-a-time access)
• Network data model (1969s)
• Access path predefined (navigational access).
• Relational data model (1970s)
• Conceptual simplicity (structural independence).
• Provides ad hoc queries (SQL)
• Set-oriented access.
• Entity Relationship data model (1976s)
• Easy to understand (more semantics).
• Limited to conceptual modeling (no implementation component).
• Semantic data models (1978s)
• More semantics in data model.
• Support for complex objects.
• Object-oriented data model (1985s)
• Inheritance (class hierarchy).
• Behavior.
• Extended Relational
  • Unstructured data (XML.
• Big Data data model (2009s)
• XML data exchanges.
• Addresses Big Data problem.
• NoSQL: Less semantic in data model.
• Based on schema-less key-value data model.
• Suited for large sparse data stores.

Hierarchical Models

• It manages large amounts of data for complex manufacturing projects.
• Is represented by an upside-down tree which contains segments.
• Segments are equivalent to a file system's record type.
• Depicts a set of one-to-many (1:M) relationships.

Network Models

• Network Models represent relationships.
• Network Models improve database performance and impose a database standard.
• Network Models depicts both one-to-many (1:M) and many-to-many (M:N) relationships.

Relational Model

• Produces an automatic transmission database that replaced standard transmission databases.
• The Relatioal Model is based on a relation.
• Relation or table is a matrix composed of intersecting tuple and attribute.
• Tuple represents rows.
• Atribute rerpresents columns.
• Describes a precise set of data manipulation constructs.

Entity Relationship model features

• Graphical representation of entities and their relationships in a database structure.
• Entity relationship diagram (ERD). Uses graphic representations to model database components.
• Entity instance or entity occurrence are rows in the relational table.
• Connectivity is a term used to label the relationship types.

Object-Oriented Data Model (OODM)

• Is an object-oriented database management system, this based on OODM.
• Object contains data and their relationships with operations that are performed on it.
• Object is basic building block for autonomous structures.
• Object is abstraction of real-world entity.
• Attributes describe the properties of an object.
• Class: Collection of similar objects with shared structure and behavior organized in a class hierarchy Class hierarchy: Resembles an upside-down tree in which each class has only one parent
• Inheritance: Object inherits methods and attributes of parent class
• Unified Modeling Language (UML) Describes sets of diagrams and symbols to graphically model a system.

Object/Relational and XML

• Extended relational data model (ERDM)
• Supports OO features and complex data representation.
• Object/Relational Database Management System (O/R DBMS)
• Based on ERDM, focuses on better data management.
• Extensible Markup Language (XML)
• Manages unstructured data for efficient and effective exchange of all data types.

Relational Database Management System (RDBMS)

• Performs basic functions provided by the hierarchical and network DBMS systems.
• Makes the relational data model easier to understand and implement.
• Hides the complexities of the relational model from the user.

SQL-Based Relational Database Application

• End-user interface allows end user to interact with the data.
• Collection of tables stored in the database, and each table is independent from another.
• Rows in different tables are related based on common values in common attributes
• SQL engine executes all queries.

Big Data

• Big data aims to find new and better ways to manage large amounts of web and sensor-generated data.
• Big data Provides high performance and scalability at a reasonable cost.
• The characteristics of Big Data inclue volume, velocity, and variety.

Big Data Challenges

• Volume does not allow the usage of conventional structures.
• It can be expensive to store big data.
• OLAP tools proved inconsistent dealing with unstructured data.

Big Data New Technologies

• Hadoop
• Hadoop Distributed File System (HDFS)
• MapReduce
• NoSQL

NoSQL Databases

• NoSQL Databases is not based on the relational model
• Support distributed database architectures
• Provide high scalability, high availability, and fault tolerance
• Support large amounts of sparse data
• Geared toward performance rather than transaction consistency
• Store data in key-value stores

Degrees of Abstraction

• Data abstraction is the idea that a database design begins with a high-level view and as it approaches implementation level, the level of detail increases.
• American National Standards Institute (ANSI) Standards Planning and Requirements Committee (SPARC) defined a framework for data modeling based on degrees of data abstraction - ANSI/ SPARC Architecture.

Degrees of Abstraction levels

• External level - users see the data in the form of rows and columns, and will have different views based on levels of access rights.
• Conceptual level - conceptual model represents a global view of the entire database by the entire organization, also known as the Designer's View
• Internal level - representation of the database as "seen" by the DBMS.
• Physical level - describes how the data is actually stored in the physical memory like magnetic tapes, hard disks etc.
• The external model represents the end users' view of the data environment.
• ER diagrams are used to represent the external views.
• External schema: Specific representation of an external view.
• Conceptual schema provides a Basis for the identification and high-level description of the main data objects, as well as has a Macro-level view of data environment. Is software and hardware independent in logical design. the Task to complete creating a conceptual data model.
• Internal schema is a Specific representation of an internal model.
• Logical independence: Changing internal model without affecting the conceptual model.
• The physical model operates at the lowest level of abstraction and Provides the definition of physical storage and data access methods. and Described the way data are saved on storage media such as disks or tapes. Requires no physical-level details because the Relational model is. aimed at logical level. Physical independence: Changes in physical model do not affect internal model.