Information Systems, Data and Databases

• The notes cover a review of database concepts.

Information System Concepts

• It is a set of interrelated components collecting, manipulating, and disseminating data to provide feedback meeting an objective.
• The components consist of data, hardware, software, telecommunication, people, and procedures.

Four Stages of Processing

• Input: Data is collected and introduced into a system.
• A transaction qualifies as a business input
• A transaction is a business event usually entered as input.
• Data Processing: Calculations are performed on the input data.
• Output: The result produced by the Information System.
• Storage: Large amounts of data are stored on discs.

Data and Information

• Data is stored representations of meaningful objects and events.
• Data can be either structured (numbers, text, dates) or unstructured (images, video, documents).
• Information is data processed to increase knowledge for the user.
• Data can be presented in context or summarized to become information.

Metadata

• Definitively, Metadata is data describing the properties and context of user data.
• Properties to describe include data names, length/size, and allowable values.
• Context includes source, ownership, and usage.

Database System Environment

• A Database System is an organization of components defining and regulating data management within a database environment.
• The five major parts are hardware, software, people, procedures, and data.

Data Models

• Data models consist of data descriptions of the structure of a database that provides the abstraction means.

Basic Building Blocks of Data Models

• Entity: A person, place, thing, or event about which data will be collected and stored.
• Attribute: Characteristics of an entity.
• Relationship: Describes an association among entities.

Three Types of Relationships

• One-to-Many: One entity instance associates with many instances of a related entity.
• Many-to-Many: An entity associates with many occurrences of a related entity, and one occurrence of the related entity associates with many occurrences of the first entity.
• One-to-One: One entity instance associates with only one instance of the related entity.

Categories of Data Model

• High-Level/Conceptual Data Model: Gives concepts close to how users perceive data.
• Representational/Logical Data Model: Concepts understandable by end-users, but related to data organization within the computer.
• Low-Level/Physical Data Model: Concepts detailing how data stores in the computer.
• Creating an Entity-Relationship Diagram (ERD) for business data requirements leads to a Conceptual Design.
• Transforming the ERD to relational models involves Logical Design.
• Creating the database based on a DBMS involves Physical Design.

Business Rule

• Business rules define or constrain aspects of the business.
• Well-written business rules define entities, attributes, relationships, and constraints.
• They describe policies, procedures, or principles within an organization.
• Business rules assert business structure, control behavior, are user-friendly, and automate via DBMS software.

Importance of Business Rules

• They standardize a company’s view of data.
• It acts as a communication tool between users and designers.
• The designer can understand the nature, role, and scope of data with business rules.
• It allows designers to understand business processes and develop appropriate relationship participation rules, develop constraints, and create an accurate data model.

A Good Business Rule Is

• Declarative, stating what, not how.
• Precise, with clear, agreed-upon meaning.
• Atomic, existing as one statement.
• Consistent, internally and externally.
• Expressible in structured, natural language
• Distinct and non-redundant.
• Business-Oriented and understood by business people.

Entity Relationship Diagram

• A data modeling technique that is a graphical representation of the entities and the relationships between entities within an information system.
• Three basic elements in ER Models: Entity, Attributes, & Relationships
• Entities are the "things" about which information is sought, a person, object, place, or event for which data is collected.
• Attributes are the data collected about the entities.
• Relationships provide the structure needed to draw information from multiple entities or the link between entities.
• An Entity may be an object with physical or conceptual existence.

Types of Attributes

• Key Attribute: A unique, distinguishing characteristic of the entity.
• Composite Attribute: Attributes divisible into smaller sub-attributes.
• Required Attribute: Attributes that must have a value or cannot left empty.
• Optional Attribute: Can be left Empty.
• Single-Valued Attribute: An attribute with one value
• Multi-valued Attribute: More than one value
• Derived Attribute: An attribute whose value is calculated from another attribute, and needs not be physically stored

Degree of Relationships

• This covers the number of entity types participating.
• Unary Relationship: When there is one entity related to another of the same entity type.
• Binary Relationship: Entities of two different types relate to each other.
• Ternary Relationship: Three different type Entities are related to each other.
• N'ary Relationship: Entities of three or more different types have relations with each other.

Cardinality of Relationship

• Describes how many entity instances can exist in the relationship.
• One-to-one: Each entity will have exactly one related entity.
• One-to-many: One side can have many related entities, but the other can have one related entity as a maximum.
• Many-to-many: Entities on each side can have multiple related entities.

Cardinality Constraints

• This is effectively the number of instances of one entity that can or be associated with each instance of another entity.
• Minimum Cardinality:
• Optional if zero.
• Mandatory if one or more.