Databases and Data Management

• A database is an organized collection of logically related data.
• Data refers to stored representations of meaningful objects and events, which can be structured (numbers, text, dates) or unstructured (images, video, documents).
• Information is data processed to increase knowledge for the user.
• Metadata is data about data, including characteristics describing attributes of an entity, such as name, data type, size, format, and special characteristics of files or relations.

Repository

• A repository includes a data dictionary.
• It's where all metadata resides for a database.
• It may contain information on the relationships between files or tables.

Data in Context

• Graphical displays transform data into useful information for managerial decision-making and interpretation.
• Metadata includes descriptions of data properties or characteristics like data types, field sizes, allowable values, and data context.

File Processing System

• It involves a collection of files and programs that access or modify these files.

Disadvantages of File Processing System

• Program data dependence means all programs maintain metadata for each file they use.
• Data Duplication exists between different systems or programs using separate copies of the same data.
• Limited Data Sharing occurs without centralized control, leading to lengthy development times.
• Programmers often must design their own file formats.

Database Solutions

• It's a central repository of shared data managed by a controlling agent.
• Data is stored in a standardized, convenient form.
• It utilizes a Database Management System (DBMS).

Advantages of Database Approach

• Program-data independence allows separate data descriptions from application programs.
• Planned data redundancy improves data consistency and data sharing.
• It also increases productivity of application development and enforce standards.
• Improved data quality along with data accessibility and responsiveness.
• It leads to reduced program maintenance and improved decision support.

Elements of Database Approach

• Database applications are application programs to perform database activities (create, read, update, delete) for database users
• Data models are graphical systems used to capture the nature and relationships among data.
• Relational databases use tables representing entities and primary/foreign keys for relationships.

Internet Technologies

• Networks and telecommunications
• Distributed database
• Client-server
• 3-tier architectures

Components of Database Environment

• CASE tools
• Repository
• DBMS
• Database
• Application programs
• User Interface
• Data/Database Administrators
• System Developers
• End Users

Sample Database Applications

• Banking
• Airlines
• Universities
• Sales
• Online retailers
• Manufacturing
• Human resources
• Telecommunication

Database Development Process

• Planning – Enterprise modeling
• Planning -Conceptual data modeling
• Analysis- Conceptual data modeling
• Design- Logical database design
• Design- Physical database design and definition
• Implementation
• Maintenance

Model

• It's an abstraction of a real-world object or event, depicted in a diagram showing tables and relationships.

Data Modeling

• It involves discovering and analyzing organizational and user's data requirements.
• It is based on policies, meetings, procedures, system specifications.
• It identifies what data is important and what data should be maintained or stored.

Entity-Relationship Diagram (ERD)

• It illustrates the conceptual and logical structure of databases.
• Entities, attributes, and relationships are identified to construct a model.
• An Entity refers to a table in the database.
• An Attribute describes detailed information about an entity.
• Relationships establish associations between entities in a data model, indicated by connections between two or more entities.
• Cardinality refers to the number of entity occurrences associated with the number of occurrences in another entity.
• It can be one-to-one (1:1), one-to-many (1:M), or many-to-many (M:M).

Identifier

• An identifier is an attribute that uniquely identifies instances, represented by underlining or as a primary key (PK) in the database.
• Composite identifiers consist of two or more attributes.

Identifying Entities

• Study the problem domain at hand.
• Write a summary paragraph for the problem domain, considering what data needs to be stored, and queries and reports that need to be processed.
• Find nouns from the summary paragraph, which can be people, places, objects, or events.
• Tangible nouns include customer, product, and machine tool.
• Intangible nouns include order and accounting receivable.
• Proper nouns are not good candidates.

Rules in Identifying Entities

• Every entity type should be important in its own right within the problem domain.
• If an object type (noun) has only one property to store, then it is an attribute of another entity.
• If an object type has only one data instance, then do not model it as an entity type.
• If a relationship needs to have a unique identifier, model it as an entity type.

Relationship Types

• Existence relationship: An example of this is employee has children
• Functional relationship with example entities and flow
• Event relationship: customer places order

Classes of Attributes

• Simple attributes cannot be subdivided, like age, gender, or marital status.
• Composite Attributes can be subdivided, address or phone number.
• Derived Attributes are not physically stored but calculated, like age derived from birth date and current date.