Here is a proposed design for a University database system: Entities: - Students - Faculty - Courses - Classes - Degrees - Grades Tables: - Students (student_id, name, address, etc.) - Faculty (faculty_id, name, department, etc.) - Courses (course_id, name, description, etc.) - Classes (class_id, course_id, faculty_id, semester, year) - Degrees (degree_id, name) - Grades (student_id, class_id, grade) Relationships: - Students take Classes - Faculty teach Classes

1. What is a “Database”
• A Database is a collection of related data.
– Facts that can be recorded and have specific
meaning.
– Represents some aspect of the real world.
– A database is a logically coherent collection of
data. Random assortment of data cannot be
construed as a database.
– A database is designed, built, and populated for a
specific purpose.

2. What is a Database Management
System (DBMS or DMS)
• A general purpose software system that
facilitates the definition, storing,
manipulating, security, organization, retrieval,
and sharing of data in a database.
• The descriptive information about a database
is called meta-data.
• To interact with a DBMS you must issue
transactions.

3. Types of Database Management
Systems
• Hierarchical
• Network
• Relational
• Object Oriented
• XML
• Experimental models such as Spatial-Temporal
Note: We will concentrate on Relational

4. Relational Database
• Characterized by data being arranged to appear as a table of
data with rows and columns.
• Each column has a unique meaning, name, and data type.
• Each Row must have one or more columns.
• A Database must have one or more related tables.
• There may be several database instances on one machine.
• How the data is physically organized and stored on the
machine is hidden from the programmer and user.

5. Definitions
• Attribute: A single data item related to a database.
AKA Field, column
• Candidate Key: A field or group of fields that a could
be a primary key.
• Cursor: The specific record or tuple in a table or view
that the database is currently pointing to.
• Data Mining: Automated data analysis techniques
used to uncover previously undetected relationships
or information.

6. Other Definitions
• Domain: The set of allowed values for an attribute.
• Entity: A single object about which data can be
stored in a database table. Examples: Person,
specimen, or location.
• ER Diagram: A graph that shows the tables and the
relationships between each one.
• Foreign Key: An attribute(s) in a table that is a
primary key in another table.

7. Other Definitions
• Functional Dependency: When one attribute is
related to another. Usually uni-directional. Examples:
Social Security Number -> Name.
• Join: The operation of putting the information in
multiple tables together in one.
• Normalization: The operation if reducing the amount
of redundant information in a database.
• SQL: The Structured Query Language standard.

8. Other Definitions
• Tuple: A row or record.
• View: A view is a “virtual table” that is
generated on the fly when the view is
accessed. The view is generally created using a
pre-defined transaction. Example: You may
want to generate a table of personnel without
privacy information and with information
from other tables includes such as zip code,
city, state.

9. Relationships

10. Database Design
• Define the purpose of the Database
• Gather requirements
• Gather data items based upon requirements (In other words,
what data will be needed to satisfy the requirements for data
storage, retrieval and reporting).
– Name each attribute using standard naming conventions. See if there
exist standards for the particular area. Example: Darwin Core
Standard for biological information systems.
– Define the attribute of each data item.
– Define range of data values.
– Define compatibility checks on the data.

11. Database Design (cont)
• Group related data items into an entity or an object.
(examples: person, class, organization)
• Take an entity and define the physical tables.
• Normalize Tables:
– 1st Normal Form: Each attribute must be autonomous and
there must not be any repeating groups.
– 2nd Normal Form: Is in 1NF and Only attributes allowed that
are directly related to the Primary Key.
– 3rd Normal Form: 2NF and there are no transitive
dependencies.
Note: Most databases seldom go beyond 3NF.

12. Database Design (cont)
• Create any additional tables needed to support the
requirements.
– Attribute look up ( Examples: specimen sex, form etc.
– Cross reference tables needed for normalization.
• Create Views.
• Create indexes.
• Decide on visibility of tables and data along with
security features.
• Create backup and logging strategy.

13. Entity Relationship Diagrams

14. E-R Diagrams
Taken from www.smartdraw.com/tutorials/software/erd

15. E-R Diagrams
Taken from www.smartdraw.com/tutorials/software/erd

16. E-R Diagrams
Taken from www.smartdraw.com/tutorials/software/erd

17. Example
# Database: morphbank
#-----------------------------------------------------------
# server version 4.1.1a-alpha-max-degug
DROP TABLE species;
DROP TABLE classification;
DROP TABLE specimen;
DROP TABLE image;
DROP TABLE viewtable;
DROP TABLE imageannotation;
DROP TABLE phylogeneticcharacter;
DROP TABLE phylogeneticcharacterset;
DROP TABLE phylogeneticcharacterstatetable;
DROP TABLE publicationtable;
DROP TABLE usertable;
DROP TABLE grouptable;

18. Example
#
# Table structure for table 'species'
#
CREATE TABLE species(
SpeciesID int(8) NOT NULL auto_increment,
GenusID int(32) NOT NULL,
FamilyName varchar(128) NOT NULL,
GenusName varchar(128) NOT NULL,
Variety varchar(128),
SpeciesEpithet varchar(128),
SpeciesNameAuthors varchar (128),
SpeciesDescribedYear char(4),
DateIdentified date DEFAULT '0000-00-00',
IdentifiedBy varchar(128) NOT NULL,
PRIMARY KEY (SpeciesID));

19. Example
# Table Structure for table 'classification'
CREATE TABLE classification(
GenusID int(32) NOT NULL auto_increment,
GenusName varchar(128) NOT NULL,
FamilyName varchar(128) NOT NULL,
OrderName varchar(128) NOT NULL,
ClassName varchar(128),
PhylumName varchar(128) NOT NULL,
KingdomName varchar(128) NOT NULL,
PRIMARY KEY (GenusID));

20. Example
#
# Table structure for table 'specimen'
#
CREATE TABLE specimen(
MorphBankSpecimenID int(32) NOT NULL,
SpeciesID int(8) NOT NULL,
CatalogNumber int (32) NOT NULL AUTO_INCREMENT,
DateLastModified date NOT NULL default '0000-00-00',
InstitutionCode varchar(128),
CollectionCode varchar(128),
ScientificName varchar(128),
BasisOfRecord char(1),
SubSpecies varchar (128),
TypeStatus varchar (255),
TypeName varchar (128),
CollectionNumber varchar (128),

21. Example
FieldNumber varchar (128),
CollectorName varchar (128),
DateCollected date NOT NULL DEFAULT '0000-00-00',
TimeofDate time,
ContinentOcean varchar(128),
Country varchar(56),
StateProvince varchar(56),
County varchar(56),
Locality varchar(56),
Latitude double,
Longitude double,
CoordinatePrecision int(8),
MinimumElevation int(32),

22. Example
MaximumElevation int(32),
MinimumDepth int(32),
MaximumDepth int(32),
Sex varchar(8),
PreparationType varchar(255),
IndividualCount int(32),
PreviousCatalogNumber varchar(128),
RelationshipType varchar(128),
RelatedCatalogItem varchar (128),
DevelopmentalStage varchar (128),
Notes varchar(255),
PRIMARY KEY(CatalogNumber));

23. In-Class Exercise
Work on the design of a University database
system designed to track student, faculty,
course, classes, degrees, and grades.