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AIT 524: Database Management Essentials
Test 1 Study Guide
Test 1 consists of three parts that are equally weighted:
• Part 1 will assess how well students can apply concepts learned in class to solve
real-world problems and design a database.
• Part 2 includes 50 multiple-choice/true-false questions and covers the topics
listed below. Questions are based on the material covered in the textbook,
presented in the Power-Point presentations and open-source materials before Test
1.
• Part 3 will assess students' knowledge of relational algebra.
Test 1 is closed book, closed notes. Students will be taking all parts on Blackboard.
The best way to prepare for Test 1 is to review practice problems and HW assignments,
and complete practice quizzes. In addition, it is recommended to go over examples and
exercises available in the textbooks required for this course.
Test 1 topics include but are not limited to:
Database Systems:
Ø ad hoc query—A “spur-of-the-moment” question.
Ø centralized database—A database located at a single site.
Ø data—Raw facts, that is, facts that have not yet been processed to reveal their
meaning to the end user.
Ø data anomaly—A data abnormality that exists when inconsistent changes to a
database have been made. For example, an employee moves, but the address
change is corrected in only one file and not across all files in the database.
Ø data dependence—A data condition in which the data representation and
manipulation are dependent on the physical data storage characteristics.
Ø data dictionary—A DBMS component that stores metadata—data about data.
Thus, the data dictionary contains the data definition as well as its characteristics
and relationships. A data dictionary may also include data that are external to the
DBMS. Also known as an information resource dictionary. See also active data
dictionary, metadata, and passive data dictionary.
Ø data inconsistency—A condition in which different versions of the same data
yield different (inconsistent) results.
Ø data independence—A condition that exists when data access is unaffected by
changes in the physical data storage characteristics.
Ø data integrity—In a relational database, refers to a condition in which the data in
the database is in compliance with all entity and referential integrity constraints.
Ø data management—A process that focuses on data collection, storage, and
retrieval. Common data management functions include addition, deletion,
modification, and listing.
Ø data redundancy—A condition that exists when a data environment contains
redundant (unnecessarily duplicated) data.
Ø data warehouse—Bill Inmon, the acknowledged “father of the data warehouse,”
defines the term as “an integrated, subject-oriented, time-variant, nonvolatile
collection of data that provides support for decision making.”
Ø database—A shared, integrated computer structure that houses a collection of
related data. A database contains two types of data: end-user data (raw facts) and
metadata. The metadata consist of data about data, that is, the data characteristics
and relationships.
Ø database design—The process that yields the description of the database
structure. The database design process determines the database components.
Database design is the second phase of the database life cycle.
Ø database management system (DBMS) —Refers to the collection of programs
that manages the database structure and controls access to the data stored in the
database.
Ø database system—An organization of components that defines and regulates the
collection, storage, management, and use of data in a database environment.
Ø desktop database—A single-user database that runs on a personal computer.
Ø distributed database—A logically related database that is stored over two or
more physically independent sites.
Ø enterprise database—The overall company data representation, which provides
support for present and expected future needs.
Ø field—A character or group of characters (alphabetic or numeric) that defines a
characteristic of a person, place, or thing. For example, a person’s Social Security
number, address, phone number, and bank balance all constitute fields.
Ø file—A named collection of related records.
Ø information—The result of processing raw data to reveal its meaning.
Information consists of transformed data and facilitates decision making.
Ø islands of information—A term used in the old-style file system environment to
refer to independent, often duplicated, and inconsistent data pools created and
managed by different organizational departments.
Ø knowledge—The body of information and facts about a specific subject.
Knowledge implies familiarity, awareness, and understanding of information as it
applies to an environment. A key characteristic of knowledge is that “new”
knowledge can be derived from “old” knowledge.
Ø logical data format—The way in which a human being views data.
Ø metadata—Data about data, that is, data concerning data characteristics and
relationships. See also data dictionary.
Ø multiuser database—A database that supports multiple concurrent users.
Ø operational database—A database that is designed primarily to support a
company’s day-to-day operations. Also known as a transactional database or
production database.
Ø performance tuning—Activities that make a database perform more efficiently
in terms of storage and access speed.
Ø physical data format—The way in which a computer “sees” (stores) data.
Ø production database—The main database designed to keep track of the day-to-
day operations of a company. See also transactional database.
Ø query—A question or task asked by an end user of a database in the form of SQL
code. A specific request for data manipulation issued by the end user or the
application to the DBMS.
Ø query language—A nonprocedural language that is used by a DBMS to
manipulate its data. An example of a query language is SQL.
Ø query result set—The collection of data rows that are returned by a query.
Ø record—A collection of related (logically connected) fields.
Ø single-user database—A database that supports only one user at a time.
Ø structural dependence—A data characteristic that exists when a change in the
database schema affects data access, thus requiring changes in all access
programs.
Ø structural independence—A data characteristic that exists when changes in the
database schema do not affect data access.
Ø Structured Query Language—A powerful and flexible relational database
language composed of commands that enable users to create database and table
structures, perform various types of data manipulation and data administration,
and query the database to extract useful information.
Ø transactional database—A database designed to keep track of the day-to-day
transactions of an organization. See also production database.
Ø workgroup database—A multiuser database that supports a relatively small
number of users (usually fewer than 50) or that is used for a specific department
in an organization.
Data Models
Ø attribute—A characteristic of an entity or object. An attribute has a name and a
data type.
Ø business rule—Narrative descriptions of a policy, procedure, or principle within
an organization. Examples: A pilot cannot be on duty for more than 10 hours
during a 24-hour period. A professor may teach up to four classes during any one
semester.
Ø class—A collection of like objects with shared structure (attributes) and behavior
(methods). A class encapsulates an object’s data representation and a method’s
implementation. Classes are organized in a class hierarchy.
Ø class hierarchy—The organization of classes in a hierarchical tree where each
“parent” class is a superclass and each “child” class is a subclass. See also
inheritance.
Ø conceptual model—The output of the conceptual design process. The conceptual
model provides a global view of an entire database. Describes the main data
objects, avoiding details.
Ø conceptual schema—A representation of the conceptual model, usually
expressed graphically. See also conceptual model.
Ø connectivity—Describes the classification of the relationship between entities.
Classifications include 1:1, 1:M, and M:N.
Ø constraint—A restriction placed on data. Constraints are normally expressed in
the form of rules. Example: “A student’s GPA must be between 0.00 and 4.00.”
Constraints are important because they help to ensure data integrity.
Ø Crow’s Foot notation—A representation of the entity relationship diagram using
a three-pronged symbol to represent the “many” sides of the relationship.
Ø data definition language (DDL)—The language that allows a database
administrator to define the database structure, schema, and subschema.
Ø data management language (DML)—The language (set of commands) that
allows an end user to manipulate the data in the database (SELECT, INSERT,
UPDATE, DELETE, COMMIT, and ROLLBACK).
Ø data model—A representation, usually graphic, of a complex “real-world” data
structure. Data models are used in the database design phase of the database life
cycle.
Ø entity—Something about which someone wants to store data; typically a person,
a place, a thing, a concept, or an event. See also attribute.
Ø entity instance—A term used in ER modeling to refer to a specific table row.
Also known as an entity occurrence.
Ø entity occurrence—See entity instance.
Ø entity relationship diagram (ERD)—A diagram that depicts an entity
relationship model’s entities, attributes, and relations.
Ø entity relationship (ER) model (ERM)—A data model developed by P. Chen in
1975. It describes relationships (1:1, 1:M, and M:N) among entities at the
conceptual level with the help of ER diagrams.
Ø entity set—In a relational model, refers to a grouping of related entities.
Ø extended relational data model (ERDM)—Sometimes referred to as the
enhanced entity relationship model; the result of adding more semantic constructs
(entity supertypes, entity subtypes, and entity clustering) to the original entity
relationship (ER) model.
Ø external model—The application programmer’s view of the data environment.
Given its business-unit focus, an external model works with a data subset of the
global database schema.
Ø external schema—The specific representation of an external view, that is, the
end user’s view of the data environment.
Ø hardware independence—Means that a model does not depend on the hardware
used in the implementation of the model. Therefore, changes in the hardware will
have no effect on the database design at the conceptual level.
Ø hierarchical model—No longer a major player in the current database market;
important to know, however, because the basic concepts and characteristics form
the basis for subsequent database development. This model is based on an
“upside-down” tree structure in which each record is called a segment. The top
record is the root segment. Each segment has a 1:M relationship to the segment
directly below it.
Ø inheritance—In the object-oriented data model, the ability of an object to inherit
the data structure and methods of the classes above it in the class hierarchy. See
also class hierarchy.
Ø internal model—In database modeling, refers to a level of data abstraction that
adapts the conceptual model to a specific DBMS model for implementation.
Ø internal schema—Depicts a specific representation of an internal model, using
the database constructs supported by the chosen database. (The internal model is
the representation of a database as “seen” by the DBMS. In other words, the
internal model requires a designer to match the conceptual model’s characteristics
and constraints to those of the selected implementation model.)
Ø logical design—A stage in the design phase that matches the conceptual design to
the requirements of the selected DBMS and is, therefore, software-dependent. It is
used to translate the conceptual design into the internal model for a selected
database management system, such as DB2, SQL Server, Oracle, IMS, Informix,
Access, and Ingress.
Ø logical independence—A condition that exists when the internal model can be
changed without affecting the conceptual model. (The internal model is hardware
independent because it is unaffected by the choice of computer on which the
software is installed. Therefore, a change in storage devices or even a change in
operating systems will not affect the internal model.)
Ø many-to-many (M:N or *..*) relationship—One of three types of relationships
(associations among two or more entities) in which one occurrence of an entity is
associated with many occurrences of a related entity and one occurrence of the
related entity is associated with many occurrences of the first entity.
Ø method—In the object-oriented data model, a named set of instructions to
perform an action. Methods represent real-world actions. Methods are invoked
through messages.
Ø network model—A data model standard created by the CODASYL Data Base
Task Group in the late 1960s. It represented data as a collection of record types
and relationships as predefined sets with an owner record type and a member
record type in a 1:M relationship.
Ø object—An abstract representation of a real-world entity that has a unique
identity, embedded properties, and the ability to interact with other objects and
with itself.
Ø object-oriented data model (OODM)—A data model whose basic modeling
structure is an object.
Ø object-oriented database management system (OODBMS)—Data
management software used to manage data found within an object-oriented
database model.
Ø object/relational database management system (O/RDBMS)—A DBMS based
on the extended relational model (ERDM). The ERDM, championed by many
relational database researchers, constitutes the relational model’s response to the
OODM. This model includes many of the object-oriented model’s best features
within an inherently simpler relational database structural environment.
Ø one-to-many (1:M or 1..*) relationship—One of three types of relationships
(associations among two or more entities) that are used by data models. In a 1:M
relationship, one entity instance is associated with many instances of the related
entity.
Ø one-to-one (1:1 or 1..1) relationship—One of three types of relationships
(associations among two or more entities) that are used by data models. In a 1:1
relationship, one entity instance is associated with only one instance of the related
entity.
Ø physical independence—A condition that exists when the physical model can be
changed without affecting the internal model.
Ø physical model—A model in which the physical characteristics (location, path,
and format) are described for the data. Both hardware- and software-dependent.
See also physical design.
Ø relation—In a relational database model, an entity set. Relations are implemented
as tables. Relations (tables) are related to each other through the sharing of a
common entity characteristic (value in a column).
Ø relational database management system (RDBMS)—A collection of programs
that manages a relational database. The RDBMS software translates a user’s
logical requests (queries) into commands that physically locate and retrieve the
requested data. A good RDBMS also creates and maintains a data dictionary
(system catalog) to help provide data security, data integrity, concurrent access,
easy access, and system administration to the data in the database through a query
language (SQL) and application programs.
Ø relational diagram—A graphical representation of a relational database’s
entities, the attributes within those entities, and the relationships among those
entities.
Ø relational model—Developed by E. F. Codd (of IBM) in 1970, it represents a
major breakthrough for users and designers because of its conceptual simplicity.
The relational model, based on mathematical set theory, represents data as
independent relations. Each relation (table) is conceptually represented as a
matrix of intersecting rows and columns. The relations are related to each other
through the sharing of common entity characteristics (values in columns).
Ø relationship—An association between entities.
Ø schema— A logical grouping of database objects (tables, indexes, views, queries,
etc.) that are related to each other. Usually, a schema belongs to a single user or
application.
Ø semantic data model—The first of a series of data models that more closely
represented the real world, modeling both data and their relationships in a single
structure known as an object. The SDM, published in 1981, was developed by M.
Hammer and D. McLeod.
Ø software independence—A property of any model or application that does not
depend on the software used to implement it.
Ø subschema—In the network model, the portion of the database “seen” by the
application programs that produce the desired information from the data contained
within the database.
Ø table—A (conceptual) matrix composed of intersecting rows (entities) and
columns (attributes) that represents an entity set in the relational model. Also
called a relation.