AIS Ch. 4 Flashcards

1
Q

Database

A

A set of interrelated, centrally coordinated data files that are stored with as little data redundancy as possible.

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2
Q

Database MGMT System (DBMS)

A

The program that manages and controls the data and the interfaces between the data and the application programs that use the data stored in the database.

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3
Q

Database System

A

The data-base, DBMS, and the application programs that access the database through the DBMS.

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4
Q

Database Administrator (DBA)

A

The person responsible for coordinating, controlling, and managing the database.

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5
Q

Data Warehouse

A

Very large database containing detailed and summarized data for a number of years that are used for analysis rather than transaction processing.

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6
Q

Business Intelligence

A

Analyzing large amounts of data for strategic decision making.

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7
Q

Online Analytical Processing (OLAP)

A

Using queries to investigate hypothesized relationships among data.

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8
Q

Data Mining

A

Using sophisticated statistical analysis to “discover” un-hypothesized relationships in data.

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9
Q

Record Layout

A

Document that shows the items stored in a file, including the order and length of the data fields and the type of data stored.

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10
Q

Logical View

A

How people conceptually organize, view, and understand the relationships among data items.

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11
Q

Physical View

A

The way data are physically arranged and stored in the computer system.

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12
Q

Schema

A

A description of the data elements in a database the relationships among them, and the logical model used to organize and describe the data.

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13
Q

Conceptual-level Schema

A

The organization-wide view of the entire database; also called a subschema.

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14
Q

Subschema

A

A subset of the schema; the way the user defines the data and the data relationships.

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15
Q

Internal-Level Schema

A

A low-level view of the entire database describing how the data are actually stored and accessed.

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16
Q

Data Dictionary

A

Information about the structure of the database, including a description of each data element.

17
Q

Data Definition Language

A

DBMS language that builds the data dictionary, creates the database, describes logical views, and specifies record or field security constraints.

18
Q

Data Manipulation Language (DML)

A

DBMS language that contains powerful, easy-to-use commands that enable users to retrieve, sort, order, and display data.

19
Q

Report Writer

A

DBMS language that simplifies report creation.

20
Q

Data Model

A

An abstract representation of database contents.

21
Q

Relational Data Model

A

A two-dimensional table representation of data; each row represents a unique entity (record) and each column is a field where record attributes are stored.

22
Q

Tuple

A

A row in a table that contains data about a specific item in a database table.

23
Q

Primary Key

A

Database attribute or combination of attributes, that uniquely identifies each row in a table.

24
Q

Foreign Key

A

An attribute in a table that is also a primary key in another table; used to link the two tables.

25
Q

Update Anomaly

A

Improper database organization where a non-primary key item is stored multiple times; updating the item in one location and not the others causes data inconsistencies.

26
Q

Insert Anomaly

A

Improper database organization that results in the inability to add records to a database.

27
Q

Delete Anomaly

A

Improper organization of a database that results in the loss of all information about an entity when a row is deleted.

28
Q

Relational Database

A

A database built using the relational data model.

29
Q

Entity Integrity Rule

A

A non-null primary key ensures that every row in a table represents something and that it can be identified.

30
Q

Referential Integrity Rule

A

Foreign keys which link rows in one table to rows in another table must have values that correspond to the value of a primary key in another table.

31
Q

Normalization

A

Following relational database creation rules to design a relational database that is free from delete, insert, and update anomalies.

32
Q

Semantic Data Modeling

A

Using knowledge of business processes and information needs to create a diagram that shows what to include in a fully normalized database.