Chapter 1 Flashcards

1
Q

Data

A

Raw facts, or facts that have not yet been processed to reveal their meaning to the end user.

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2
Q

Information

A

The result of processing raw data to reveal its meaning. Information consists of transformed data and facilitates decision making.

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3
Q

Knowledge

A

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 is that new knowledge can be derived from old knowledge.

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4
Q

Data Management

A

A process that focuses on data collection, storage, and retrieval. Common data management functions include addition, deletion, modification, and listing.

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5
Q

Database

A

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.

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6
Q

Metadata

A

Data about data; that is, data about data characteristics and relationships.

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7
Q

End-User Data

A

Raw facts of interest to the end user.

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8
Q

Database Management System (DBMS)

A

The collection of programs that manages the database structure and controls access to the data stored in the database.

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9
Q

Advantages of a DBMS

A
  1. Improved data sharing
  2. Improved data security
  3. Better data integration
  4. Minimized data inconsistency
  5. Improved data access
  6. Improved decision making
  7. Increased end-user productivity
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10
Q

Data Inconsistency

A

A condition in which different versions of the same data yield different (inconsistent) results.

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11
Q

Query

A

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.

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12
Q

Ad Hoc Query

A

A “spur-of-the-moment” question.

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13
Q

Query Result Set

A

The collection of data rows returned by a query.

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14
Q

Data Quality

A

A comprehensive approach to ensuring the accuracy, validity, and timeliness of data.

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15
Q

Single-User Database

A

A database that supports only one user at a time.

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16
Q

Desktop Database

A

A single-user database that runs on a personal computer.

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17
Q

Multiuser Database

A

A database that supports multiple concurrent users.

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18
Q

Workgroup Database

A

A multiuser database that usually supports fewer than 50 users or is used for a specific department in an organization.

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19
Q

Enterprise Database

A

The overall company data representation, which provides support for present and expected future needs.

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20
Q

Centralized Database

A

A database located at a single site.

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21
Q

Distributed Database

A

A logically related database that is stored in two or more physically independent sites.

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22
Q

Cloud Database

A

A database that is created and maintained using cloud services, such as Microsoft Azure or Amazon AWS.

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23
Q

General-Purpose Databases

A

A database that contains a wide variety of data used in multiple disciplines.

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24
Q

Discipline-Specific Databases

A

A database that contains data focused on specific subject areas.

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25
Q

Operational Database aka Online Transaction Processing (OLTP) Database, Transactional Database, or Production Database

A

A database designed primarily to support a company’s day-to-day operations. Also known as a transactional database, OLTP database, or production database.

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26
Q

Analytical Database

A

A database focused primarily on storing historical data and business metrics used for tactical or strategic decision making.

27
Q

Data Warehouse

A

A specialized database that stores historical and aggregated data in a format optimized for decision support. An integrated, subject-oriented, time-variant, nonvolatile collection of data that provides support for decision making.

28
Q

Online Analytical Processing (OLAP)

A

Decision support system (DSS) tools that use multidimensional data analysis techniques. OLAP creates an advanced data analysis environment that supports decision making, business modeling, and operations research.

29
Q

Business Intelligence

A

A comprehensive approach to capture and process business data with the purpose of generating information to support business decision making.

30
Q

Unstructured Data

A

Data that exists in its original, raw state; that is, in the format in which it was collected and does not conform to a predefined data model.

31
Q

Structured Data

A

Data that conforms to a predefined data model and has been formatted to facilitate storage, use, and information generation.

32
Q

Semistructured Data

A

Data that has already been processed to some extent.

33
Q

Extensible Markup Language (XML)

A

A metalanguage used to represent and manipulate data elements. Unlike other markup languages, XML permits the manipulation of a document’s data elements. XML facilitates the exchange of structured documents such as orders and invoices over the Internet.

34
Q

XML Database

A

A database system that stores and manages semistructured XML data.

35
Q

Social Media

A

Web and mobile technologies that enable “anywhere, anytime, always on” human interactions.

36
Q

NoSQL

A

A new generation of database management systems that is not based on the traditional relational database model.

37
Q

Database Design

A

The process that yields the description of the database structure and determines the database components. The second phase of the database life cycle.

38
Q

Data Processing (DP) Specialist

A

The person responsible for developing and managing a computerized file processing system.

39
Q

Field

A

A character or group of characters (alphabetic or numeric) that has a specific meaning. A field is used to define and store data.

40
Q

Record

A

A logically connected set of one or more fields that describes a person, place, or thing.

41
Q

File

A

A collection of related records. For example, a file might contain data about the students currently enrolled at Gigantic University.

42
Q

Problems with File System Data Processing

A
  1. Lengthy development times
  2. Difficulty of getting quick answers
  3. Complex system administration
  4. Lack of security and limited data sharing
  5. Extensive programming
43
Q

Structural Dependence

A

A data characteristic in which a change in the database schema affects data access, thus requiring changes in all access programs.

44
Q

Structural Independence

A

A data characteristic in which changes in the database schema do not affect data access.

45
Q

Data Type

A

Defines the kind of values that can be used or stored. Also, used in programming languages and database systems to determine the operations that can be applied to such data.

46
Q

Data Dependence

A

A data condition in which data representation and manipulation are dependent on the physical data storage characteristics.

47
Q

Data Independence

A

A condition in which data access is unaffected by changes in the physical data storage characteristics.

48
Q

Logical Data Format

A

The way a person views data within the context of a problem domain.

49
Q

Physical Data Format

A

The way a computer “sees” (stores) data.

50
Q

Islands of Information

A

In the old file system environment, pools of independent, often duplicated, and inconsistent data created and managed by different departments.

51
Q

Data Redundancy

A

Exists when the same data is stored unnecessarily at different places.

52
Q

Results of Data Redundancy

A
  1. Poor data security
  2. Data inconsistency
  3. Data-entry errors
  4. Data integrity problems
53
Q

Data Integrity

A

In a relational database, a condition in which the data in the database complies with all entity and referential integrity constraints.
Means 2 things:
1. Data is accurate
2. Data is verifiable

54
Q

Data Anomaly

A

A data abnormality in which inconsistent changes have been made to a database. For example, an employee moves, but the address change is not corrected in all files in the database.

55
Q

Types of Data Anomalies

A
  1. Update anomalies
  2. Insertion anomalies
  3. Deletion anomalies
56
Q

Database System

A

An organization of components that defines and regulates the collection, storage, management, and use of data in a database environment.

57
Q

Five Parts of a Database System

A
  1. Hardware (all of the systems physical devices)
  2. Software (OS, DBMS software, application programs and utility software)
  3. People (sys admins, database admins, database designers, system analysts and programmers, end users)
  4. Procedures (instructions and rules the govern the design and use of the database system)
  5. Data
58
Q

Data Dictionary

A

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 is external to the DBMS.

59
Q

DBMS Functions

A
  1. Data dictionary management
  2. Data storage management
  3. Data transformation and presentation
  4. Security management
  5. Multiuser access control
  6. Backup and recovery management
  7. Data integrity management
  8. Database access languages and application programming interfaces
  9. Database communication interfaces
60
Q

Performance Tuning

A

Activities that make a database perform more efficiently in terms of storage and access speed.

61
Q

Query Language

A

A nonprocedural language that is used by a DBMS to manipulate its data. An example of a query language is SQL.

62
Q

Structured Query Language (SQL)

A

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.

63
Q

Database System Disadvantages

A
  1. Increased costs
  2. Management complexity
  3. Maintaining currency
  4. Vendor dependence
  5. Frequent upgrade/replacement cycles