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2nd year, 1st Sem > DCIT24 > Flashcards

DCIT24 Flashcards

1
Q

it is concerned with the nature and use of data

A

data analysis

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2
Q

in practice, go directly from fact finding to implementation dependent data analysis

A

systems analysts

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3
Q

establishing the nature of data

A

data analysis

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4
Q

establishing the use of data

A

functional analysis

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5
Q

the designer creates a written specification in words for the database system to be built

A

database study

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6
Q

conceptual, logical, and physical design steps in taking specifications to physical implementable designs

A

database design

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7
Q

it is quite possible that the database is to run on a machine which as yet does not have a database management system running on it

A

implementation and loading

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8
Q

the database, once implemented, must be tested against the specification supplied by the client

A

testing and evaluation

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9
Q

the system is actually in real usage by the company

A

operation

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10
Q

designers rarely get everything perfect first time

A

maintenance and evolution

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11
Q

often referred to as the three-level model

A

three-level database model

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12
Q

the design moves from a written specification taken from the real-world requirements to a physically implementable design

A

three-level database model

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13
Q

three levels of database model

A
  • conceptual design
  • data model mapping
  • physical design
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14
Q

takes the requirements and produces a high-level data model of the database structure

A

conceptual design phase

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15
Q

the conceptual schema is converted into database internal structures

A

physical design phase

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16
Q

process of creating a visual representation of either a whole
information system or parts of it to communicate connections between data points and structures

A

data modeling

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17
Q

built around business needs

A

data models

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18
Q

can be modeled at various levels of abstraction

A

data

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19
Q

types of data models

A
  • conceptual data models
  • logical data models
  • physical data models
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20
Q

also referred to as domain models

A

conceptual data models

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21
Q

offer a big-picture view of what the system will contain, how it will be organized, and which business rules are involved

A

conceptual data models

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22
Q

less abstract and provide greater detail about the concepts and relationships in the domain

A

logical data models

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23
Q

provide a schema for how the data will be physically stored within a database

A

physical data models

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24
Q

they’re the least abstract of all

A

physical data models

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25
Q

data modeling techniques

A
  • hierarchical data modeling
  • network data modeling
  • relational data modeling
  • entity-relationship data modeling
  • dimensional data modeling
  • object-oriented data modeling
  • graph data modeling
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26
Q

organize data in a treelike arrangement of parent and child records

A

hierarchical data modeling

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27
Q

similar hierarchical method is also used today

A

XML or extensible markup language

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28
Q

a popular data modeling option in mainframe databases that isn’t used as much now

A

network data modeling

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29
Q

allowing child records to be connected to multiple parent records

A

network data modeling

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30
Q

a more flexible alternative to hierarchical and network ones

A

relational data modeling

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31
Q

visually map entities, their attributes and the relationships between different entities

A

entity-relationship data modeling

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32
Q

primarily used in data warehouses and data marts that support business intelligence applications

A

dimensional data modeling

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33
Q

similar to the ER method, but it abstracts entities into objects

A

object-oriented data modeling

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34
Q

a more modern offshoot of network and hierarchical models

A

graph data modeling

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35
Q

steps of data modeling

A
  • define an entity
  • define key properties for each entity
  • identify relationships between entities
  • mapping properties to entities
  • reduce redundance in performance requirements
  • complete and validate the data model
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36
Q

benefits of data modeling

A
  • internal agreement on data definitions and standards
  • increased involvement in data management by business users
  • more efficient database design at a lower cost
  • better use of available data assets
37
Q

the constraints, policies, and logic that govern how your data behaves and relates to each other

A

business rules

38
Q

business rules

A
  • identify the sources
  • define the scope
  • express the rules
  • implement the rules
39
Q

a collection of tasks or processes that enhance the designing, development, implementation, and maintenance of enterprise data management system

A

database design

40
Q

it is not an important discussion that has to be taken forward in this article because we are focused on the database design

A

life cycle

41
Q

this stage is concerned with planning the entire DDLC (Database Development Life Cycle)

A

planning

42
Q

this stage covers the boundaries and scopes of the proper database after planning

A

system definition

43
Q

concerned with the practices and implementations of the logical model

A

physical model

44
Q

concerned with developing a model based on the proposed requirements

A

logical model

45
Q

used to import and convert data from the old to the new system

A

data conversion and loading

46
Q

concerned with error identification in the newly implemented system

A

testing

47
Q

a crucial step because it checks the database directly and compares the requirement specifications

A

testing

48
Q

process of determining the logical data structures that are required to support information resources within an organization

A

logical database design

49
Q

critical to the implementation of a corporate database

A

logical design

50
Q

represents the database as a collection of relations

A

relational model

51
Q

properties which define a relation

A

attributes

52
Q

stored along with its entities

A

table

53
Q

it represents records

A

rows

54
Q

it represents attributes

A

column

55
Q

a single row of a table, which contains a single record

A

tuple

56
Q

represents the name of the relation with its
attributes

A

relation schema

57
Q

total number of attributes which in the relation

A

degree

58
Q

total number of rows present in the table

A

cardinality

59
Q

it represents the set of values for a specific attribute

A

column

60
Q

a finite set of tuples in the RDBMS system

A

relation instance

61
Q

every row has one, two or multiple attributes

A

relation key

62
Q

every attribute has some pre-defined value and scope

A

attribute domain

63
Q

conditions which must be present for a valid relation

A

relational integrity constraints

64
Q

can be violated if an attribute value is not appearing in the corresponding domain or it is not of the appropriate data type

A

domain constraints

65
Q

an attribute that can uniquely identify a tuple in a relation

A

key of the table

66
Q

based on the concept of Foreign Keys

A

referential integrity constraints

67
Q

an important attribute of a relation

A

foreign key

68
Q

a type of flowchart that illustrates how “entities” relate to each other within a system

A

entity relationship (ER) diagram

69
Q

a definable thing that can have data stored about it

A

entity

70
Q

group of definable things

A

entity type

71
Q

same as an entity type, but defined at a particular point in time

A

entity set

72
Q

an attribute that uniquely defines an entity in an entity set

A

entity keys

73
Q

set of attributes (one or more) that together define an entity in an entity set

A

super key

74
Q

minimal super key, it has the least possible number of attributes

A

candidate key

75
Q

a candidate key chosen by the database designer to uniquely identify the entity set

A

primary key

76
Q

how entities act upon each other or are associated with each other

A

relationship

77
Q

same entity participates more than once in the relationship

A

recursive relationship

78
Q

property or characteristic of an entity

A

attribute

79
Q

property or characteristic of a relationship

A

descriptive attribute

80
Q

the attribute value is atomic and
can’t be further divided

A

simple

81
Q

sub-attributes spring from an attribute

A

composite

82
Q

attributed is calculated or otherwise derived from another attribute

A

derived

83
Q

more than one attribute value is denoted

A

multi-value

84
Q

just one attribute value

A

single value

85
Q

cardinality can be shown as look-across or same-side

A

cardinality views

86
Q

minimum or maximum numbers that apply to a relationship

A

cardinality constraints

87
Q

highest level in a data flow diagram // a tool popular among business analysts

A

context diagram

88
Q

first drawn in the middle of the chart // usually a circle shape that represents a conceptual boundary

A

context bubble

89
Q

represents all the external components that may interact with the system

A

system context diagram

2nd year, 1st Sem (11 decks)

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