omis ch1-4 Flashcards

(59 cards)

1
Q

data about data, labels, give context

A

metadata

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2
Q

Processes transaction data from terminal

A

Transaction processing system (TPS)

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3
Q

Integrates all functional areas of the organization

A

Enterprise resource planning
(ERP)

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4
Q

Supports the activities within specific functional area

A

Functional area IS

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5
Q

Provides access to data and analysis tools

A

Decision support system

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6
Q

Mimics human expert in a particular area and makes decisions

A

Expert system (ES)

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7
Q

Present structured, summarized information about aspects of business important to executives

A

Dashboards

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8
Q

Manages flows of products, services, and information among organizations

A

Supply chain management system

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9
Q

Enables transactions among organizations and between organizations and customers

A

Electronic commerce system

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10
Q

Information systems that connect two or more organizations

A

Interorganizational Information Systems (IOSs)

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11
Q

lowest level of data measurement. Numbers representing nominal data can be used only to classify or categorize. (arithmetic operations don’t make sense)

A

Nominal

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12
Q

ordinal-level measurement can be used to classify, categorize, rank or order objects, qualitative data, imprecise measurements, distance between ranks not always equal, arithmetic still doesn’t make sense but excellent > poor

A

Ordinal

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13
Q

where the distances between consecutive numbers have meaning and the data are always numerical, quantitative, arithmetic operations can be performed, zero has a meaning (not an absence)

A

Interval

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14
Q

Highest level of data measurement. Ratio data has the same properties as interval data, but ratio data have an absolute zero and the ratio of two numbers is meaningful, quantitative, defined zero point that means absence of characteristic being studied

A

Ratio

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15
Q

require that data be interval or ratio

A

Parametric statistics

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16
Q

data values are nominal or ordinal

A

Non-parametric statistics

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17
Q

takes traditional data and describes what has happened or is happening in a business

A

Descriptive analytics

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18
Q

finds relationships in the data that are not readily apparent with descriptive analytics

A

Predictive analytics

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19
Q

follows descriptive and predictive analytics in an attempt to find the best course of action under certain circumstances

A

Prescriptive analytics

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20
Q

process of collecting, exploring, and analyzing large volumes of data in an effort to uncover hidden patterns and/or relationships that can be used to enhance business decision-making

A

Data mining

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21
Q

data quality, correctness and accuracy

A

Veracity

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22
Q

-32,768 to 32,767, 16 bits

A

Short value

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23
Q

-(2^63) to 2^63, 64 bits

A

Long value

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25
~ 7 decimal digits, 32 bits
Float
26
~ 16 decimal digits, 64 bits
Double
27
variable that can only hold positive values
Unsigned
28
variable that can hold negative and positive values
Signed
29
can only have 2 outcomes, T/F
Boolean
30
stores any string of letters, numbers and symbols
Char
31
systematic way of organizing & accessing data
Data structure
32
sequence of characters, position of first character is 0
String
33
an ordered collection of values with two main characteristics: Ordered and fixed length Homogenous: every value in the array must be of the same type
Array
34
Individual values in array
Elements
35
order in which function calls/instructions/statements are executed when a program is running
Control flow
36
Each element’s relative position number, usually begins with 0
Index
37
Sequential execution: statements executed one after another as they appear Conditional execution: if statements Repetition: loops, repeats set of statements multiple times Nested control structures: combining conditional statements and loops
Types of control flow
38
LIFO, 3 basic stack commands, 3 basic stack commands, Push: insert a data item on the stack, Pop: Remove an item from the top of the stack, Peek: read the value of an item from the top of the stack without removing it
Stack
39
FIFO, Enqueue: putting items in Dequeue: removing items from queue
Queue
40
can be used to define connections, hierarchy, has nodes w/ parent child relationship
Trees
41
node without parent
Root
42
link between any two nodes
Edge
43
node with at least one child
Internal node
44
node without children
External node (leaf)
45
parent, grandparent, great grandparent etc.
Ancestors of a node
46
number of ancestors (number of edges from root to the node)
Depth of a node
47
maximum depth of any node to leaf
Height of a tree
48
child, grandchild, great grandchild etc.
Descendant of a node
49
each internal node has at most two children, children of a node are an ordered pair
Binary tree
50
collection of nodes that have data and are connected to other nodes, can also be used to illustrate examples
Graph data structure
51
vertex is said to be adjacent if there is an edge connecting them
Adjacency
52
sequence of edges that allows you to go from vertex a to vertex b [0-1, 1-2]
Path
53
a graph in which an edge (u,v) doesn’t necessarily mean that there’s an edge (v,u), also edges are represented by arrow to show direction
Directed graph
54
aka maps/dictionaries, abstract data type that can hold data in (key, value) pairs, e.g. phonebook
Associative arrays
55
implements an associative array, but uses a hash function to compute an index aka hash code into an array from which the desired value can be found
Hash table
56
allows access to data in a meaningful way
Key
57
put into bins based on their suits (key) and then sort inside of the bins into order
Bucket technique
58
when the hash function generates the same index for multiple keys
Hash collision
59
can fix collision by creating a linked list in that index, can be multiple chaining
Chaining