omis ch1-4 Flashcards
data about data, labels, give context
metadata
Processes transaction data from terminal
Transaction processing system (TPS)
Integrates all functional areas of the organization
Enterprise resource planning
(ERP)
Supports the activities within specific functional area
Functional area IS
Provides access to data and analysis tools
Decision support system
Mimics human expert in a particular area and makes decisions
Expert system (ES)
Present structured, summarized information about aspects of business important to executives
Dashboards
Manages flows of products, services, and information among organizations
Supply chain management system
Enables transactions among organizations and between organizations and customers
Electronic commerce system
Information systems that connect two or more organizations
Interorganizational Information Systems (IOSs)
lowest level of data measurement. Numbers representing nominal data can be used only to classify or categorize. (arithmetic operations don’t make sense)
Nominal
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
Ordinal
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)
Interval
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
Ratio
require that data be interval or ratio
Parametric statistics
data values are nominal or ordinal
Non-parametric statistics
takes traditional data and describes what has happened or is happening in a business
Descriptive analytics
finds relationships in the data that are not readily apparent with descriptive analytics
Predictive analytics
follows descriptive and predictive analytics in an attempt to find the best course of action under certain circumstances
Prescriptive analytics
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
Data mining
data quality, correctness and accuracy
Veracity
-32,768 to 32,767, 16 bits
Short value
-(2^63) to 2^63, 64 bits
Long value
~ 7 decimal digits, 32 bits
Float
~ 16 decimal digits, 64 bits
Double
variable that can only hold positive values
Unsigned
variable that can hold negative and positive values
Signed
can only have 2 outcomes, T/F
Boolean
stores any string of letters, numbers and symbols
Char
systematic way of organizing & accessing data
Data structure
sequence of characters, position of first character is 0
String
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
Individual values in array
Elements
order in which function calls/instructions/statements are executed when a program is running
Control flow
Each element’s relative position number, usually begins with 0
Index
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
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
FIFO, Enqueue: putting items in
Dequeue: removing items from queue
Queue
can be used to define connections, hierarchy, has nodes w/ parent child relationship
Trees
node without parent
Root
link between any two nodes
Edge
node with at least one child
Internal node
node without children
External node (leaf)
parent, grandparent, great grandparent etc.
Ancestors of a node
number of ancestors (number of edges from root to the node)
Depth of a node
maximum depth of any node to leaf
Height of a tree
child, grandchild, great grandchild etc.
Descendant of a node
each internal node has at most two children, children of a node are an ordered pair
Binary tree
collection of nodes that have data and are connected to other nodes, can also be used to illustrate examples
Graph data structure
vertex is said to be adjacent if there is an edge connecting them
Adjacency
sequence of edges that allows you to go from vertex a to vertex b [0-1, 1-2]
Path
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
aka maps/dictionaries, abstract data type that can hold data in (key, value) pairs, e.g. phonebook
Associative arrays
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
allows access to data in a meaningful way
Key
put into bins based on their suits (key) and then sort inside of the bins into order
Bucket technique
when the hash function generates the same index for multiple keys
Hash collision
can fix collision by creating a linked list in that index, can be multiple chaining
Chaining
