Mis 235 Midterm Part 2

Front 1

A character of data

Back 1

bytes

Front 2

Layout of a table

Back 2

-Columns are also called fields
-Fields are grouped into rows, which are also called records
-A group of similar rows or records is called a table or file

Front 3

Collection of tables plus relaitonships among the rows in those tables

Back 3

Databases

Front 4

Column or groups of columns that identifies a unieque row in a table

Back 4

Primary key

Front 5

Certain columns are keys, but they are keys of a different table than the one which they are inside

Back 5

foreign key

Front 6

databases that carry their data in the form of tables and that repreesnt relationships using foreign keys

Back 6

Relational databses

Front 7

SQL

Back 7

Structure query language- the universal interface to relational databases

Front 8

Diagramming tool used to express entity (table) relationships, very useful in developing complex databases

Back 8

Entity Relationship Diagram

Front 9

Data that describes data

Back 9

metadata

Front 10

Technique used to make complex databases more efficient

Back 10

Normalization

Front 11

Database advantages

Back 11

1. increased flexibility
2. increased scalability and performance
3. reduced information redundancy
4. increased information quality
5. increased information security

Front 12

Software program used to create, process, and administer a database

Back 12

Database Management system (DBMS)

Front 13

assembly of forms, reports, queries, and application programs that process a database

Back 13

database application system

Front 14

product process large organizational and workgroup databases

Back 14

Enterprise DBMS

Front 15

designed for smaller, simpler database application

Back 15

Personal DBMS
-only personal is microsoft access

Front 16

information containing patterns, relationships and trends

Back 16

business intelligence

Front 17

integrate data from multiple sources, and they process that data by sorting, grouping, summing, averaging, and comparing

Back 17

reporting systems

Front 18

process data using sophisticated statistical techniques, such as regression analysis and decision tree analysis

Back 18

data mining systems

Front 19

computes correlations of items on past orders to determine items that are frequently purchased together

Back 19

market-basket analysis

Front 20

create value from intellectual captial by collecting and sharing human knowledge of products, product users, best practices, and other critical knowledge with employees, managers, customers, suppliers, and other s who ened it

Back 20

knowledge management systems

Front 21

encapsulate the knowledge of human experts in the form of if/then rules

Back 21

expert systems

Front 22

the more attributes there are, the easier it is to build a model that fits the sample data but that is worthless as a predictor

Back 22

curse of dimensionality

Front 23

reposiotry of an organization's electronially stored data. designed to facilitate reporting and analysis

Back 23

data warehosue

Front 24

data collection that is created to address the needs of a particular business function, problem, or opportunity

Back 24

data mart (smaller than a data warehouse)

Front 25

Extraction, transformation, and loading

Back 25

process that extracts information from internal and external databases, transfroms the info using a common set of enterprise definitions, loads the information into a data warehouse

Front 26

common term for the representation of multidimensional information

Back 26

cube

Front 27

analysts do not create a model or hypothesis before running the analysis

Back 27

unsupervised data mining

Front 28

statistical techniques that identify groups of entities that have similar characteristics

Back 28

custer analysis (under unsupervised data mining)

Front 29

Data miners develop a model prior to the analysis and apply statistical techniquest to data to esimate parameters of the model

Back 29

supervised data mining

Front 30

simulates human intelligence such as the ability to reason and learn

Back 30

artificial intelligence

Front 31

attempts to emulate the way the human brain works (used for predicted values and making classifications)

Back 31

neural network

Front 32

focuses on identifying customer groups based on demographica and attributes such as attitude and psychological profiles

Back 32

traditional segmentations

Front 33

looks at groups of customers in terms of the revenue they generation and the costs of establishing and maintaining relationships with them

Back 33

value-based segmenation