Quiz 5

Question 1

multidimensional data

Answer 1

Question 2

dimension

Answer 2

attribute of decision-making

Question 3

fact

Answer 3

outcome measure

Question 4

dimension table key

Answer 4

• Uniquely identifies a record
• Should not be same as operational database key
• Avoid built-in meanings into the key used in the data warehouse

Question 5

dimension table records

Answer 5

• Few records in table

Question 6

dimension table attributes

Answer 6

• Large number of attributes
• Attributes capture specific aspects of decisions made
• Mostly non-numeric, categorical data types

Question 7

operational vs. DW primary keys

Answer 7

• Operational Keys:
  
  • Meaningful
  • Unique at that time and while it is present in the database
  • Key can change
  • New column is defined as key
  • Data type changed
• Surrogate Key (DW):
  
  • Completely independent of operational
  • Completely meaningless
  • Operational key kept in data warehouse but as a regular attribute, not as a primary key

Question 8

fact table key

Answer 8

• concatenated key of dimension keys
• only two types of columns in fact table
  
  • concatenated keys
  • measures

Question 9

fact table granularity

Answer 9

• Determined by Granularity of Dimension Tables
• Example:
  
  • Each record represents sales by (Dealer, Date, Customer, Product)

Question 10

fact table measures

Answer 10

• typically numeric attributes

Question 11

fact table sparseness

Answer 11

• Small percentage of combinations of dimensions are recorded in the fact table

Question 12

fact table size

Answer 12

• Compared to dimension table, this table is very large
• >100x smallest dimension table

Question 13

(fully) additive measure

Answer 13

• Measure that is summed during roll up
• Stored in fact table
• Example:
  
  • Total Sales = ∑Sales_<em>i</em>

Question 14

semi-additive measure

Answer 14

• Calculated
• All of its components are additive, but itself is not
• Components stored but result calculated on the spot
• Example:
  
  • Average Sales = Total Sales / Count of sales

Question 15

non-additive measure

Answer 15

• Calculation must always be performed starting at finest granularity
• If any component is non-additive, then the measure is non-additive
• Example:
  
  • Total number of unique customers
  • No way to determine from roll-up

Question 16

factless fact table

Answer 16

• Has no measures
• Each record identifies occurrence of an event
• Example:
  
  • Test-drive of a car
  • Which customer, When (Time), Where (Dealer), What Car (Product)
• Why?
  
  • Analyze influence of events on Other Outcomes
  • For example:
    
    • Do certain promotions increase test-drives?
    • Relationship between test-drives and sales

Question 17

star schema and queries

Answer 17

• star schema makes querying simple

Question 18

star schema cost of query

Answer 18

• …

Question 19

roll-up

Answer 19

• A roll-up involves summarizing the data along a dimension
• The summarization rule might be computing totals along a hierarchy or applying a set of formulas such as “profit = sales - expenses”

Question 20

drill-down/up

Answer 20

• Drill Down/Up allows the user to navigate among levels of data
• ranging from the
  
  • most summarized (up) to the
  • most detailed (down)

Question 21

slice

Answer 21

• Slice is the act of picking a rectangular subset of a cube by choosing a single value for one of its dimensions
• creating a new cube with one fewer dimension