1. Data Warehousing: and OLAP —BIS 541— — Chapter 3 —
2013/2014 Summer

2. Chapter 3: Data Warehousing and OLAP Technology for Data Mining
What is a data warehouse?
A multi-dimensional data model
Data warehouse architecture
Data warehouse implementation
Further development of data cube technology
From data warehousing to data mining

3. What is Data Warehouse?
Defined in many different ways, but not rigorously.
A decision support database that is maintained separately from the organization’s operational database
Support information processing by providing a solid platform of consolidated, historical data for analysis.
“A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process.”—W. H. Inmon
Data warehousing:
The process of constructing and using data warehouses

4. Data Warehouse—Subject-Oriented
Organized around major subjects, such as customer, product, sales.
Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing.
Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.

5. Data Warehouse—Integrated
Constructed by integrating multiple, heterogeneous data sources
relational databases, flat files, on-line transaction records
Data cleaning and data integration techniques are applied.
Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources
E.g., Hotel price: currency, tax, breakfast covered, etc.
When data is moved to the warehouse, it is converted.

6. Data Warehouse—Time Variant
The time horizon for the data warehouse is significantly longer than that of operational systems.
Operational database: current value data.
Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years)
Every key structure in the data warehouse
Contains an element of time, explicitly or implicitly
But the key of operational data may or may not contain “time element”.

7. Data Warehouse—Non-Volatile
A physically separate store of data transformed from the operational environment.
Operational update of data does not occur in the data warehouse environment.
Does not require transaction processing, recovery, and concurrency control mechanisms
Requires only two operations in data accessing:
initial loading of data and access of data.

8. Data Warehouse vs. Heterogeneous DBMS
Traditional heterogeneous DB integration:
Build wrappers/mediators on top of heterogeneous databases
Query driven approach
When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set
Complex information filtering, compete for resources
Data warehouse: update-driven, high performance
Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis

9. Data Warehouse vs. Operational DBMS
OLTP (on-line transaction processing)
Major task of traditional relational DBMS
Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc.
OLAP (on-line analytical processing)
Major task of data warehouse system
Data analysis and decision making
Distinct features (OLTP vs. OLAP):
User and system orientation: customer vs. market
Data contents: current, detailed vs. historical, consolidated
Database design: ER + application vs. star + subject
View: current, local vs. evolutionary, integrated
Access patterns: update vs. read-only but complex queries

10. OLTP vs. OLAP

11. Why Separate Data Warehouse?
High performance for both systems
DBMS— tuned for OLTP: access methods, indexing, concurrency control, recovery
Warehouse—tuned for OLAP: complex OLAP queries, multidimensional view, consolidation.
Different functions and different data:
missing data: Decision support requires historical data which operational DBs do not typically maintain
data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources
data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled

12. Chapter 2: Data Warehousing and OLAP Technology for Data Mining
What is a data warehouse?
A multi-dimensional data model
Data warehouse architecture
Data warehouse implementation
Further development of data cube technology
From data warehousing to data mining

13. From Tables and Spreadsheets to Data Cubes
A data warehouse is based on a multidimensional data model which views data in the form of a data cube
A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions
Dimensions
A sale data warehouse with respect to dimension
time, item, branch, location
Dimension tables, such as
item (item_name, brand, type), or
time(day, week, month, quarter, year)
Facts: numerical measures
dollars_sold: sales amount in dollars
units_sold: number of units sold
Fact table contains measures (such as dollars_sold) and keys to each of the related dimension tables

14. A 2-D data cube: A table or spreadsheet for sales from AllElectronics
AllElectronics sales data for items sold per
quarter in the city of Istanbul
Time dimension organized in quarters
item dimension by types of items sold
The fact or measure is dollar_sold

15. A three dimensional cube
Dimensions: time, item, location for the cities

16. A Data Cube Representation of the same data
Sales volume as a function of product, month, and region
Dimensions: Product, Location, Time
Hierarchical summarization paths
locations
izmir
ankara
istabbul
items
phon
comp
Quarters Q1

17. A 4-D Cube as a series of 3-D cubes
Dimensions: item,time,location,supplier
From
Supplier A
From
Supplier B
From
Supplier C
locations
items
Quarters

18. n-Dimensional Cube Any n-D data as a series of (n-1)-D “cubes”
In data warehousing literature,
A data cube is referred to as a cuboid
The lattice of cuboids forms a data cube.
The cuboid holding the lowest level of summarization is called a base cuboid.
the 4-D cuboid is the base cuboid for the given four dimensions
The top most 0-D cuboid, which holds the highest-level of summarization, is called the apex cuboid.
Here this is the total sales, or dollars_sold summarized over all four dimensions
typically denoted by all

19. Cube: A Lattice of Cuboids
all
0-D(apex) cuboid
time
item
location
supplier
1-D cuboids
time,item
time,location
item,location
location,supplier
2-D cuboids
time,supplier
item,supplier
time,location,supplier
time,item,location
3-D cuboids
time,item,supplier
item,location,supplier
4-D(base) cuboid
time, item, location, supplier

20. Conceptual Modeling of Data Warehouses
Modeling data warehouses: dimensions & measures
Star schema: A fact table in the middle connected to a set of dimension tables
Snowflake schema: A refinement of star schema where some dimensional hierarchy is normalized into a set of smaller dimension tables, forming a shape similar to snowflake
Fact constellations: Multiple fact tables share dimension tables, viewed as a collection of stars, therefore called galaxy schema or fact constellation

21. Example of Star Schema item branch time Sales Fact Table time_key
day
day_of_the_week
month
quarter
year
time
item_key
item_name
brand
type
supplier_type
item
Sales Fact Table
time_key
item_key
branch_key
branch_key
branch_name
branch_type
branch
location_key
street
city
province_or_street
country
location
location_key
units_sold
dollars_sold
avg_sales
Measures

23. Example of Snowflake Schema
time_key
day
day_of_the_week
month
quarter
year
time
item_key
item_name
brand
type
supplier_key
item
supplier_key
supplier_type
supplier
Sales Fact Table
time_key
item_key
branch_key
location_key
street
city_key
location
branch_key
branch_name
branch_type
branch
location_key
units_sold
city_key
city
province_or_street
country
dollars_sold
avg_sales
Measures

24. A unnormalized City Table
City province and
Country is repeated
For every steet in
İstanbul
A Normalized City Table
Unnecessary repitations of province end country are eliminated
Memory gain but complex queries

25. Example of Fact Constellation
time_key
day
day_of_the_week
month
quarter
year
time
item_key
item_name
brand
type
supplier_type
item
Shipping Fact Table
Sales Fact Table
time_key
item_key
time_key
shipper_key
item_key
from_location
branch_key
branch_key
branch_name
branch_type
branch
location_key
to_location
location_key
street
city
province_or_street
country
location
dollars_cost
units_sold
units_shipped
dollars_sold
avg_sales
shipper_key
shipper_name
location_key
shipper_type
shipper
Measures

26. Example The relational database scheme for AE:
time(time_key,day,day_of_week,month,quarter,year)
item(item_key,item_name,brand,type,supplier_type)
branch(branch_key,branch_name,branch_type)
location(location_key,street,city,province_or_state,country)
sales(time_key,item_key,branch_key,location_key,number_of_units_sold,price)

27. Example cont.
Select s.time_key,s.item_key,s.branch_key,s.location_key,
sum(s.number_of_units_sold*s.price),
sum(s.number_of_units_sold)
from time t, item i,branch b,location l,sales s,
where s.time_key=t.time_key and
s.item_key=i.item_key and
s.branch_key=b.branch_key and
s.location_key=l.location_key
group by s.time_key, s.item_key, s.branch_key,s.location_key

28. Example Cont.
The cube created is the base cuboid of the sales_star datacube
it contains all of the dimensions
granularity of each is at the join key level
by changing the group by clauses
E.g.,
group by t.month: sum up the measures of each group by month
removing group by s.branch_key: generate a higher-level cuboid
recoving all group bys: total sum of dollars sold and total count of units_sold
zero-dimensional cuboid is apex cuboid

29. Time by month
Select t.year,t.month,s.item_key,s.branch_key,s.location_key,sum(s.number_of_units_sold*s.price),sum(s.number_of_units_sold)
from time t, item i,branch b,location l,sales s,
where s.time_key=t.time_key and
s.item_key=i.item_key and
s.branch_key=b.branch_key and
s.location_key=l.location_key
group by t.year, t.month, s.item_key, s.branch_key,s.location_key

30. A three dimensional cuboid
Select s.time_key,s.item_key, s.location_key,
sum(s.number_of_units_sold*s.price),
sum(s.number_of_units_sold)
from time t, item i,branch b,location l,sales s,
where s.time_key=t.time_key and
s.item_key=i.item_key and
s.branch_key=b.branch_key and
s.location_key=l.location_key
group by s.time_key, s.item_key,s.location_key

31. Concept hierarchies
Defines a sequence of mappings from a set of low-level concepts to high-level more general concepts
E.g., dimension location is described by
number,street,city,province_or_state,zipcode and country
are related by a total order, forming a concept hierarchy
street<city<province_or_state<country
The attributes of a dimension may be organized in a partial order, forming a lattice
day,week,month,quarter, year
day<[month<quarter,week]<year

32. A Concept Hierarchy: Dimension (location)
all
all
Europe
...
North_America
region
Germany
...
Spain
Canada
...
Mexico
country
Vancouver
...
city
Frankfurt
...
Toronto
L. Chan
...
M. Wind
office

33. Partially ordered co
The attributes of a dimension may be organized in a partial order, forming a lattice
day,week,month,quarter, year
day<[month<quarter,week]<year
predefined in the data mining system
time
fiscal year starting on April 1
academic year starting on September 1

34. Multidimensional Data
Sales volume as a function of product, month, and region
Dimensions: Product, Location, Time
Hierarchical summarization paths
Region
Industry Region Year
Category Country Quarter
Product City Month Week
Office Day
Product
A partially ordered hierarchy
Month

35. Cuboids Corresponding to the Cube
all
0-D(apex) cuboid
item
location
time
1-D cuboids
item,time
item,location
time, location
2-D cuboids
3-D(base) cuboid
item, time, location

36. Set grouping hierarchy
discretizing or grouping values for a given dimension or attribute
Ex: price
There may be more than one concept hierarchy for a given attribute or dimension based on different user viewpoints
price by defining ranges for
inexpensive, moderately_priced,expensive

37. How defined? provided by manually by system users domain experts
knowledge engineers or
automatically generated based on statistical analysis of the data distribution

38. Typical OLAP Operations
In the multidimensional model data are organized into multiple dimensions
each dimension contains multiple levels of abstraction defined by concept hierarchies
This organization provides users with the flexibility to view data from different perspectives

39. Example Refer to figure 2.10 in Han’s book data cube for AllElls sales
dimensions: location,time,item
location -- city
time -- quarters
item -- item types
measure displayed is dollars-sold

40. Cuboids Corresponding to the Cube
all
0-D(apex) cuboid
item
location
time
1-D cuboids
item,time
item,location
time, location
2-D cuboids
3-D(base) cuboid
item, time, location

41. Roll-up (drill-up) Climbing up a concept hierarchy for a dimension or
by dimension reduction
Ex:roll-up operation aggregates data by ascending the location hierarchy
from the level of city
to the level of country
rather than grouping the data by city,the cubes
groups the data by country

42. By a drill up opperation examine sales
By country rather than city level
roll up

43. Two dimensional cuboid One dim. cuboid
when performed by dimension reduction
one or more dimensions are removed from the cube
Ex a sales cube with location and time
roll-up may remove the time dimension
aggregation of total sales by location
rather than by location and by time
Two dimensional cuboid
One dim. cuboid

44. Drill-down (roll-down)
reverse of roll-up
navigates from less detailed data to more detailed data
from higher level summary to lower level summary or detailed data, or
stepping down a concept hierarchy for a dimension
introducing new dimensions
Ex: drill-down for time
day<month<quarter<year
form the level of quarter
to the more detailed level of month
Adding a new dimension to the data

45. Drill down

46. Slice and dice Slice: a selection on one dimension of the cube
resulting in subcube
Ex: sale data are selected for dimension time using time =Q1
dice: defines a subcube by performing a selection on two or more dimensions
Ex: a dice opp. Based on
location=“toronto” or “vencover” and
time =Q1 or Q2 and
item = “home entertainment” or “computer”

47. slice
dice

48. Pivot (rotate)
Visualization opp. Rotates the data axes in view to provide an alternative presentation of data
Ex:item and location axes in a 2-D slice are rotated
or transforming a 3-D cube into a series of 2-D planes

49. Other OLAP operations
drill across: involving (across) more than one fact table
drill through: through the bottom level of the cube to its back-end relational tables (using SQL)
ranking the top N or bottom N items in lists
moving averages
growth rates
interests

50. Parent Child Dimensions
Based on two dimension table columns that together define the lieage relationships among the members of the dimension
Member key column:identifies each member
Parent key column: identifies the parent of each member

51. Example: A HR dimension
Poal West
James Smith
Amy Joens
Jill Kelly
John Grande
Jo Brown

52. A Concept Hierarchy: Dimension (location)
all
all
Europe
...
North_America
region
Germany
...
Spain
Canada
...
Mexico
country
Vancouver
...
city
Frankfurt
...
Toronto
L. Chan
...
M. Wind
office

53. Example: Parent Child Dimension
Emp._Name Emp_Number Man_Emp_Num
James Smith
Amy Jones
Paul West
Jill Kelly
John Grande
Jo Brown
Emp_Num identifies each member
Man_Emp_Nun identifies the parent of each member

54. A Star-Net Query Model Radial lines from a central point
each line represents a concept hierarchy for a dimension
each abstraction level is called a footprint
granularities available for use by OLAP
Ex:figure 2.11
four radial lines,for concept hierarchies
location,customer,item,time
time line has 4 footprints:
day,month,quarter,year

55. A Star-Net Query Model Each circle is called a footprint
Customer Orders
Shipping Method
Customer
CONTRACTS
AIR-EXPRESS
ORDER
TRUCK
PRODUCT LINE
Time
Product
ANNUALY
QTRLY
DAILY
PRODUCT ITEM
PRODUCT GROUP
CITY
SALES PERSON
COUNTRY
DISTRICT
REGION
DIVISION
Each circle is called a footprint
Location
Promotion
Organization

56. Chapter 2: Data Warehousing and OLAP Technology for Data Mining
What is a data warehouse?
A multi-dimensional data model
Data warehouse architecture
Data warehouse implementation
Further development of data cube technology
From data warehousing to data mining

57. Design of a Data Warehouse: A Business Analysis Framework
Four views regarding the design of a data warehouse
Top-down view
allows selection of the relevant information necessary for the data warehouse
Data source view
exposes the information being captured, stored, and managed by operational systems
Data warehouse view
consists of fact tables and dimension tables
Business query view
sees the perspectives of data in the warehouse from the view of end-user

58. Data Warehouse Design Process
Top-down, bottom-up approaches or a combination of both
Top-down: Starts with overall design and planning (mature)
Bottom-up: Starts with experiments and prototypes (rapid)
From software engineering point of view
Waterfall: structured and systematic analysis at each step before proceeding to the next
Spiral: rapid generation of increasingly functional systems, short turn around time, quick turn around
Typical data warehouse design process
Choose a business process to model, e.g., orders, invoices, etc.
Choose the grain (atomic level of data) of the business process
Choose the dimensions that will apply to each fact table record
Choose the measure that will populate each fact table record

59. Multi-Tiered Architecture
Operational
DBs
other
sources
Monitor
&
Integrator
OLAP Server
Metadata
Extract
Transform
Load
Refresh
Analysis
Query
Reports
Data mining
Serve
Data
Warehouse
Data Marts
Data Sources
Data Storage
OLAP Engine
Front-End Tools

60. Three Data Warehouse Models
Enterprise warehouse
collects all of the information about subjects spanning the entire organization
Data Mart
a subset of corporate-wide data that is of value to a specific groups of users. Its scope is confined to specific, selected groups, such as marketing data mart
Independent vs. dependent (directly from warehouse) data mart
Virtual warehouse
A set of views over operational databases
Only some of the possible summary views may be materialized

61. Data Warehouse Development: A Recommended Approach
Multi-Tier Data Warehouse
Distributed Data Marts
Enterprise Data Warehouse
Data Mart
Data Mart
Model refinement
Model refinement
Define a high-level corporate data model

62. Storage of the cube Cuboids are referred as aggregations
One factor affecting storage requirements
Sparsity: the amount of empty cells in a cube
The base cuboid is likely to contain many empty cells
it is a spares cube or array
the 0 or lower dimensional cuboids are less spares than the higher dimensional ones
it is not likely that they contain empty cells
Moving along higher levels for the dimension hierarchy
the cuboids becomes less spares or more dense

63. Two dimensional sparse cuboidPC
Prt CD DV 10 1 4 2
items 10 1 6 2
One dimensional
Densed cuboid

64. OLAP Server Architectures Relational OLAP (ROLAP)
Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middle ware to support missing pieces
query response is generally slower
low storage requirement
Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services
greater scalability
appropriate for large data sets that are infrequently queried
historical data from less recent previous years

65. Multidimensional OLAP (MOLAP)
Array-based multidimensional storage engine (sparse matrix techniques)
fast indexing to pre-computed summarized data
a two-level storage representation
dense subcubes are stored as array structures
spars subcubes are stored by compression techniques
appropriate for cubes with frequent use and rapid query response

66. Hybrid OLAP (HOLAP) combines ROLAP and MOLAP benefiting from
greater scalability of ROLAP
faster computation of MOLAP
Large volumes of data base cuboid is stored in a relational database
aggregations are stored as arrays
appropriate for for cubes that requre
rapid query response for summaries based on a large amount of base data

67. Efficient Data Cube Computation
Data cube can be viewed as a lattice of cuboids
The bottom-most cuboid is the base cuboid
The top-most cuboid (apex) contains only one cell
Example: A data cube containing
item,city,year as dimensions and
sales_in_$ as measure
typical queries are
compute sum of sales, grouping by item and city
compute sum of sales, grouping by item
compute sum of sales, grouping by city
what is the total number of cuboids or group by s for this data cube
23=8

68. Cuboids Corresponding to the Cube
all
0-D(apex) cuboid
country
product
date
1-D cuboids
product,date
product,country
date, country
2-D cuboids
3-D(base) cuboid
product, date, country

69. Efficient Data Cube Computation
How many cuboids in an n-dimensional cube with 1 levels?
2n cuboids including the base cuboid
in OLAP compute all or at least some of the cuboids in advance
fast response time
avoids some redundant computation

70. Number of cuboids excluding the top level all
if the cube has many dimensions with multiple level hierarchies
T is total number of cuboids
Li is the number of levels associated with dimension i
excluding the top level all
as generalizing to all is equivalent to the removal of a dimension

71. Materialization of data cube
There are three choices for data cube materialization given a base cuboid:
Materialize every (cuboid) (full materialization),
huge amounts of memory space
none (no materialization), or
slow processing of queries
some (partial materialization)
trade-off between storage space and response time
Selection of which cuboids to materialize
Based on size, sharing, access frequency, etc.
A heuristic approach for cuboid selection
materialize the set of cuboids on which other popularly referenced cuboids are based

72. Processing Cubes Complete load of the cube:
all dimension and fact table data is read and
all specified aggregations cuboids are calculated
process a cube when
its structure is new or
its dimensions or measures have been edited
Incrementally updating a cube:
new data is added but existing data not changed and cube structure si the same
Refreshing:
data cleared and reloaded
its aggregations recalculated
faster then processing:no design of aggregation tables

73. Calculated Members
Dimension member or measure whose value is computed at run time using an expression
Only the definitions are stored but values exists only in memory upon a query
do not increase in cube size
Ex: if sales and cost are included in the base fact table
a profit measure can be a calculated member
profit = sales – cost
Average_sales = sales/#_items_sold

74. Virtual cubes Combination of multiple cubes in one logical cube
can be based on a single cube to expose only selected subsets of measures and dimensions
Require no physical space
store only the dimensions information not actual data
provide a valuable security functiton
limiting the access of some users

75. Member Properties Attribute of a dimension member
provides additional information about the member
a column in the same dimension table as the associated members
used in queries
provide users more options when analysing cube data

76. Example: time table A typical time table:
(time_id,day,month,quarter,year,business day,leap,day of the week)
dimension levels day<month<quarter<year
member properties for day:
weekend or business day:0 or 1
day of the week:1,2,3,...,7
a member property for year is
whether it is leap year or not:0 or 1

77. Virtual Dimensions
Logical dimension based on a member property of a level in a physical dimension
enables users to analyze cube data based on the member properties of dimension levels
add a virtual dimension to a cube only if
the dimension that supplies its member property is also included in the cube
adding a virtual dimension does not increase cube size
not affect cube processing time
calculated in memory when needed
query processing time is slower

78. Example
The business day column was a member property for day level of the time dimension
the user may want to investigate sales by
type of day (business or weekend)
makes business day member property as a virtual dimension of the sale cube

79. Exercise
How do you represent the same organizational chart by treditional concept hyerarchies?

80. Data Warehouse Usage Three kinds of data warehouse applications
Information processing
supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts and graphs
Analytical processing
multidimensional analysis of data warehouse data
supports basic OLAP operations, slice-dice, drilling, pivoting
Data mining
knowledge discovery from hidden patterns
supports associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools.
Differences among the three tasks

81. From On-Line Analytical Processing to On Line Analytical Mining (OLAM)
Why online analytical mining?
High quality of data in data warehouses
DW contains integrated, consistent, cleaned data
Available information processing structure surrounding data warehouses
ODBC, OLEDB, Web accessing, service facilities, reporting and OLAP tools
OLAP-based exploratory data analysis
mining with drilling, dicing, pivoting, etc.
On-line selection of data mining functions
integration and swapping of multiple mining functions, algorithms, and tasks.
Architecture of OLAM

82. An OLAM Architecture Mining query Mining result OLAM Engine OLAP
Layer4
User Interface
User GUI API
OLAM
Engine
OLAP
Engine
Layer3
OLAP/OLAM
Data Cube API
Layer2
MDDB
MDDB
Meta Data
Database API
Filtering&Integration
Filtering
Layer1
Data Repository
Data cleaning
Data
Warehouse
Databases
Data integration

83. Summary Data warehouse A multi-dimensional model of a data warehouse
A subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process
A multi-dimensional model of a data warehouse
Star schema, snowflake schema, fact constellations
A data cube consists of dimensions & measures
OLAP operations: drilling, rolling, slicing, dicing and pivoting
OLAP servers: ROLAP, MOLAP, HOLAP
Efficient computation of data cubes
Partial vs. full vs. no materialization
Multiway array aggregation
Bitmap index and join index implementations
Further development of data cube technology
Discovery-drive and multi-feature cubes
From OLAP to OLAM (on-line analytical mining)

84. References (I)
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OLAP council. MDAPI specification version 2.0. In
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85. References (II)
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