1. 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

2. What is Data Warehouse? Defined in many different ways
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

3. 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.

4. 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
When data is moved to the warehouse, it is converted.

5. 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”.

6. 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.

7. Data Warehouse vs. Heterogeneous DBMS
Many Organizations may collect diverse kinds of Heterogeneous Dbases such as Multiple, Autonomous and Distributed Information sources
Integration of these are must for easy and efficient access
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

8. 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
View: current, local vs. evolutionary, integrated
Access patterns: update vs. read-only but complex queries

9. OLTP vs. OLAP

10. Why Separate Data Warehouse?
We know that Operational Databases store huge amounts of data
Why not perform ILAP directly on such Dbases instead of spending additional time and resources to construct a separate data warehouse?
The Major reason is…

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. 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. Data Warehousing and OLAP Technology for Data Mining
A data warehouse is based on a multi-dimensional data model which views data in the form of a data cube
Data Cube allowed to be modeled multiple dimensions
A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions
Dimension tables, such as item (item_name, brand, type), or time(day, week, month, quarter, year)
Fact table contains measures (such as dollars_sold) and keys to each of the related dimension tables
In data warehousing literature, an n-D base cube is called a base cuboid. The top most 0-D cuboid, which holds the highest-level of summarization, is called the apex cuboid. The lattice of cuboids forms a data cube.

14. Cube: A Lattice of Cuboids
all
Highest Level of Summarization 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

15. Conceptual Modeling of Data Warehouses
The most popular data model for a data warehouse is a Multidimensional Model
There are different forms
Star schema
Snowflake schema
Fact constellations schema

16. Star Schema This is the most common modeling paradigm
It contains a large central table called fact table , which containing the bulk of data with no redundancy
And it has a set of smaller attendant tables called dimension tables for each dimension

17. Example of Star Schema Supplier / Location are redundant
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
Attributes such as
location_key
street
city
province_or_street
country
location
location_key
units_sold
dollars_sold
avg_sales
Measures

18. Defining a Star Schema in DMQL
define cube sales_star [time, item, branch, location]:
dollars_sold = sum(sales_in_dollars), avg(sales_in_dollars), units_sold = count(*)
define dimension time as (time_key, day, day_of_week,
month, quarter, year)
define dimension item as (item_key, item_name, brand,
type, supplier_type)
define dimension branch as (branch_key, branch_name,
branch_type)
define dimension location as (location_key, street, city,
province_or_state, country)

19. 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

20. 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

21. Defining a Snowflake Schema in DMQL
define cube sales_snowflake [time, item, branch, location]:
dollars_sold = sum(sales_in_dollars),
define dimension time as (time_key, day, day_of_week, month, quarter, year)
define dimension item as (item_key, item_name, brand, type, supplier(supplier_key, supplier_type))
define dimension branch as (branch_key, branch_name, branch_type)
define dimension location as (location_key, street, city(city_key, province_or_state, country))

22. Fact constellations
Sophisticated applications may require multiple fact tables to share dimension tables
It can be viewed as a collection of stars, therefore called galaxy schema or fact constellation
Two fact tables : sales and shipping

23. 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

24. Defining a Fact Constellation in DMQL
define cube sales [time, item, branch, location]:
dollars_sold = sum(sales_in_dollars), avg_sales = avg(sales_in_dollars), units_sold = count(*)
define dimension time as (time_key, day, day_of_week, month, quarter, year)
define dimension item as (item_key, item_name, brand, type, supplier_type)
define dimension branch as (branch_key, branch_name, branch_type)
define dimension location as (location_key, street, city, province_or_state, country)
define cube shipping [time, item, shipper, from_location, to_location]:
dollar_cost = sum(cost_in_dollars), unit_shipped = count(*)
define dimension time as time in cube sales
define dimension item as item in cube sales
define dimension shipper as (shipper_key, shipper_name, location as location in cube sales, shipper_type)
define dimension from_location as location in cube sales
define dimension to_location as location in cube sales

25. Data Warehouse vs Data Mart
DWarehouse collects the information about the subjects of the entire organizations
Fact constellation should be used
DMart focuses on selected subjects like department-wide
Star or Snowflake can be used

26. 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
Month

27. A Sample Data Cube All, All, All Date Product Country
Total annual sales
of TV in U.S.A.
Date
Product
Country
All, All, All
sum TV
VCR PC
1Qtr
2Qtr
3Qtr
4Qtr
U.S.A
Canada
Mexico

29. Data Preprocessing Data Cleaning
Data Integration and Transformation and
Data Reduction

30. Why Data Preprocessing?
Data in the real world is dirty
incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data
noisy: containing errors or outliers
inconsistent: containing discrepancies in codes or names
No quality data, no quality mining results!
Quality decisions must be based on quality data
Data warehouse needs consistent integration of quality data

31. Multi-Dimensional Measure of Data Quality
A well-accepted multidimensional view:
Accuracy
Completeness
Consistency
Timeliness
Believability
Value added
Interpretability
Accessibility
Broad categories:
intrinsic, contextual, representational, and accessibility.

32. Major Tasks in Data Preprocessing
Data Cleaning
Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies
Data Integration
Integration of multiple databases, data cubes, or files
Data Transformation
Normalization and aggregation
Data Reduction
Obtains reduced representation in volume but produces the same or similar analytical results

33. Forms of data preprocessing

34. Data Cleaning Data cleaning tasks Fill in missing values
Identify outliers and smooth out noisy data
Correct inconsistent data

35. Missing Data Data is not always available
E.g., many tuples have no recorded value for several attributes, such as customer income in sales data
Missing data may be due to
equipment malfunction
inconsistent with other recorded data and thus deleted
data not entered due to misunderstanding
certain data may not be considered important at the time of entry
not register history or changes of the data
Missing data may need to be inferred.

36. How to Handle Missing Data?
Ignore the tuple: usually done when class label is missing (assuming the tasks in classification—not effective when the percentage of missing values per attribute varies considerably.
Fill in the missing value manually: tedious + infeasible?
Use a global constant to fill in the missing value: e.g., “unknown”, a new class?!
Use the attribute mean to fill in the missing value
Use the attribute mean for all samples belonging to the same class to fill in the missing value: smarter
Use the most probable value to fill in the missing value: inference-based such as Bayesian formula or decision tree

37. Noisy Data Noise: random error or variance in a measured variable
Incorrect attribute values may due to
faulty data collection instruments
data entry problems
data transmission problems
technology limitation
inconsistency in naming convention
Other data problems which requires data cleaning
duplicate records
incomplete data
inconsistent data

38. How to Handle Noisy Data?/ Smoothing
Binning method( for Data Transformation)
first sort data and partition into (equi-depth) bins
then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc.
Clustering
detect and remove outliers
Combined computer and human inspection
detect suspicious values and check by human
Regression
smooth by fitting the data into regression functions

39. Cluster Analysis

40. Regression y Y1
Y1’
y = x + 1 X1 x

41. Data Integration Data integration:
combines data from multiple sources into a coherent store
Schema integration
integrate metadata from different sources
Entity identification problem: identify real world entities from multiple data sources, e.g., A.cust-id  B.cust-#
Detecting and resolving data value conflicts
for the same real world entity, attribute values from different sources are different
possible reasons: different representations, different scales, e.g., metric vs. British units

42. Handling Redundant Data in Data Integration
Redundant data occur often when integration of multiple databases
The same attribute may have different names in different databases
One attribute may be a “derived” attribute in another table, e.g., annual revenue
Redundant data may be able to be detected by correlational analysis
Careful integration of the data from multiple sources may help reduce/avoid redundancies and inconsistencies and improve mining speed and quality

43. Data Transformation
Smoothing: remove noise from data ( binning, clustering, Regression)
Aggregation: Daily sales data may be aggreagated to so as to compute monthly / yearly
Which is used for analysis
ie summarization or data cube construction
Generalization: low-level data replaced by higher-level data
Street to city/country
Age to like young, middle-age or senior

44. Data Transformation
Normalization: scaled to fall within a small, specified range
-1.0 to 1.0 or 0.0 to 1.0
There are three types
Min-Max
Z-score and
Normalization by decimal scaling
Attribute/feature construction
New attributes constructed from the given ones tp help the mining process

45. Data Transformation: Normalization
min-max normalization
z-score normalization
normalization by decimal scaling
Where j is the smallest integer such that Max(| |)<1

46. Data Reduction Strategies
Warehouse may store terabytes of data: Complex data analysis/mining may take a very long time to run on the complete data set
Data reduction
Obtains a reduced representation of the data set that is much smaller in volume but yet produces the same (or almost the same) analytical results
Data reduction strategies
Data cube aggregation
Dimensionality reduction
Data Compression
Numerosity reduction

47. Data Cube Aggregation
Minimizing information from Multidimensional analysis of sales

48. Dimensionality Reduction
Data sets for analysis may contain hundreds of attributes
Many of which may be irrelavant to the mining task or redundant
It reduces the data set size by removing such attributes from it
Reduction Techniques
step-wise forward selection – procedure starts with empty set of attributes and the best attribute is added to the set
step-wise backward elimination
combining forward selection and backward elimination
decision-tree induction- Tree constructed with the given data. If Attributes that do not appear in the tree are declared irrelevant

49. Example of Decision Tree Induction
Initial attribute set:
{A1, A2, A3, A4, A5, A6}
A4 ?
A1?
A6?
Class 2
Class 1
Class 2
Class 1
Reduced attribute set: {A1, A4, A6}
>

50. Data Compression String compression
There are extensive theories and well-tuned algorithms
Typically lossless
But only limited manipulation is possible without expansion
Audio/video compression
Typically lossy compression, with progressive refinement
Sometimes small fragments of signal can be reconstructed without reconstructing the whole
Time sequence is not audio
Typically short and vary slowly with time

51. Data Compression Original Data Compressed Data lossless lossy
Approximated
lossy

52. Numerosity Reduction Parametric methods
Assume the data fits some model, estimate model parameters, store only the parameters, and discard the data (except possible outliers)
Log-linear models: obtain value at a point in m-D space as the product on appropriate marginal subspaces
Non-parametric methods
Do not assume models
Major families: histograms, clustering, sampling

53. Regression and Log-Linear Models
Linear regression: Data are modeled to fit a straight line
Often uses the least-square method to fit the line
Multiple regression: allows a response variable Y to be modeled as a linear function of multidimensional feature vector
Log-linear model: approximates discrete multidimensional probability distributions

54. Regress Analysis and Log-Linear Models
Linear regression: Y =  +  X
Two parameters ,  and  specify the line and are to be estimated by using the data at hand.
using the least squares criterion to the known values of Y1, Y2, …, X1, X2, ….
Multiple regression: Y = b0 + b1 X1 + b2 X2.
Many nonlinear functions can be transformed into the above.
Log-linear models:
The multi-way table of joint probabilities is approximated by a product of lower-order tables.
Probability: p(a, b, c, d) = ab acad bcd

55. Histograms A popular data reduction technique
Divide data into buckets and store average (sum) for each bucket
Can be constructed optimally in one dimension using dynamic programming
Related to quantization problems.

56. Clustering
Partition data set into clusters, and one can store cluster representation only
Can be very effective if data is clustered but not if data is “smeared”
Can have hierarchical clustering and be stored in multi-dimensional index tree structures
There are many choices of clustering definitions and clustering algorithms, further detailed in Chapter 8

57. Sampling
Allow a mining algorithm to run in complexity that is potentially sub-linear to the size of the data
Choose a representative subset of the data
Simple random sampling may have very poor performance in the presence of skew
Develop adaptive sampling methods
Stratified sampling:
Approximate the percentage of each class (or subpopulation of interest) in the overall database
Used in conjunction with skewed data
Sampling may not reduce database I/Os (page at a time).

58. Sampling SRSWOR (simple random sample without replacement) SRSWR
Raw Data
SRSWOR
(simple random
sample without
replacement)
SRSWR

59. Sampling
Raw Data
Cluster/Stratified Sample

60. OLAP What is On-Line Analytical Processing - OLAP?
OLAP is technology that uses a multidimensional view of aggregate data to provide quick access to strategic information for further analysis.
Allows fast and efficient analysis of data by turning raw data into information that can be understood by users and manipulated in various ways.
OLAP applications require multidimensional views of data, and usually, calculation-intensive capabilities and time intelligence.

61. OLAP Need for OLAP To solving Modern Business Problems Market Analysis
Financial Forecasting
Multidimensional Data Model
OLAP Guidelines ie it should support for
Transparency
Accessibility
Consistent Reporting Performance
Client/Server Architecture
Multiuser Support
Flexible Reporting
SQL Interface, Incremental Dbase Refresh

62. OLAP Multidimensional versus MultiRelational OLAP
Multidimensional Dbase Systems are referred to as Multirelational Systems
ie to increase the speed to extract the knowledge, the Star or Snowflake schemas are used

63. OLAP Categorization of OLAP Tools It uses Multidimensional Dbase MDDB
MDDBASES + OLAP = MOLAP
RDBMS + OLAP = ROLAP
Managed Query Environment MQE or HYBRID Architecture