Chpt2.ppt

Data Warehousing/Mining 1
Data Warehousing/Mining
Comp 150 DW
Data Warehousing and OLAP
Technology for Data Mining
Instructor: Dan Hebert

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

What is a 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

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.

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.

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”.

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.

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

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

OLTP vs. OLAP
OLTP OLAP
users clerk, IT professional knowledge worker
function day to day operations decision support
DB design application-oriented subject-oriented
data current, up-to-date
detailed, flat relational
isolated
historical,
summarized, multidimensional
integrated, consolidated
usage repetitive ad-hoc
access read/write
index/hash on prim. key
lots of scans
unit of work short, simple transaction complex query
# records accessed tens millions
#users thousands hundreds
DB size 100GB-TB 100GB-TB
metric transaction throughput query throughput, response

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: Decision Support 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

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
– 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.

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

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

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

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

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

A Data Mining Query Language,
DMQL: Language Primitives
 Cube Definition (Fact Table)
define cube <cube_name> [<dimension_list>]:
<measure_list>
 Dimension Definition ( Dimension Table )
define dimension <dimension_name> as
(<attribute_or_subdimension_list>)

Defining a Star Schema in
DMQL
define cube sales_star [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)

Defining a Snowflake Schema in
DMQL
define cube sales_snowflake [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(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))

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

Measures: Three Categories
 distributive: if the result derived by applying the function
to n aggregate values is the same as that derived by
applying the function on all the data without partitioning.
 E.g., count(), sum(), min(), max().
 algebraic: if it can be computed by an algebraic function
with M arguments (where M is a bounded integer), each
of which is obtained by applying a distributive aggregate
function.
 E.g., avg(), min_N(), standard_deviation().
 holistic: if there is no constant bound on the storage size
needed to describe a subaggregate.
 E.g., median(), mode(), rank().

A Concept Hierarchy: Dimension
(location)
Mexico
all
Europe North_America
Canada
Spain
Germany
Vancouver
M. Wind
L. Chan
...
...
...
... ...
...
all
region
office
country
Toronto
Frankfurt
city
Define a sequence of mappings from a set of low
level concepts to higher-level, more general concepts

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

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

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

Browsing a Data Cube
 Visualization
 OLAP capabilities
 Interactive manipulation

Typical OLAP Operations
 Roll up (drill-up): summarize data
– by climbing up hierarchy or by dimension reduction
 Drill down (roll down): reverse of roll-up
– from higher level summary to lower level summary or detailed data, or
introducing new dimensions
 Slice and dice:
– project and select
 Pivot (rotate):
– reorient the cube, visualization, 3D to series of 2D planes.
 Other 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)

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

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

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

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

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

Data Warehouse Development:
A Recommended Approach
Define a high-level corporate data model
Data
Mart
Data
Mart
Distributed
Data Marts
Multi-Tier Data
Warehouse
Enterprise
Data
Warehouse
Model refinement
Model refinement
1
2 2
3
4

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
– Include optimization of DBMS backend, implementation of aggregation
navigation logic, and additional tools and services
– greater scalability
 Multidimensional OLAP (MOLAP)
– Array-based multidimensional storage engine (sparse matrix techniques)
– fast indexing to pre-computed summarized data
 Hybrid OLAP (HOLAP)
– Combination of ROLAP & MOLAP technology - User flexibility, e.g.,
low level: relational, high-level: array
 Specialized SQL servers
– specialized support for SQL queries over star/snowflake schemas

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
 Materialization (pre-computation) of data cube
– Materialize every (cuboid) (full materialization), none
(no materialization), or some (partial materialization)
– Selection of which cuboids to materialize
 Based on size, sharing, access frequency, etc.

Cube Operation
 Cube definition and computation in DMQL
define cube sales[item, city, year]: sum(sales_in_dollars)
compute cube sales
 Transform it into a SQL-like language (with a new operator cube by,
introduced by Gray et al.’96)
SELECT item, city, year, SUM (amount)
FROM SALES
CUBE BY item, city, year
 Need compute the following Group-Bys
(year, item, city),
(year,item),(year, city), (item, city),
(year), (item), (city)
()
(item)
(city)
()
(year)
(city, item) (city, year) (item, year)
(city, item, year)

Cube Computation: ROLAP-Based
Method
 Efficient cube computation methods
– ROLAP-based cubing algorithms (Agarwal et al’96)
– Array-based cubing algorithm (Zhao et al’97)
– Bottom-up computation method (Bayer & Ramarkrishnan’99)
 ROLAP-based cubing algorithms
– Sorting, hashing, and grouping operations are applied to the
dimension attributes in order to reorder and cluster related
tuples
– Grouping is performed on some subaggregates as a “partial
grouping step”
– Aggregates may be computed from previously computed
aggregates, rather than from the base fact table

Multi-way Array Aggregation for
Cube Computation
 Partition arrays into chunks (a small subcube which fits in memory).
 Compressed sparse array addressing: (chunk_id, offset)
 Compute aggregates in “multiway” by visiting cube cells in the order
which minimizes the # of times to visit each cell, and reduces
memory access and storage cost.
A
B
29 30 31 32
1 2 3 4
5
9
13 14 15 16
64
63
62
61
48
47
46
45
a1
a0
c3
c2
c1
c 0
b3
b2
b1
b0
a2 a3
C
B
44
28 56
40
24 52
36
20
60

Multi-Way Array Aggregation for
Cube Computation (Cont.)
 Method: the planes should be sorted and computed
according to their size in ascending order.
– See the details of Example 2.12 (pp. 75-78)
– Idea: keep the smallest plane in the main memory, fetch and
compute only one chunk at a time for the largest plane
 Limitation of the method: computing well only for a
small number of dimensions
– If there are a large number of dimensions, “bottom-up
computation” and iceberg cube computation methods can be
explored

Indexing OLAP Data: Bitmap Index
 Index on a particular column
 Each value in the column has a bit vector: bit-op is fast
 The length of the bit vector: # of records in the base table
 The i-th bit is set if the i-th row of the base table has the value for
the indexed column
 not suitable for high cardinality domains (many different values)
Cust Region Type
C1 Asia Retail
C2 Europe Dealer
C3 Asia Dealer
C4 America Retail
C5 Europe Dealer
RecID Retail Dealer
1 1 0
2 0 1
3 0 1
4 1 0
5 0 1
RecIDAsia Europe America
1 1 0 0
2 0 1 0
3 1 0 0
4 0 0 1
5 0 1 0
Base table Index on Region Index on Type

Indexing OLAP Data: Join Indices
 Join index: JI(R-id, S-id) where R (R-id, …)  S
(S-id, …)
 Traditional indices map the values to a list of
record ids
– It materializes relational join in JI file and speeds
up relational join — a rather costly operation
 In data warehouses, join index relates the values
of the dimensions of a start schema to rows in the
fact table.
– E.g. fact table: Sales and two dimensions city and
product
 A join index on city maintains for each distinct
city a list of R-IDs of the tuples recording the
Sales in the city (ditto for join index on item)
– Join indices can span multiple dimensions

Efficient Processing OLAP Queries
 Determine which operations should be performed on the
available cuboids:
– transform drill, roll, etc. into corresponding SQL and/or OLAP
operations, e.g, dice = selection + projection
 Determine to which materialized cuboid(s) the relevant
operations should be applied.
 Exploring indexing structures and compressed vs. dense
array structures in MOLAP

Metadata Repository
 Meta data is the data defining warehouse objects. It has
the following kinds
– Description of the structure of the warehouse
 schema, view, dimensions, hierarchies, derived data defn, data mart
locations and contents
– Operational meta-data
 data lineage (history of migrated data and transformation path),
currency of data (active, archived, or purged), monitoring information
(warehouse usage statistics, error reports, audit trails)
– The algorithms used for summarization
– The mapping from operational environment to the data warehouse
– Data related to system performance
 warehouse schema, view and derived data definitions
– Business data
 business terms and definitions, ownership of data, charging policies

Data Warehouse Back-End Tools and
Utilities
 Data extraction:
– get data from multiple, heterogeneous, and external sources
 Data cleaning:
– detect errors in the data and rectify them when possible
 Data transformation:
– convert data from legacy or host format to warehouse format
 Load:
– sort, summarize, consolidate, compute views, check integrity, and
build indicies and partitions
 Refresh
– propagate the updates from the data sources to the warehouse

Discovery-Driven Exploration of Data
Cubes
 Hypothesis-driven: exploration by user, huge search
space
 Discovery-driven (Sarawagi et al.’98)
– pre-compute measures indicating exceptions, guide user in the
data analysis, at all levels of aggregation
– Exception: significantly different from the value anticipated,
based on a statistical model
– Visual cues such as background color are used to reflect the
degree of exception of each cell

Discovery-Driven Exploration of Data
Cubes (2)
– Computation of exception indicator (modeling fitting and
computing SelfExp, InExp, and PathExp values) can be
overlapped with cube construction
– SelfExp – The degree of surprise of the cell value, relative to
other cells at the same level of aggregation
– InExp - The degree of surprise somewhere beneath the cell, if
one were to drill down from it
– Path - The degree of surprise for each drill-down path from the
cell

Examples: Discovery-Driven Data
Cubes Study data aggregated over all items, then translate
SelfExp & InExp into visual clues, the darker the color,
the greater the degree of exception
Drill
down on
item,
then
translate
SelfExp
& InExp
Drill
down
on
region

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

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

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

Summary
 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)

Homework Assignment
 Implement a Data Warehouse using a star
schema for the sales example in Chapter 2 page
49
 Import the following data into your database.
– SalesFact.txt, BranchDimension.txt,
ItemDimension.txt, LocationDimension.txt,
TimeDimension.txt
– Use “COPY” command in postgresql to copy data

Homework Assignment
(continued)
 Implement a program that allows you to do the
following :
– roll-up on total sales by year and by year/quarter
– roll-up on total sales by country, by state, and by city
– roll-up on total sales by item brand and by item type
(digital or analog)
– drill down on total sales by month and by day
– drill down on total sales by item name
– drill down on total sales by street address

Homework Assignment
(continued)
 Have a simple user interface to allow activation
of each of the functions
 Use SQL statements to implement each roll-up,
drill-down
 Due date – Feb 19 at the start of class

Homework Assignment
(continued)
 Example - Run drill down on total sales by
street address in Query Analyzer
– select sum(s.dollarssold) as totalsales, l.street from
SalesFactTable s, LocationDimensionTable l where
l.locationkey = s.locationkey group by l.street

Chpt2.ppt

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