Chapter 4, Data Warehouse and OLAP Operations · Chapter 4, Data Warehouse and OLAP Operations...
Transcript of Chapter 4, Data Warehouse and OLAP Operations · Chapter 4, Data Warehouse and OLAP Operations...
11/16/2017
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Chapter 4, Data Warehouse and OLAP Operations
Young-Rae Cho
Associate Professor
Department of Computer Science
Baylor University
CSI 4352, Introduction to Data Mining
Chapter 4, Data Warehouse & OLAP Operations
CSI 4352, Introduction to Data Mining
Basic Concept of Data Warehouse
Data Warehouse Modeling
Data Warehouse Architecture
Data Warehouse Implementation
From Data Warehousing to Data Mining
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What is Data Warehouse?
Data Warehouse ( defined in many different ways )
A decision support database that is maintained separately from the
organization’s operational database
The support of information processing by providing a solid platform of
consolidated, historical data for analysis
“A data warehouse is a (1) subject-oriented, (2) integrated,
(3) time-variant, and (4) 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
e.g., customers, products, 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
Excluding data that are not useful in the decision support process
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Data Warehouse – Integrated
Integrating multiple, heterogeneous data sources
Relational databases, flat files, on-line transaction records
Apply data cleaning and data integration techniques
Ensures consistency in naming conventions, encoding structures,
attribute measures, etc. among different data sources
Data Warehouse – Time Variant
The time horizon of data warehouses is significantly longer than that of
operational systems
Operational databases have current data values
Data warehouses provide information from a historical perspective
(e.g., 10-20 years)
Time is a key structure in data warehouses
Contain the attribute of time (explicitly or implicitly)
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Data Warehouse – Nonvolatile
A physically separate storage of data transformed from operational databases
Operational update of data does not occur
Not require transaction processing, recovery, and concurrency control
mechanisms
Require only two operations, initial loading of data and access of data
Data Integration Methods
Methods
(1) Process to provide uniform interface to multiple data sources
→ Tradition Database Integration
(2) Process to combine multiple data sources into coherent storage
→ Data warehousing
Traditional DB Integration
A query-driven approach
Wrappers / mediators on top of heterogeneous data sources
Data Warehousing
An update-driven approach
Combined the heterogeneous data sources in advance
Stored them in a warehouse for direct query and analysis
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OLTP vs. OLAP
OLTP (on-line transaction processing)
Major task of traditional relational DBMS
Day-to-day operations: e.g., purchasing, inventory, manufacturing,
banking, 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 (customers vs. market analysts)
Data contents (current, detailed vs. historical, consolidated)
Database design (ER + application vs. star + subject)
View (current, local vs. evolutionary, integrated)
OLTP vs. OLAP
Feature OLTP OLAP
Characteristic operational processing information processing
Orientation transaction analysis
Users clerk, DBA knowledge worker (CEO, analyst)
Function day-to-day operations decision support
DB Design application-oriented subject-oriented
Data current, up-to-date historical, integrated, summarized
Unit of work short, simple transaction complex query
Access read/write/update read-only (lots of scans)
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Why Data Warehouse?
Performance Issue
DBMS: tuned for OLTP
e.g., access methods, indexing, concurrency control, recovery
Data warehouse: tuned for OLAP
e.g., complex queries, multidimensional view, consolidation
Data Issue
Decision support requires historical data, consolidated and summarized
data, consistent data
Chapter 4, Data Warehouse & OLAP Operations
CSI 4352, Introduction to Data Mining
Basic Concept of Data Warehouse
Data Warehouse Modeling
Data Warehouse Architecture
Data Warehouse Implementation
From Data Warehousing to Data Mining
11/16/2017
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Data Format for Warehouse
Dimensions
Multi-dimensional data model
Data are stored in the form of a data cube
Data Cube
A view of multi-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
Cuboid
Each combination of dimensional spaces in a data cube
0-D cuboid, 1-D cuboid, 2-D cuboid, … , n-D cuboid
The Lattice of Cuboids
all
time item location supplier
time,location
time,supplier
item,location
item,supplier
location,supplier
time,item,supplier
time,location,supplier
item,location,supplier
0-D (apex) cuboid
1-D cuboids
2-D cuboids
3-D cuboids
4-D (base) cuboid
time,item
time,item,location
time, item, location, supplier
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Conceptual Modeling
Key of Modeling Data Warehouses
Handling dimensions & measures
Examples
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_keydayday_of_the_weekmonthquarteryear
time
location_keystreetcitystatecountry
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_keyitem_namebrandtypesupplier_type
item
branch_keybranch_namebranch_type
branch
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Example of Snowflake Schema
supplier_keysupplier_type
supplier
city_keycitystatecountry
city
time_keydayday_of_the_weekmonthquarteryear
time
location_keystreetcity_key
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_keyitem_namebrandtypesupplier_key
item
branch_keybranch_namebranch_type
branch
Example of Fact Constellation
time_keydayday_of_the_weekmonthquarteryear
time
location_keystreetcitystatecountry
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
item_keyitem_namebrandtypesupplier_type
item
branch_keybranch_namebranch_type
branch
time_key
item_key
shipper_key
from_location
to_location
dollars_cost
units_shipped
shipper_keyshipper_namelocation_keyshipper_type
shipper
Shipping Fact Table
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Cube Definition in DMQL
Cube Definition (Fact Table)
define cube <cube_name> [<dimension_list>]: <measure_list>
Dimension Definition (Dimension Table)
define dimension <dimension_name> as (<attribute_or_dimension_list>)
Special Case (Shared Dimension Table)
define dimension <dimension_name> as <dimension_name_first>
in cube <cube_name_first>
Star Schema Definition in DMQL
Example
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, state, country)
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Snowflake Schema Definition in DMQL
Example
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(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))
Fact Constellation Schema Definition in DMQL
Example 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, 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
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Measures
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, each of which is obtained by applying a distributive aggregate function
e.g., avg(), standard_deviation()
Holistic
If there is no constant bound on the storage size needed to describe a subaggregate
e.g., median(), mode(), rank()
Concept Hierarchy
Schema Hierarchy
e.g., street < city < state < country
e.g., day < { month < quarter ; week } < year
Set-group hierarchy
e.g., { (0..100] ; (100..200] } < (0..200]
e.g., { (0..10] < lowPrice ; { (10..100] ; (100..200] } < highPrice }
< allProducts
Year
Quarter| Week
Month
Day
(0..200]
(0..100] (100..200]
allProducts
lowPrice highPrice
(0..10] (10..100] (100..200]
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Three Components of Data Cube
Example
Measures: data values as a function of products, locations and time
Dimensions:
Hierarchies:
Company|
Category|
Productloca
tions
time
Country|
City|
Office
Year
Quarter| Week
Month
Day
product location time
Example of Cuboid Cells
all
product quarter country
product, quarter
product, country
quarter, country
product, quarter, country
0-D (apex) cuboid
1-D cuboids
2-D cuboids
3-D (base) cuboid
product dimension
time dimension
location dimension
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Example of Data Cube
Total annual salesof TV in U.S.A.
Quarter
Cou
ntr
ysum
sumTV
VCRPC
1Qtr 2Qtr 3Qtr 4Qtr
U.S.A
Canada
Mexico
sum
OLAP Operations
Roll-up (drill-up)
Summarizes (aggregates) data
by climbing up hierarchy or by dimension reduction
Drill-down (roll-down)
Reverse of roll-up
by stepping down to lower-level data or introducing new dimensions
Slice
Selecting data on one dimension
Dice
Selecting data on multi-dimensions
Pivot (rotate)
Reorienting the cube, or transforming 3-D data to a series of 2-D spaces
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Roll-UP & Drill-Down
coun
try
quarter
city
quarter
coun
try
month
loca
tio
n
time
Slice, Dice & Pivot
coun
try
quarter
quarter
coun
try
quarter
loca
tio
n
time
Q1 Q2 Q3 Q4
USA
canada
mexico
pclaptop
supercom
coun
try
USA
canada
Q1 Q2
pclaptop
Q1 Q2 Q3 Q4
USA
canada
mexico
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Starnet Query Model
Shipping Method
air
truckorder
Orders
contracts
Customer
Productcategory
item
sales-person
division
division
OrganizationPromotion
citycountry
region
Location
dailyquarterlyannuallyTime
Each circle is called a footprint
customerID
Chapter 4, Data Warehouse & OLAP Operations
CSI 4352, Introduction to Data Mining
Basic Concept of Data Warehouse
Data Warehouse Modeling
Data Warehouse Architecture
Data Warehouse Implementation
From Data Warehousing to Data Mining
11/16/2017
17
Data Warehouse Design
Top-Down View
Allows the 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
Shows the perspectives of data in the warehouse to end-users
Data Warehouse Design Process
Categories by Process Direction
Top-down: Starts with overall design and planning (mature)
Bottom-up: Starts with experiments and prototypes (rapid)
Categories by Software Engineering View
Waterfall: structured, systematic analysis at each step before proceeding
to the next
Spiral: rapid generation of functional systems, short turn around time
Typical Data Warehouse Design Process
Choose business processes for modeling, e.g., orders, invoices, etc
Choose the grain (atomic level of data) of the business processes
Choose the dimensions that will apply to each fact table
Choose the measures that will populate each fact table
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Data Warehouse Architecture
DataWarehouse
ExtractTransformLoadRefresh
OLAP Engine
Query
Analysis
Reports
Monitor &IntegratorMetadata
Data Sources Front-End Tools
Serve
Data Marts
Operational DBs
Othersources
Data Storage
OLAP Server
Three Data Warehouse Models
Enterprise Warehouse
A global view with all the information about subjects spanning the entire
organization
Data Mart
A subset of corporate-wide data that is of value to a specific group of
users
Its scope is confined to specific, selected groups
Independent vs. dependent (directly from warehouse) data mart
Virtual Warehouse
A set of views over operational databases
Only some of possible summary views may be materialized
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Development of Data Warehouse
Define a high-level corporate data model
Model Refinement
Enterprise
Data
Warehouse
Multi-Tier
Data Warehouse
Distributed
Data Marts
Data Mart
Data Mart
Model Refinement
Utilities of Back-End Tools
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 the original format to the warehouse format
Loading
Sort, summarize, consolidate, compute views, check integrity, and
build indices and partitions
Refresh
Propagate the updates from data sources to the warehouse
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OLAP Server Architecture
Relational OLAP (ROLAP)
Uses relational or extended-relational DBMS to store and manage
warehouse data and OLAP middle ware
Includes optimization of DBMS back-end, implementation of aggregation
navigation logic, and additional tools and services
High scalability
Multidimensional OLAP (MOLAP)
Sparse array-based multidimensional storage engine
Fast indexing to pre-computed summarized data
Hybrid OLAP (HOLAP)
Low-level: relational / high-level: array
High flexibility
Metadata Repository
Definition of Metadata
The data defining data warehouse objects
Examples
Description of the structure of the data warehouse, e.g., schema, view,
dimensions, hierarchies, data definitions, data mart locations and contents
Operational meta-data, e.g., history of migrated data, currency of data,
warehouse usage statistics, error reports
Algorithms used for summarization
Mapping from operational environment to data warehouse
Data related to system performance
Business data, e.g., business terms and definitions, ownership of data,
charging policies
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Chapter 4, Data Warehouse & OLAP Operations
CSI 4352, Introduction to Data Mining
Basic Concept of Data Warehouse
Data Warehouse Modeling
Data Warehouse Architecture
Data Warehouse Implementation
From Data Warehousing to Data Mining
Data Cube Computation
View as a Lattice of Cuboids
How many cuboids in an n-dimensional cube?
How many cuboid cells in an n-dimensional cube
with Li levels?
Materialization of Data Cube
Full materialization (all cuboids), Partial materialization (some cuboids),
No materialization (only base cuboid)
Selection of cuboids to materialize
• Based on the size, sharing, access frequency, etc.
)11
(
n
i iL
n2
all
product time location
product, time
product, location
time, location
product, time, location
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Cube Operation
Cube Definition and Computation in DMQL
define cube sales [item, city, year]: sum (sales_in_dollars)
compute cube sales
Cube Definition and Computation in SQL
select item, city, year, SUM (amount)
from sales
cube by item, city, year
Internal Operations
group by (item, city, year)
group by (item, city), (item, year), (city, year)
group by (item), (city), (year)
group by ()
Iceberg Cube
Iceberg Cube Computation
Computing only the cuboid cells whose count or
other aggregates satisfying the condition like
HAVING COUNT(*) >= min_sup
Motivation
Only a small portion of cube cells may be “above the water’’
in a sparse cube
Only calculate “interesting” cells — data above certain threshold
Avoid explosive growth of the cube
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Chapter 4, Data Warehouse & OLAP Operations
CSI 4352, Introduction to Data Mining
Basic Concept of Data Warehouse
Data Warehouse Modeling
Data Warehouse Architecture
Data Warehouse Implementation
From Data Warehousing to Data Mining
Data Warehouse Usage
Information Processing
Supports querying, basic statistical analysis, and reporting using
crosstabs, tables, charts and graphs
Analytical Processing
Supports OLAP operations in multi-dimensional space
Data Mining
Supports pattern discovery from warehouse data, and presenting
the mining results using visualization tools
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From OLAP To OLAM
On-Line Analytical Mining (OLAM)
( OLAP + Data Mining ) in data warehouse
Why OLAM ?
High quality data
• Data warehouse contains integrated, consistent and cleaned data
Information processing infrastructure
• ODBC/OLE DB connections, web accessing, service facilities
OLAP-based exploratory data analysis
• Mining with drilling, dicing, pivoting, etc.
On-line selection of data mining functions
• Integration and swapping of multiple data mining functions
OLAM System Architecture
Data Warehouse
OLAMEngine
OLAPEngine
User GUI API
Data Cube API
Database API data cleaning data integration
Layer3
OLAP/OLAM
Layer2
MDDB
Layer1
Data Repository
Layer4
User Interfacemining query mining result
Meta Data
Databases
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Questions?
References
Gray, J., et al., “Data Cube: A Relational Aggregation Operator
Generalizing Group-By, Cross-Tab and Sub-Totals”, Data Mining and
Knowledge Discovery, Vol. 1 (1997)
Lecture Slides are found on the Course Website,
www.ecs.baylor.edu/faculty/cho/4352