data warehousing
DESCRIPTION
TRANSCRIPT
Data warehousing
Han, J. and M. Kamber. Data Mining: Concepts and Techniques. 2001. Morgan Kaufmann.
KDD process
Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Selection
Data Mining
Pattern Evaluation
Application
Data mining is the process of discovering interesting knowledge from large amounts of data stored in databases, data warehouses and/or other information repositories.
End User
BusinessAnalyst
DataAnalyst
DBA
MakingDecisions
Data PresentationVisualization Techniques
Data MiningInformation Discovery
OLAP, Multi-dimensional Analysis
Statistical Analysis, Querying and ReportingData Warehouses / Data Marts
Data SourcesPaper, Files, Information Providers, Database Systems, OLTP
Data mining andBusiness Intelligence
Data Exploration
Graphical user interface
Pattern evaluation
Data mining engine
Database or data warehouse server
DatabaseData
warehouse
Knowledge base
Data cleaningData integration
Filtering
Architecture of atypical data mining system
…Credit_infoIncomeAgeAddressNameCust_ID
…Supplier PriceTypeCategoryBrandNameitem_ID
…CommissionSalaryGroupDepartmentNameEmp_ID
amount …Pay_methodTimeDateEmp_idCust_idTrans_ID
Item_ID QtyTrans_ID
Customer
Item
Employee
Purchases
Items_sold
A relational database fragment
Queries
List of items sold in previous quarterTotal sales last month, grouped by salespersonNumber of sales transactions in DecemberSalesperson with highest amount of sales
Data warehouse
Integrates data from various sourcesData organized on a historical perspectivePresents different levels of summarized data
Multi-dimensional structuredimension: attributecell: aggregate measures
Data source in Chicago
Data source in New York
Data source in Toronto
Data source in Vancouver
CleanTransformIntegrateLoad
Datawarehouse
Query and analysis tools
Client
Client
Typical data warehouse architecture
Addr
ess
(city
)
Tim
e (q
tr.)
Q1
Q2
Q3
Q4
T1 T2 T3 T4 T5 T6
New YorkToronto
Vancouver
Item-types
Chicago
T1 T2 T3 T4 T5 T6
Q1
Q2
Q3
Q4Ad
dres
s (c
ount
ry)
USACanada
Tim
e (m
onth
) Jan
Feb
March
T1 T2 T3 T4 T5 T6
New YorkToronto
Vancouver
Item-types
ChicagoAddr
ess
(city
)
Item-types
Multi-dimensional data
Drill down on data for Q1
Roll-upon Address
Data WarehouseA decision support database that is maintained separately from the organization’s operational database
Collection of data this is– subject-oriented– integrated– time-variant– nonvolatile
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 – Ensure consistency in naming conventions, encoding
structures, attribute measures, etc. among different data sources
• e.g., Hotel price: currency, tax, breakfast covered, etc.
– Data is converted when moved to the warehouse.
Data Warehouse — Time Variant
• The time horizon for data warehouse is significantly longer than that of operational systems.– Operational database: current value data.
– Data warehouse: provides 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
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/integrators 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 the individual heterogeneous sites involved, and results are integrated into a global answer set
• Complex information filtering, compete for resources with local processing
• 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):– Users and system orientation: transaction vs. decision support– Data contents: current, detailed vs. historical, consolidated– Database design: ER + application vs. star schema + 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 on primary key
multiple large scans
unit of work short, simple transaction complex query
# records accessed tens millions
#users thousands hundreds
DB size 100MB-GB 100GB-TB
metric transaction throughput query throughput, response
Why Separate Data Warehouse?• Maintain 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 data and function– 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
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 literaturen-D base cube is called a base cuboid.The lattice of cuboids forms a data cube.
Time
Item
Location
Supplier
SALES
Multi-dimensional “cube”Sales by Item, Time, Location, Supplier
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 item,location,supplier
time, item, location, supplier
0-D(apex) cuboid
1-D cuboids
2-D cuboids
3-D cuboids
4-D(base) cuboid
time,location,supplier
current detailed data(up to 3 years)
monthly data (up to 15 years)
weekly data(up to 7 years)
retail monthly data(up to 15 years)
yearly data(keep all data)
special event effects (up to 30 years)
old detailed data (archived)
quarterly data(up to 20 years)
old weekly data(archived)
old monthly data(archived)
old quarterly data(archived)
old monthly data(archived)
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
Star Schema Example
time_keydayday_of_the_weekmonthquarteryear
time
location_keystreetcityprovince_or_statecountry
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_salesMeasures
item_keyitem_namebrandtypesupplier_type
item
branch_keybranch_namebranch_type
branch
Snowflake Schema example
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_type
item
branch_keybranch_namebranch_type
branch
city_keycityprovince_or_streetcountry
city
location_keystreetcityprovince_or_statecountry
location
Fact Constellation example
time_keydayday_of_the_weekmonthquarteryear
time
location_keystreetcityprovince_or_streetcountry
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
item_keyitem_namebrandtypesupplier_type
item
branch_keybranch_namebranch_type
branch
Shipping Fact Table
time_key
item_key
shipper_key
from_location
to_location
dollars_cost
units_shipped
shipper_keyshipper_namelocation_keyshipper_type
shipper
Multiple fact tables, sharing dimensionsCollection of stars – fact constellation or
galaxy schema
Data warehouse vs. data marts• Data warehouse
– Enterprise-wide scope– Subjects that span the organization– Fact constellation used to model multiple, interrelated
subjects
• Data mart– Department-wide scope– Departmental subset of data warehouse– Star, snowflake schema
Star schema is more efficient and thereby popular
Computing Measures• Measure: numerical value at each point in the data cube
e.g. <time=“Q1”, location=“Chicago”, item=“xyz”>: avg-amount
• Need to be able to efficiently compute measures
Measure types• Distributive: E.g., count(), sum(), min(), max().
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.
• Algebraic: E.g., avg(), min_N(), standard_deviation().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.
• Holistic: E.g., median(), mode(), rank().
There is no constant bound on the storage size needed to describe a sub-aggregate. No constant function with M arguments (constant M) that characterizes the computation.Can be difficult to compute efficiently – approximate computation
Concept Hierarchy
all
Europe North_America
MexicoCanadaSpainGermany
Vancouver
...
......
... ...
all
region
country
TorontoFrankfurtcity
Example: Location dimension
Concept hierarchiesFull or partial ordering Industry Region Year
Category Country Quarter
Product City Month Week
Office Day
Set-grouping hierarchye.g. price
($0..$1000]
($0..$1000] ($0..$1000] ($0..$1000]
($0..$1000] ($0..$1000]
($0..$1000]
($0..$1000]
($0..$1000]
($0..$1000]
Multiple hierarchies for an attribute price: {inexpensive, moderately_priced, expensive}
OLAP examplesSales volume as a function of product, month, and region
Prod
uct
Region
Month
Dimensions: Product, Location, TimeHierarchical summarization paths
Industry Region Year
Category Country Quarter
Product City Month Week
Office Day
A Sample Data CubeTotal annual salesof TV in U.S.A.Date
Produ
ct
Cou
ntrysum
sumTV
VCRPC
1Qtr 2Qtr 3Qtr 4QtrU.S.A
Canada
Mexico
sum
Addr
ess
(city
)
Tim
e (q
tr.)
Q1
Q2
Q3
Q4
T1 T2 T3 T4 T5 T6
New YorkToronto
Vancouver
Item-types
Chicago
T1 T2 T3 T4 T5 T6
Q1
Q2
Q3
Q4Ad
dres
s (c
ount
ry)
USACanada
Tim
e (m
onth
) Jan
Feb
March
T1 T2 T3 T4 T5 T6
New YorkToronto
Vancouver
Item-types
ChicagoAddr
ess
(city
)
Item-types
Drill down, Roll up
Drill down on data for Q1
Roll-upon Address
Tim
e (q
tr.)
Q1
Q2
Q3
Q4
T1 T2 T3 T4 T5 T6
New YorkToronto
Vancouver
Item-types
ChicagoChicago
Toronto
Q1
Q2
T3 T8
Dice for(location in {Chicago, Toronto}and time in {Q1}And Item in {T3, T8}
Chicago
New York
Toronto
Vancouver
T1 T2 T3 T4 T5 T6
SliceFor Time in {Q1}
Slicing and Dicing
Slice: Selection on one dimension
Dice; Selection on two or more dimensions
Browsing a Data Cube
VisualizationOLAPInteractive
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 ModelShipping Method
AIR-EXPRESS
TRUCKORDER
Customer Orders
CONTRACTS
Customer
Product
PRODUCT GROUP
PRODUCT LINE
PRODUCT ITEM
SALES PERSON
DISTRICT
DIVISION
OrganizationPromotion
CITY
COUNTRY
REGION
Location
DAILYQTRLYANNUALYTime
Data Warehouse Design: Four Views • Top-down view
• selection of the relevant information necessary for the data warehouse based on current and future needs
• Data source view• exposes the information being captured, stored, and
managed by operational systems (E/R models, CASE, etc)
• Data warehouse view• fact tables and dimension tables, pre-calculated totals,
counts, etc. Source information, date, time for historical context
• Business query view • perspectives of data in the warehouse from the view of end-
user
Data Warehouse Design Process• Top-down, bottom-up approaches or a combination
– 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 – 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
MultiMulti--Tiered DW ArchitectureTiered DW Architecture
DataWarehouse
ExtractTransformLoadRefresh
OLAP Engine
AnalysisQueryReportsData mining
Monitor&
IntegratorMetadata
Data Sources Front-End Tools
Serve
Data Marts
OperationalDBs
othersources
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 Marts• Data warehouse designed to meet the needs
of a specific group of users• Should (but may not) be designed with
corporate standards and accessibility in mind– incorporate standards for hardware,
software, networking, DBMS, naming conventions, etc.
– vendor’s attempt to bypass IT and sell directly to end-users?
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 refinementModel refinement
OLAP Server Types• Relational OLAP (ROLAP)
– Use relational or extended-relational DBMS to store and manage warehouse data and OLAP middleware 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)– User flexibility, e.g., low level: relational, high-level: array
• Specialized SQL servers– specialized support for SQL queries over star/snowflake schemas
Metadata RepositoryMeta 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, 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 indices and partitions• Refresh
– propagate the updates from the data sources to the warehouse
Advanced examples
Exploration of Data CubesHypothesis-driven: exploration by user, huge search space
Discovery-driven – pre-computed measures indicate 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
– Computation of exception indicator can be included in cube constructionSelfExp: degree of surprise in cell, relative to values at same levels of aggregationInExp: degree of surprise somewhere beneath the cell, if we drill downPathExp: degree of surprise for each drill down path from cell
Advanced examplesExample: Discovery-driven exploration
Advanced examples
Complex Aggregation at Multiple Granularities
• Ex. Total sales in 2000 by Item, Region, Month, with subtotals
• Ex. Grouping by all subsets of {item, region, month}, find the maximum price in 2000 for each group, and the total sales generated by all maximum-price-sales
• Ex. Among the max-price-sales, find the min and max shelf life. Find the fraction of the total sales due to cases that have min shelf life.
Advances examples
Sales#units,$value
Supplier
Product
Sales#units,$value Product
Sales%sales
Supplier
Product
Sales volume as a % of total units sold of Product
Ordering
Sales#units,$value
Supplier
Product
Group sales by contiguous 10-day intervals.
10 day Moving-avg of Sales, by Product
Order Products by Sales-$ and group into deciles of decreasing performance.