1 data warehousing: and olap —mis 542— — chapter 4 — 2014/2015 fall
TRANSCRIPT
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Data Warehousing: and OLAP —MIS 542—
— Chapter 4 —
2014/2015 Fall
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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
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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
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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.
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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.
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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”.
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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.
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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
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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
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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 100MB-GB 100GB-TB
metric transaction throughput query throughput, response
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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
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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
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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
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time ent comp phoneQ1 605 825 14Q2 680 952 31Q3 812 1023 30Q4 927 1038 38
location = Istanbulitem (type)
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 quartersitem dimension by types of items soldThe fact or measure is dollar_sold
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A three dimensional cube
Dimensions: time, item, location for the cities
time ent comp phone time ent comp phone time ent comp phoneQ1 605 825 14 Q1 1087 968 38 Q1 854 882 89Q2 680 952 31 Q2 1130 1024 41 Q2 943 890 64Q3 812 1023 30 Q3 1034 1048 45 Q3 1032 924 59Q4 927 1038 38 Q4 1142 1091 54 Q4 1129 992 63
location = Istanbulitem (type)
location = Ankaraitem (type)
location = Izmiritem (type)
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A Data Cube Representation of the same data
Sales volume as a function of product, month, and region
item
slo
catio
ns
Quarters
Dimensions: Product, Location, TimeHierarchical summarization paths
ankara
izmir
istabbul
Q1
comp
phon
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A 4-D Cube as a series of 3-D cubes
Dimensions: item,time,location,supplier
Quarters
item
slo
catio
ns
From Supplier A From
Supplier B From Supplier C
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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 ccuboid 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
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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
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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
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Example of Star Schema
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
Measures
item_keyitem_namebrandtypesupplier_type
item
branch_keybranch_namebranch_type
branch
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Sales Fact TableTime_keu Prod_key Loca_key branch_keyunit_sales $L_sales $_cost
1 2 2 1 20 100000 50004 6 17 8 30 600 206 12 4 77 25 5000 200
Time TableTime_ke day day_week month season year
6 Cumartesi 6 Ocak Q1 20034 Persembe 4 Ocak Q1 2003
15 Pazartesi 1 Ocak Q1 2003
Product TableProd_key Prod namebrand type supplier
12 Hpyazici HP HP3225 Hp baiyi6 IBM Bilgis IBM PC IBM PC 2GIBM temsil
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Example of Snowflake Schema
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
supplier_keysupplier_type
supplier
city_keycityprovince_or_streetcountry
city
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A unnormalized City Table
laca_key steet city province country1 HalGazi Istanbul Marmara Turkiye2 Nisbetiye Istanbul Marmara Turkiye3 Buyukdere Istanbul Marmara Turkiye4 AtaturkBul Ankara IcAnadolu Turkiye
City province and Country is repeatedFor every steet in İstanbul
loca_key steet city_key1 HalGazi 12 Nisbetiye 13 Buyukdere 14 AtaturkBul 2
A Normalized City Table
city_key province country1 Marmara Turkiye2 icanadolu Turkiye
Unnecessary repitations of province end country are eliminatedMemory gain but complex queries
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Example of Fact Constellation
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
Measures
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
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Measures: Three Categories
A multidimensional point in the data cube space dimension-value pairs: (time=“Q1”,location=“Istanbul”,item=“comput
er”) A data cube measure is a numerical function that
can be evaluated at each point in the data cube space
computed for a given point by aggregating the data corresponding to the respective dimension-value pairs defining the given point
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Measures: Three Categories
distributive: Suppose the data D is partitioned into n sets Di i
=1,..n the computation of the function f on each partition
derives one aggregate value Ai =f(Di) i = 1,..,n, f(D)=f(A1,A2,..,An)
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.
The function can be computed in a distributed manner E.g., count(), sum(), min(), max().
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Example: Sum()
Data set: D:{1,3,6,8,9} Sum(D) = 27 Partition the set into D1 end D2 as D1:{1,3,6), D2:{8.9} Sum(D1) = 10, Sum(D2) = 17 Sum(sum(D1),sum(D2)) = sum(10,17)
=27 =sum(D)
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Measures: Three Categories
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().
E.g.,avg() = sum()/count() both sum() and count() are distributive agg.
Functions Show that
min_N(), standard_deviation().
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Measures: Three Categories
holistic: if there is no constant bound on the storage size needed to describe a subaggregate.
There is no an algebric function with M arguments(M being bounded) that characterizes the computation
E.g., median(), mode(), rank().
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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_t
ype) branch(branch_key,branch_name,branch_type) location(location_key,street,city,province_or_sta
te,country) sales(time_key,item_key,branch_key,location_ke
y,number_of_units_sold,price)
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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
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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
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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
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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
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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
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A Concept Hierarchy: Dimension (location)
all
Europe North_America
MexicoCanadaSpainGermany
Vancouver
M. WindL. Chan
...
......
... ...
...
all
region
office
country
TorontoFrankfurtcity
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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
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Multidimensional Data
Sales volume as a function of product, month, and region
Pro
duct
Regio
n
Month
Dimensions: Product, Location, TimeHierarchical summarization paths
Industry Region Year
Category Country Quarter
Product City Month Week
Office Day
A partially ordered hierarchy
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Cuboids Corresponding to the Cube
all
item time location
item,time item,location time, location
item, time, location
0-D(apex) cuboid
1-D cuboids
2-D cuboids
3-D(base) cuboid
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Set grouping hierarchy
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
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How defined?
provided by manually by system users domain experts knowledge engineers or
automatically generated based on statistical analysis of the data distribution
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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
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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
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Cuboids Corresponding to the Cube
all
item time location
item,time item,location time, location
item, time, location
0-D(apex) cuboid
1-D cuboids
2-D cuboids
3-D(base) cuboid
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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
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location by cityIstanbul Ankara Berlin Münih
PC 20 30 50 40Printer 15 5 10 20
location y countryTürkiyy Almanya
PC 50 90Printer 20 30
roll up
By a drill up opperation examine salesBy country rather than city level
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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
locat AllPC 140Printer 50
location by countryTürkiye Almanya
PC 50 90Printer 20 30
Two dimensional cuboid One dim. cuboid
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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
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2002Q1 Q2 Q3 Q4
PC 10 15 20 5Printer 5 10 5 3
measure is salesTime 2002
PC 50Printer 23
Drill down
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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”
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Time 2002Q1 Q2 Q3 Q4
PC 10 15 20 5printer 5 10 5 3
measure salesTime 2002Q1
PC 10Printer 5
time2002Q1
PC 10
slice
dice
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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
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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
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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
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Poal West
James Smith Amy Joens Jill Kelly
John Grande Jo Brown
Example: A HR dimension
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A Concept Hierarchy: Dimension (location)
all
Europe North_America
MexicoCanadaSpainGermany
Vancouver
M. WindL. Chan
...
......
... ...
...
all
region
office
country
TorontoFrankfurtcity
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Example: Parent Child Dimension
Emp._Name Emp_Number Man_Emp_Num James Smith 1 3 Amy Jones 2 3 Paul West 3 3 Jill Kelly 4 3 John Grande 5 1 Jo Brown 6 1 Emp_Num identifies each member Man_Emp_Nun identifies the parent of each
member
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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
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A Star-Net Query Model
Shipping 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
Each circle is called a footprint
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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
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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
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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
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Multi-Tiered ArchitectureMulti-Tiered Architecture
DataWarehouse
ExtractTransformLoadRefresh
OLAP Engine
AnalysisQueryReportsData mining
Monitor&
IntegratorMetadata
Data Sources Front-End Tools
Serve
Data Marts
Operational DBs
other
sources
Data Storage
OLAP Server
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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
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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
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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
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PC Prt CD DV
01.10.2003
10
02.10.2003
1
03.10.2003
4 2
04.10.2003
2
Two dimensional sparse cuboid
items
01.10.2003 10
02.10.2003 1
03.10.2003 6
04.10.2003 2
One dimensionalDensed cuboid
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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
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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
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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
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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
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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
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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
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Number of cuboids
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
)11(
n
i iLT
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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
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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
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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
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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
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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
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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
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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
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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
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Exercise
How do you represent the same organizational chart by treditional concept hyerarchies?
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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
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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
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An OLAM Architecture
Data Warehouse
Meta Data
MDDB
OLAMEngine
OLAPEngine
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
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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)
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References (I) S. Agarwal, R. Agrawal, P. M. Deshpande, A. Gupta, J. F. Naughton, R. Ramakrishnan, and S.
Sarawagi. On the computation of multidimensional aggregates. In Proc. 1996 Int. Conf. Very Large Data Bases, 506-521, Bombay, India, Sept. 1996.
D. Agrawal, A. E. Abbadi, A. Singh, and T. Yurek. Efficient view maintenance in data warehouses. In Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data, 417-427, Tucson, Arizona, May 1997.
R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data, 94-105, Seattle, Washington, June 1998.
R. Agrawal, A. Gupta, and S. Sarawagi. Modeling multidimensional databases. In Proc. 1997 Int. Conf. Data Engineering, 232-243, Birmingham, England, April 1997.
K. Beyer and R. Ramakrishnan. Bottom-Up Computation of Sparse and Iceberg CUBEs. In Proc. 1999 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'99), 359-370, Philadelphia, PA, June 1999.
S. Chaudhuri and U. Dayal. An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26:65-74, 1997.
OLAP council. MDAPI specification version 2.0. In http://www.olapcouncil.org/research/apily.htm, 1998.
J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, and H. Pirahesh. Data cube: A relational aggregation operator generalizing group-by, cross-tab and sub-totals. Data Mining and Knowledge Discovery, 1:29-54, 1997.
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References (II)
V. Harinarayan, A. Rajaraman, and J. D. Ullman. Implementing data cubes efficiently. In Proc. 1996 ACM-SIGMOD Int. Conf. Management of Data, pages 205-216, Montreal, Canada, June 1996.
Microsoft. OLEDB for OLAP programmer's reference version 1.0. In http://www.microsoft.com/data/oledb/olap, 1998.
K. Ross and D. Srivastava. Fast computation of sparse datacubes. In Proc. 1997 Int. Conf. Very Large Data Bases, 116-125, Athens, Greece, Aug. 1997.
K. A. Ross, D. Srivastava, and D. Chatziantoniou. Complex aggregation at multiple granularities. In Proc. Int. Conf. of Extending Database Technology (EDBT'98), 263-277, Valencia, Spain, March 1998.
S. Sarawagi, R. Agrawal, and N. Megiddo. Discovery-driven exploration of OLAP data cubes. In Proc. Int. Conf. of Extending Database Technology (EDBT'98), pages 168-182, Valencia, Spain, March 1998.
E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. John Wiley & Sons, 1997.
Y. Zhao, P. M. Deshpande, and J. F. Naughton. An array-based algorithm for simultaneous multidimensional aggregates. In Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data, 159-170, Tucson, Arizona, May 1997.