Data Warehousing: and OLAP MIS 542 Chapter 4

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Data Warehousing and OLAP MIS 542 Chapter
4

• Spring
• 2004

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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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What is Data Warehouse?

• Defined in many different ways, but not
  rigorously.
• A decision support database that is maintained
  separately from the organizations 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 managements
  decision-making process.W. H. Inmon
• Data warehousing
• The process of constructing and using data
  warehouses

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Data WarehouseSubject-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 WarehouseIntegrated

• 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 WarehouseTime 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 WarehouseNon-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
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Why Separate Data Warehouse?

• High performance for both systems
• DBMS tuned for OLTP access methods, indexing,
  concurrency control, recovery
• Warehousetuned 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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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 quarters item
dimension by types of items sold The fact or
measure is dollar_sold
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A three dimensional cube
Dimensions time, item, location for the cities
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A Data Cube Representation of the same data

• Sales volume as a function of product, month, and
  region

Dimensions Product, Location, Time Hierarchical
summarization paths
izmir
locations
ankara
istabbul
items
phon
comp
Quarters
Q1
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A 4-D Cube as a series of 3-D cubes
Dimensions item,time,location,supplier
From Supplier A
From Supplier B
From Supplier C
locations
items
Quarters
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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 cuboid
• 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
0-D(apex) cuboid
time
item
location
supplier
1-D cuboids
time,item
time,location
item,location
location,supplier
2-D cuboids
time,supplier
item,supplier
time,location,supplier
time,item,location
3-D cuboids
item,location,supplier
time,item,supplier
4-D(base) cuboid
time, item, location, supplier
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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

Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
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Example of Snowflake Schema
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_sales
Measures
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A unnormalized City Table
City province and Country is repeated For every
steet in Istanbul
A Normalized City Table
Unnecessary repitations of province end country
are eliminated Memory gain but complex queries
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Example of Fact Constellation
Shipping Fact Table
time_key
Sales Fact Table
item_key
time_key
shipper_key
item_key
from_location
branch_key
to_location
location_key
dollars_cost
units_sold
units_shipped
dollars_sold
avg_sales
Measures
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Measures Three Categories

• A multidimensional point in the data cube space
• dimension-value pairs
• (timeQ1,locationIstanbul,itemcomputer)
• 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 D1,3,6,8,9
• Sum(D) 27
• Partition the set into D1 end D2 as
• D11,3,6), D28.9
• 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_type)
• branch(branch_key,branch_name,branch_type)
• location(location_key,street,city,province_or_stat
  e,country)
• sales(time_key,item_key,branch_key,location_key,nu
  mber_of_units_sold,price)

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Example cont.

• Select s.time_key,s.item_key,s.branch_key,s.locati
  on_key,
• sum(s.number_of_units_solds.price),
• sum(s.number_of_units_sold)
• from time t, item i,branch b,location l,sales s,
• where s.time_keyt.time_key and
• s.item_keyi.item_key and
• s.branch_keyb.branch_key and
• s.location_keyl.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.lo
  cation_key,sum(s.number_of_units_solds.price),sum
  (s.number_of_units_sold)
• from time t, item i,branch b,location l,sales s,
• where s.time_keyt.time_key and
• s.item_keyi.item_key and
• s.branch_keyb.branch_key and
• s.location_keyl.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_solds.price),
• sum(s.number_of_units_sold)
• from time t, item i,branch b,location l,sales s,
• where s.time_keyt.time_key and
• s.item_keyi.item_key and
• s.branch_keyb.branch_key and
• s.location_keyl.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
• streetltcityltprovince_or_stateltcountry
• The attributes of a dimension may be organized in
  a partial order, forming a lattice
• day,week,month,quarter, year
• dayltmonthltquarter,weekltyear

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A Concept Hierarchy Dimension (location)
all
all
Europe
North_America
...
region
Mexico
Canada
Spain
Germany
...
...
country
Vancouver
...
...
Toronto
Frankfurt
city
M. Wind
L. Chan
...
office
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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
• dayltmonthltquarter,weekltyear
• 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

Dimensions Product, Location, Time Hierarchical
summarization paths
Region
Industry Region Year Category
Country Quarter Product City Month
Week Office Day
Product
A partially ordered hierarchy
Month
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Cuboids Corresponding to the Cube
all
0-D(apex) cuboid
location
item
time
1-D cuboids
item,time
item,location
time, location
2-D cuboids
3-D(base) cuboid
item, time, location
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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 Hans 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
0-D(apex) cuboid
location
item
time
1-D cuboids
item,time
item,location
time, location
2-D cuboids
3-D(base) cuboid
item, time, location
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Roll-up (drill-up)

• Climbing up a concept hierarchy for a dimension
  or
• by dimension reduction
• Exroll-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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By a drill up opperation examine sales By country
rather than city level
roll up
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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

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
• dayltmonthltquarterltyear
• form the level of quarter
• to the more detailed level of month
• Adding a new dimension to the data

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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
• locationtoronto or vencover and
• time Q1 or Q2 and
• item home entertainment or computer

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slice
dice
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Pivot (rotate)

• Visualization opp. Rotates the data axes in view
  to provide an alternative presentation of data
• Exitem 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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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
• Exfigure 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
Customer Orders
Shipping Method
Customer

CONTRACTS
AIR-EXPRESS
ORDER
TRUCK
PRODUCT LINE
Product
Time
DAILY
QTRLY
ANNUALY
PRODUCT ITEM
PRODUCT GROUP
CITY
SALES PERSON
COUNTRY
DISTRICT
REGION
DIVISION
Each circle is called a footprint
Location
Organization
Promotion
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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 Architecture
Monitor Integrator
OLAP Server
Metadata
Analysis Query Reports Data mining
Serve
Data Warehouse
Data Marts
Data Sources
OLAP Engine
Front-End Tools
Data Storage
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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
Multi-Tier Data Warehouse
Distributed Data Marts
Enterprise Data Warehouse
Data Mart
Data Mart
Model refinement
Model refinement
Define a high-level corporate data model
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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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Two dimensional sparse cuboid
One dimensional Densed 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
• 238

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

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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

73
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 processingno 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

76
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 dayltmonthltquarterltyear
• member properties for day
• weekend or business day0 or 1
• day of the week1,2,3,...,7
• a member property for year is
• whether it is leap year or not0 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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Parent Child Dimensions

• Based on two dimension table columns that
  together define the lieage relationships among
  the members of the dimension
• Member key columnidentifies each member
• Parent key column identifies the parent of each
  member

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Example A HR dimension
Poal West
James Smith
Jill Kelly
Amy Joens
John Grande
Jo Brown
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A Concept Hierarchy Dimension (location)
all
all
Europe
North_America
...
region
Mexico
Canada
Spain
Germany
...
...
country
Vancouver
...
...
Toronto
Frankfurt
city
M. Wind
L. Chan
...
office
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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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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

86
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
Layer4 User Interface
Mining query
Mining result
User GUI API
OLAM Engine
OLAP Engine
Layer3 OLAP/OLAM
Data Cube API
Layer2 MDDB
MDDB
Meta Data
Database API
FilteringIntegration
Filtering
Layer1 Data Repository
Data Warehouse
Data cleaning
Databases
Data integration
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Summary

• Data warehouse
• A subject-oriented, integrated, time-variant, and
  nonvolatile collection of data in support of
  managements 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)

89
References (I)

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