1 Data Warehousing: and OLAP —MIS 542— — Chapter 4 — 2014/2015 Fall.

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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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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

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

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

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

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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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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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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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)



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)



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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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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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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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

80

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

84

Exercise

How do you represent the same organizational chart by treditional concept hyerarchies?

85

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.

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