Multidimensional Databases

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MultidimensionalDatabases

Prof. Navneet Goyal Computer Science Department

BITS, Pilani


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O ctober 13, 2010 Dr. Navneet Goyal, BITS, Pilani 2

Database Evolution

Flat filesHierarchical and NetworkRelationalDistributed RelationalMultidimensional


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Why Multi-Dimensional Databases?

No single "best" data structure for allapplications within an enterpriseOrganizations have abandoned the searchfor the HOLY GRAIL of globally accepteddatabaseSelect the most appropriate data structureon a case-by-case basis from a palette of

standard database structuresMultidimensional Databases for OLAP?


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Why Multi-Dimensional Databases?

From econometric research conducted at MIT inthe 1960s, the multidimensional database hasmatured into the database engine of choice for data analysis applicationsInherent ability to integrate and analyze largevolumes of enterprise dataOffers a good conceptual fit with the way end-users visualize business data Most business people already think about their

businesses in multidimensional terms Managers tend to ask questions about product sales in

different markets over specific time periods


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Multidimensional Database

Spreadsheets A 2D database?Functionalities

What about a stack of similar spreadsheets for different times?Limitations?

We can not relate data in differentsheets easily


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Multidimensional Database

An MDDB is a computer software systemdesigned to allow for the efficient andconvenient storage and retrieval system of large volumes of data that is

1. Intimately related &2. Stored, viewed and analyzed form different

perspectives

These perspectives are called Dimensions


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A Motivating Example

An automobile manufacturer wants toincrease sale volumes by examining salesdata collected throughout the organization.The evaluation would require viewing

historical sales volume figures from multipledimensions such asSales volume by modelSales volume by color Sales volume by dealer Sales volume over time


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R elational Structure


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COLOR

MODEL

Mini Van

Sedan

Coupe

Red WhiteBlue

6 5 4

3 5 5

4 3 2

Sales Volumes

Multidimensional A rra y Structure


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R DBMS vs. MDD

Multidimensional array structure represents ah ig h er level of organization than the relationaltablePerspectives are embedded directly into thestructure in the multidimensional model

All possible combinations of perspectives containing aspecific attribute (the color BLUE, for example) line up alongthe dimension position for that attribute.

Perspectives are placed in fields in therelational model - tells us nothing about fieldcontents .


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MDD makes data browsing and manipulationintuitive to the end-user Any data manipulation action possible with aMDD is also possible using relationaltechnologySubstantial cognitive advantages in queryformulationSubstantial computational performanceadvantages in query processing when usingMDD

R DBMS vs. MDD


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R DBMS vs. MDD


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Mutlidimensional R epresentation

Sales Volumes

DEALERSHIP

Mini Van

Coupe

Sedan

Blue Red White

MODEL

ClydeGleason

Carr

COLOR


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V iewing Data - A n Example

DEALERSHIP

Sales Volumes

MODEL

COLOR

Assume that each dimension has 10 positions, as shown inthe cube above


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V iewing Data - A n Example

H ow many records would be there in a relational table?Implications for viewing data from an end-user standpoint?

MODEL COLOR DEALERSHIP VOLUMEMINI VAN BLUE CLYDE 2MINI VAN BLUE GLEASON 2

MINI VAN BLUE CARR 2MINI VAN RED CLYDE 1MINI VAN WHITE GLEASON 3

RE CORD NUMBER.... 998RE CORD NUMBER.... 999RE CORD NUMBER.... 1000

SALES VOLUMES FOR ALL DEALERSHIPS


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Volume figure when car type = SEDAN,color=BLUE, & dealer=GLEASON?RDBMS all 1000 records might need to be

searched to find the right recordMDB has more knowledge about where thedata liesMax. of 30 position searches!!Average case15 vs. 500

P erformance A dvantages


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Total Sales across all colors and dealers whenmodel = SEDAN?RDBMS all 1000 records must be searched to

get the answer MDB Sum the contents of one 10x10 slice



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Data manipulation that requires a minute inRDBMS may require only a few seconds in MDBMDBs are an order of magnitude faster thanRDBMSs

Performance benefits are more for queries thatgenerate cross-tab views of dataThe performance advantages offered bymultidimensional technology facilitates thedevelopment of interactive decision supportapplications like OLAP that can be impracticalin a relational environment.



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Any data manipulation action possiblewith a multidimensional database is alsopossible using relational technology

MDBs however offer several advantageslike: Ease of data presentation and navigation

Ease of maintenance Performance

R DBMS vs. MDB


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Intuitive spreadsheet like data views arenatural output of MDBsObtaining the same views in a relational

environment, requires either a complexSQL or a SQL generator against a RDB toconvert the table outputs into a moreintuitive formatTop N queries are not possible with SQLat all

Ease of Data P resentation & Navigation


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Ease of maintenance because data isstored as it is viewedNo additional overhead is required to

translate user queries into requests for dataTo provide same intuitiveness, RDBs useindexes and sophisticated joins whichrequire significant maintenance andstorage

Ease of Maintenance


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Performance of MDBs can be matched byRDBs through database tuningNot possible to tune the database for all

possible adhoc queriesTuning requires resources of anexpensive DB specialistAggregate navigators are helping RDBsto catch up with MDBs as far asaggregation queries are concerned

P erformance


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A dding Dimension - A n Example

MODEL

Mini Van

Coupe

Sedan

Blue Red White

ClydeGleason

Carr

COLOR

Sales Volumes

Coupe

Sedan

Blue Red White

ClydeGleason

Carr

COLOR

DEALERSHIP

Mini Van

Coupe

Sedan

Blue Red White

ClydeGleason

Carr

COLOR

JANUARY FEBRUARY MAR CH

Mini Van


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Wh en is MDD (In)appropriate?

PERSONNEL

LAST NAME EMPLOYEE# EMPLOYEE AGESM I TH 01 21REGAN 12 19FOX 3 1 6 3WELD 14 3 1KELLY 5 4 27LI N K 0 3 5 6KRANZ 41 4 5LUCUS 33 41WEI SS 2 3 19

First, consider situation 1


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N ow consider situation 2SALES VOLUMES FOR GLEASON DEALERSHIP

MODEL COLOR VOLUMEMINI VAN BLUE 6MINI VAN RED 5MINI VAN WHITE 4SPORTS COUPE BLUE 3SPORTS COUPE RED 5SPORTS COUPE WHITE 5SEDAN BLUE 4SEDAN RED 3SEDAN WHITE 2

1. S et up a MDD structure for situation 1, with LAST NAMEand Employee # as dimensions, and AGE as the measurement.2. S et up a MDD structure for situation 2, with MODEL andCOLOR as dimensions, and SALES VOLUME as the measurement .

When is MDD (In)appropriate?


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COLOR

MODEL

Mini Van

Sedan

Coupe

Red WhiteBlue

6 5 4

3 5 5

4 3 2

Sales Volumes

EMPLOYEE #

LAST

NAME

Kranz

Weiss

Lucas

41 3331

45

19

Employee Age

41

31

56

63

21

19

Smith

Regan

Fox

Weld

Kelly

Link

01 14 54 03 1223

27

N ote the sparse between the two MDD representations

MDD S tructures for the S ituations


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O ur sales volume dataset has a great numberof meaningful interrelationshipsInterrelationships more meaningful thanindividual data elements themselves.

The greater the number of inherent interrelationshipsbetween the elements of a dataset, the more likely it isthat a study of those interrelationships will yield businessinformation of value to the company.H ighly interrelated dataset types be placed in amultidimensional data structure for greatestease of access and analysis


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N o last name is matching with morethan one emp # and no emp # ismatching with more than one last name

In contrast, there is a sales figureassociated with every combination ofmodel and color resulting in acompleted filled up 3x3 matri x

P erformance suffers ( RDB 9 vs. MDB 18)


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The relative performance advantages ofstoring multidimensional data in amultidimensional array increase as thesize of the dataset increases

The relative performance disadvantagesof storing non-multidimensional data ina multidimensional array increase asthe size of the dataset increases.NO inherent value of storing N on-multidimensional data (employee data)in multidimensional arrays


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When is MDD Appropriate?

The greater the number of inherent interrelationshipsbetween the elements of a dataset, the more likely it isthat a study of those interrelationships will yield businessinformation of value to the company.M ost companies have limited time andresources to devote to analyzing dataIt therefore becomes critical that these highlyinterrelated dataset types be placed in amultidimensional data structure for greatest

ease of access and analysis.


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When is MDD Appropriate?Ex

amples of applications that are suited formultidimensional technology:

1. Financial Analysis and Reporting2. Budgeting3. Promotion Tracking4. Quality Assurance and Quality Control5. Product Profitability


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MDD Features - Rotation

Sales Volumes

COLOR

MODEL

Mini Van

Sedan

Coupe

Red WhiteBlue

6 5 4

3 5 5

4 3 2

MODEL

COLOR

SedanCoupe

Red

White

Blue 6 3 4

5 5 3

4 5 2( ROTATE 90

o)

View #1: ModelxColorView #2: ColorxModel

Mini Van

Also referred to as data slicing.Each rotation yields a different slice or two dimensional table

of data.


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MDD Features - Rotation

All the si x views can be obtained by simplerotation

In MDB s rotations are simple as norearrangement of data is required

R otation is also referred to as data slicing

N o. of views

2D 23D 6

4D - ? 24


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MDD Features - Ranging

H ow sales volume of models paintedwith new metallic blue compared withthe sales of normal blue color models?The user knows that only S ports Coupeand M ini Van models have received thenew paint treatmentAlso the user knows that only 2 dealers

viz, Carr and Clyde have unconstrainedsupply of these models


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The end user selects the desired positions along each dimension.Also referred to as "data dicing."The data is scoped down to a subset grouping

Sales Volumes

DEALERSHIP

Mini Van

Coupe

MetalBlue

MODEL

ClydeCarr

COLOR

NormalBlue

Mini Van

Coupe

NormalBlue

MetalBlue

ClydeCarr


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The reduced array can now be rotatedand used in computations in the samewas as the parent arrayR eferred to as D ata D icing as data isscoped down to a subset groupingComple x SQL query is required in RDBP erformance is better in MDB as less

resource consuming searches arerequired


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MDD Features Roll-Up & Drill-Down

Users want different views of the same dataFor eg., S ales Volume by model vs, sales volume bydealershipMany times views are similar

S ales volume by dealership vs. volume by districtN atural relationship between S ales Volumes at theDEALERSH I P level and S ales Volumes at theD I STR I CT level

S ales Volumes for all the dealerships in a districtsum to the S ales Volumes for that district


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MDD Features - R oll-Ups & Drill Downs

Gary

Gleason arr Levi Lucas Bolton

Midwest

St. Louishicago

lyde

REGION

DISTRI T

DEALERSHIP

ORGANIZATION DIMENSION

The figure presents a definition of a hierarchy withinthe organization dimension.

Aggregations perceived as being part of the same dimension.Moving up and moving down levels in a hierarchy is referred

toas roll-up and drill-down.


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MDD Features - R oll-Ups & Drill Downs


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ea ures:Drill-Down T h roug h a Dimension

GaryGleason Carr Levi Lucas Bolton MidwestSt. Louis ChicagoClyde

REGION

DISTRICTDEALERSHIP

MO

DEL

COLOR

Sales Volumes


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Q ueries

High degree of structure inMDB makes the querylanguage very simple and

efficientQuery language is intuitiveOutput is immediately usefulto end user


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Q ueries: Example

Display sales volume by modelfor each dealershipPRINT TOTAL.(SALES_VOLUME KEEP MODEL DEALERSHIP)

Trends emerge and comparisons are easily made

DEALERSHIPMODEL CLYDE GLEASON CARRMINI VAN 7 5 6SPORTS COUPE 4 6 8

SEDAN 3 8 1 2


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Q ueries: ExampleUse report writer in addition to SQL andwe getMINI VAN

CLYDE 7GLEASON 5

CARR 6SPORTS COUPE

CLYDE 4GLEASON 5CARR 8

SEDANCLYDE 3GLEASON 8CARR 12

MDD F t


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MDD Features:Multidimensional Computations

Well equipped to handle demanding mathematicalfunctions.Can treat arrays like cells in spreadsheets. For example, in a budget analysis situation, one candivide the A CTUAL array by the BUDGET array to

compute the VARIAN CE array.Applications based on multidimensional databasetechnology typically have one dimension definedas a "business measurements" dimension.Integrates computational tools very tightly withthe database structure.


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T h e Time Dimension

TIME as a predefined hierarchy for rolling-upand drilling-down across days, weeks, months,years and special periods, such as fiscal years. Eliminates the effort required to build sophisticated

hierarchies every time a database is set up.

Extra performance advantages

on ras ng e a ona o e an


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on ras ng e a ona o e anMD Model

Criteria Relational Model Multidimensional

DatabasesFocus Data integrity of each piece

of dataFacilitate expl oration of interrelati onships betweendimen sions

Organizati on str ucture One-dimen sional array M ulti-dimen sional array sPer s pectives Embedded in f ields Embedded directly in

MDDB str uctureComputational p ower for Query pr ocessing

Joining table s of tenrequired; computationallyexpen sive

Str ucture de signed for OLAP; computationallycheap

Cognitive i ssues inquerying data

Cumber some Int uitive

Query Lang uages SQL or SQL f r ont-end s,such as QBE

Point-and- click emp hasis No standardized lang uage

Management of TimeDimen sion

Not well suited Well suited


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R DBMS vs. MDDB

DO I still use RDBMS for my DW?MDDBs store data in hypercube, i.e.,multidimensional array

RDBMS store data as tables with row andcolumns that do not map directly tomultidimensional view that user have of data

EDW RDBMSData Marts - MDDB


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R DBMS vs. MDDB: Trade-Offs

SIZE MDDBs limited by sizeMid 1990s 10GB caused problemsToday 100GB is OK

Large DWs are still better served by relationalfront-ends running against high performanceand scalable RDBMS

VOLATILITY Highly volatile data are better handled by

RDBMS MDDBs take long to load and update


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R DBMS vs. MDDB: Trade-Offs

AGGREGATE STRATEGY MDDBs support aggregates better RDBMSs are catching up with the help of

Aggregate NavigatorsINVESTMENT PROTE CTION Most organizations already have made

significant investments in relationaltechnology and skill sets

Continued use for another purpose (DW)provides additional ROI and lowers technicalrisk of failure

MDDBs need to acquire new software andtrain staff to use it


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INTEG RA TED AR CHITECTU R E

DB vendors have integrated their multidimensional and relational databaseproductsMultidimensional Front-end tools

If queries require data that are notavailable in MDDB, the tools retrieve thedata from the larger RDBKnown as DRILL-THROUGH


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Q & A


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Multidimensional Databases

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