using the concept of info- cubes to facilitate data analysis in demographic surveillance systems...

Using the Concept of Info-Cubes to Facilitate Data

Analysis in Demographic Surveillance Systems

Yazoumé Yé, Uwe Wahser

Centre de Recherche en Santé de Nouna, Burkina Faso

INDEPTH General Meeting 2000

Yazoumé Yé, Uwe Wahser2/21

Content

• Introduction and Background (Slide 3 - 6)• The Relational Model in DSS (Slide 7 - 12)• Introducing Info Cubes (Slide 13 - 19)• Conclusion (Slide 20)



Introduction

• Background: Data Analysis in

Demographic Surveillance Systems (DSS)• Dilemma: Analysis of original data is

difficult, preprocessed output by technical staff is not flexible enough

• Proposition: provide Info-Cubes as easy-to-access data sources



Data Processing in DSS

Data Collection

D.Management

Data Entry

Analysis

Analysis of FixedFormat Output

Online AnalyticalProcessing (OLAP)

Online Transaction Processing (OLTP)



OLTP vs. OLAP

• provides big picture

• supports analysis

• needs aggregate data

• evaluate all datasets

quickly

• multidimensional model

Q: “HOW MANY live in

Atown?”

• provides detailed audit

• supports operations

• needs detailed data

• find one dataset quickly

• relational model

Q: “WHO lives in Atown?”



Peeping into DWH Architecture

OperationalDatabase(s)

Data Warehouse(DWH)

Data Marts

OLAP onMultidimensional DB

OLTP onRelational DB

Info Cubes



Relational Model in DSS

Location

Individual

Group

Residence

Relationship

MembershipOutmigration

Inmigration

Death

Birth

Observation

Status Observation

Preg. Outcome



Advantages of the RM

• Optimized for creation, reading, updating and deletion of data sets

• Ensures the retrieval of data• Eliminates redundant data to

– Ensure data consistency – Minimize data volume

• Insensitive to change

The RM supports OnLine Transaction Processing (OLTP)



Expected Output for Analysis

Muzungu Tababu Ferengi

M F M F M F

Atown 123 132 234 243 45 54

Betown 234 243 45 54 123 132

Cetown 45 54 123 132 234 243



Querying the Database

Location

Individual

Group

Resident

Relationship

MembershipOutmigration

Inmigration

Death

Birth

Observation

Status Observation

Preg. Outcome


M F M F M F

Atown 123 132 234 243 45 54

Betown 234 243 45 54 123 132

Cetown 45 54 123 132 234 243



Relational Data Storage

LocationLocID Village123 Atown234 Btown

ResidenceIndID LocID ResNoABC 123 1ABC 234 2BCD 234 1Individual

IndID Ethn.GroupABC MuzunguBCD Tababu



Problems of the RM

• Information has to be collected from several tables with complex queries– Design of queries is time consuming– Execution of queries is time consuming

• Complex model is difficult to understand– Design of queries needs skilled staff

The RM is not optimized for OnLine Analytical Processing (OLAP)



From Fixed Format Tables ...


M F M F M F

Atown 123 132 234 243 45 54

Betown 234 243 45 54 123 132

Cetown 45 54 123 132 234 243

Analysis Variable: Number of People

Dimension 1: Ethnic GroupDimension 2: Sex

Dimension 3: Town



... to Info-Cubes


M F M F M F

Atown 123 132 234 243 45 54

Betown 234 243 45 54 123 132

Cetown 45 54 123 132 234 243

Atown

Betown

Tababu

Ferengi

M

Cetown

Muzungu

45

F

54

234 243

123 132

Dimension 1: Ethnic Group

Dimension 2: Sex

Dimension 3: Town



... to MDD Tables


M F M F M F

Atown 123 132 234 243 45 54

Betown 234 243 45 54 123 132

Cetown 45 54 123 132 234 243


EthGrp

Dimension 2: Sex

Sex

Dimension 3: Town

TownMuzungu M Cetown 45Muzungu F Cetown 54Tababu M Cetown 123Tababu F Cetown 132Ferengi M Cetown 234Ferengi F Cetown 243

Muzungu M Betown 234Muzungu F Betown 243Tababu M Betown 45

... ... ... ...

V_Count

Analysis Variable



Some Terms

• Granularity: degree of detail of the aggregated data

• Drill Down: zoom into detail during OLAP

• Roll Up: zoom out

• Slicing: restricting analysis to one category

• Dicing: restricting analysis to a selection of categories



Example of Slicing

Atown

Betown

Tababu

Ferengi

M

Cetown

MuzunguF


Dimension 2: Sex

Dimension 3: Town

123 132

45 54

234 243Atown

Betown

only Tababu

M

Cetown

F

45 54

234 243

123 132



Some Remarks

• Adding dimensions increases granularity

• Adding categories increases granularity

• High granularity produces big cubes

• Number of data sets in a cube = number of existing combinations of categories across all dimensions

Challenge: define useful info-cubes which are not too big (performance) but contain sufficient dimensions (flexibility)



Advantages of Info-CubesQueries on Info-Cubes • are simple• are fast, when the granularity is not too high• are more flexible than fixed output tables

Info-Cubes• normally don’t contain confidential data• can be seized according to the needs of the

trageted researcher

Info-Cubes are suitable for OLAP and for dissemination of DSS Data



Conclusion: Possible Benefits

• Facilitate dissemination of DSS data

• Give more responsibilities to the researcher

• Reduce workload of technical staff

• Ensure consistent analysis results

• With suitable browser-tools: enable online cubes on the WWW



Links and LiteratureCommercial Demo Cubes on the WWW:• http://www.sas.com/rnd/web/demos/mddbapp/webeis.html• http://www5.ibi.com/ibi_html/insure2/

Good Overview on DWH:• Inmon WH, Welch JD, Glassey KL, Managing the Data

Warehouse. New York: John Wiley & Sons, 1997

Some Links on DWH, MDD:• www.informix.com/informix/solutions/dw/redbrick/wpapers/• www2.andrews.edu/~dheise/dw/Avondale/ACDWTOC.html• members.aol.com/fmcguff/dwmodel/index.htm• www.forwiss.tu-muenchen.de/~system42/public/Line42/

Literatur/OLAP-Modeling.html• www.archer-decision.com/star101.zip

using the concept of info- cubes to facilitate data analysis in demographic surveillance systems...

Documents

townyazoum y

uwe wahsercentre

uwe wahseroltp

uwe wahserpeeping

uwe wahseradvantages

uwe wahsercontentintroduction

databaseyazoum y

dssyazoum y