NON-ADDICTIVE SAMPLE BASED FACTS - Deetc

code to implement one of the report. In the particular
case, the query execution display the rating, for the
main targets, by time periods, for one specific channel.
WITH MEMBER Measures . TargetTotal as
’ LookupCube (" ContactTarget ",
"( Measures . weight ,"+ membertostr (
Target . currentmember )+")") ’
MEMBER Measures . rat as
’ Measures . weight / Measures . TargetTotal
/ Measures . num_min ’
, FORMAT_STRING =’ Percent ’
SELECT { Target . currentmember } ON COLUMNS ,
time . Period . Members ON ROWS
FROM AudienceTarget
WHERE ( Measures .rat , Program . Canal .[2])
Listing 1: The rating for the main targets by time periods
for channel 2 using the target aggregate
The code is rather simple because each target
weight is pre-calculated in the ContactTarget aggregate.
The LookupCube function lookup the value for
each target querying it. With the absence of this cube,
each target weight is calculate in runtime, looking up
each socio-demographic variable that made up the target.
Not only the execution times rise up, but also the
query’ code.
4 Conclusion
This work presents a dimensional model capable
to address the specificities of quota sample based data.
The goal was twofold; first, to demonstrate that is
possible to address audience analysis requirements,
using non-proprietary repositories and technologies;
second, to present a possible solution to other domains
where data is also quota sample based. In the
audience domain, the data tendencies are corrected by
a daily individual weight, that must be taken into account
if the indicators are meant to be representative
to the entire population, not just the panel’s individuals.
To deal with the audience performance indicators,
mostly non-addictive and quota depended, is necessary
to normalise each one with a reference value, calculated
from the corrective daily weights. The present
solution lay on the creation of an auxiliary contact table
to store daily weights for each possible combination
of socio-demographis values. The authors transform
this way the non-addictive facts into addictive
ones, sacrificing the capability of pre-calculated their
values and store them into a fact table directly. All of
the calculus must be done in runtime. To ensure an
efficient solution, is necessary to create a series of domain
dependant aggregates, with specific dimension
models. The performance indicators test results, using
both proprietary program and generic OLAP tools
with the discussed model, have matched.
Authors think the same methodology is appropriate
to other domains if the data is quota sample based
and the performance indicators values are always relative
to the subset of the sample used in their calculus.
REFERENCES
[1] Yvonne M. M. Bishop, E. F. Fienberg, and P. W. Holland.
Discrete multivariate analysis : theory and practice.
The MIT Press, 1975.
[2] EF Codd, SB Codd, and CT Sally. Providing
OLAP to user-analysis. Technical report,
http://www.arborsoft.com/essbase/wht ppr/coddps.
zip, 1993.
[3] Nuno Datia. Aplicação de técnicas de apoio à decisão
a dados de audimetria. Master’s thesis, Faculdade de
Ciências e Tecnologia - Universidade Nova de Lisboa,
2006.
[4] W. Edwards Deming and Frederick F. Stephan. On a
least squares adjustment of a sampled frequency table
when the expected marginal totals are known. Annals
of Mathematical Statistics, 11(4):427–444, 1940.
[5] C. Imhoff, N. Galemmo, and J.G. Geiger. Mastering
Data Warehouse Design: Relational and Dimensional
Techniques. Wiley, 2003.
[6] WH Inmon. Building the data warehouse. John Wiley
& Sons, Inc. New York, NY, USA, 2005.
[7] N. Jukic, B. Jukic, and M. Malliaris. Online Analytical
Processing (OLAP) for Decision Support. In
Handbook on Decision Support Systems. Springer,
2008.
[8] R. Kimball, L. Reeves, M. Ross, and W. Thornthwaite.
The Data Warehouse Lifecycle Toolkit. Wiley, 1998.
[9] R. Kimball and M. Ross. The Data Warehouse Toolkit:
The Complete Guide to Dimensional Modeling. Wiley,
2002.
[10] MediaSoft Kimono. http:// www. kubik. it/
kimono_ en. html , last acessed on May 2008.
[11] Sql Server Analysis Services. http://
technet. microsoft. com/ pt-br/ sqlserver/
bb671220(en-us). aspx , last acessed on May 2008.
[12] Sql Server Data TRansformations Services. http://
www. microsoft. com/ technet/ prodtechnol/ sql/
2000/ deploy/ dtssql2k. mspx , last acessed on May
2008.
[13] Markdata Telereport. http:// www. markdata. net/
v2/ , last acessed on May 2008.
[14] Rene Weber. Methods to Forecast Television Viewing
Patterns for Target Audiences. In Communication Research
in Europe and Abroad –Challenges of the First
Decade, 2003.