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16/06/2009
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16/06/2009
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<= img border=3D0 v:shapes=3D"_x0000_s10242" src=3D"slide0001_image005.gif" style=3D'position:absolute;top:94.75%;left:17.04%;width:4.49%;height:3.75%= '>
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SPATIAL OLAP: TOWARDS MORE EFFICIENT SURVEILLANCE OF CLIMATE-RELATED HEALTH VULNERABILITIES OF <= span style=3D'position:absolute;top:58.75%;left:17.41%;width:78.46%;height:9.0%= '>POPULATIONS
Y. Bédard PhD, E. Bernier MSc,
Dr P. Gosselin, T. Badard  PhD
3D"D:\02_CHAIRE\Documents 3D"Logo

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text/html; charset="us-ascii" Slide 1
=
Outline
¨Transactional Systems vs. Analytical Systems =
¨Spatial OLAP (SOLAP) concepts <= /div>
¨Using a SOLAP application to help better <= /span>understand the interactions between public health and climate change

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=
Tran= sactional Systems
¨Transactional systems refer to datab= ases developed to manage day to day transactions
¨The transactional systems usually su= pport a specific application within an organisation (e.g. inventory, booking, HR = management)
¨
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=
Tran= sactional Systems
¨The content of transactional systems= is typically mad= e of current data, no archives
¨The data are very detailed since they contain the details of every transaction = ;
¨The data updates are done with new transactions (e.g. replacing an out-of-date data by a new data)
¨Very often, many of these systems we= re built as stand-alone applicatio= ns independent from each others
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Obstacles to Conducting Analyses in Transactional Systems
¨The transactional databases are usually = normalized in order to reduce data <= span style=3D'position:absolute;top:34.75%;left:11.23%;width:83.89%;height:6.75= %'>redundancy:
&c= urren;To ensure data integrity
&c= urren;To simplify the updates
¤
¨However, a rigid normalization leads to = more complex data analys= is processes:
&c= urren;Higher number of tables and joins, complex querie= s
&c= urren;Longer processing times  &#= 13;
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Obstacles to Conducting Analyses in Transactional Systems
¨The types of analyses used for decision-making involve:
¤Summarized data (aggregated)
¤Historical data (to analyze trends a= nd for predictions)
¤Fast responses for aggregative queries
¤Easier user interface
&curr= en; =
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=
Mai 2007
7
1. Built SQL Queries&= #13;
To filter the data according to the
<= span style=3D'mso-tab-count:1;width:12.63%'> search criteria <= /div>
C= oncatenate the criteria
C= reate the aggregated values (Summarize)
2. Summarize the data
3. Associate the cartographic layer
U= sing a join
4. Apply the data classification&= #13;
5. Apply the graphi= cal symbology
Number of students registered in = &laqu= o; = ;Spring 2009 » by &laqu= o; = ; regio= ns».
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