For many decades fusion techniques have been experimented in many European countries. Fusion data is very efficient to recover information in many cases. It's a simple 3 step procedure:

• Extract a sub-sample from the large respondents data which is split in 2 halves
  
• Use these two sub-samples to produce a fusion of a third sample
  
• Run your calculations with his fusion data
  

Then we can compare the results issued from the fusion data set and the ones coming from the observed dataset.

The fusion algorithm involved is the latest available.

According to Procustrean Fusion Algorithm - Sample 1 is called the donor sample and sample 2 is called the recipient sample.

The Procustrean Fusion Algorithm (PFA) obeys five principles :

• Each recipient should receive data from a single donor.
  
• The data collected for a donor is transferred as a whole to the linked recipients.
  
• Any donor already linked should be highly discouraged to produce further links
  
• The cross-distributions between common and additional variables should be preserved unchanged by the ascription process.
  
• The similarity between two respondents should be evaluated globally.
  

The rational underlying the first two principles is to avoid breaks of the inter-correlations between the

additional variables during the ascription process.

The third principle protects against a decrease of the effective sample size.

The fourth principle refers to one of the basic requirements for a good fusion..

The last principle is somewhat more subtle. The idea behind is that if one considers that two donors are close (i.e. similar) they need to be so, not only on the basis of the common variables but also on the basis of the additional variables, otherwise the fusion process could distort the dependencies existing among these variables.

Altogether the five previous principles are useful to protect the fused database against distortion of the relationships existing between the variables.

Let me know if you have any questions.

Regards,

Tina

Hi,

I have seen some fields using indepth fusion data analysis...

• Geospatial domain - Location of planet wrt to individual references
  
• Pro-E & other 3D Cad Softwares
  
• MS SQL data link in databases.
  

Fused data is always better than data on sigle reference, coz,

• Easier & Faster Manipulation of data
  
• Data is accurate
  

Regards,

Ramabadran

Hi,

Sorry for the delayed response...

• Normally in the CAD Softwares, which uses higher memory for GUI, the regeneration of the 3D model is done as clusters. (ie) for every regeneration, the reference point varies. Hence the regeration is faster & accurate.
  

• In geospatial domain, the location of the galaxy,stars,planets are reffered by individual references. The distance & angular positions of these references are separate. A fused data of all these will give accurate relative positions & distances
  

Regards,

Ramabadran