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Analysis of homogeneity of real and simulated data assets
Leading article: 2018. November (MIAU No. 243.)
(Previous article: MIAU No. 242.)
Keywords: similarity analysis, homogeneity analysis, big-data, randomized data asset
Abstract: The access to real measurements in case of big-data projects can be disturbed through arbitrary impacts,
and this can lead to specific risks for the projects. Fortunately, in most of the challenges, analytical, hermeneutical,
and visualization modules can already be developed based on (quasi) random data assets.
This way makes possible not only the increasing of the project’s speed,
but the flexibility of the interpretation potential will also be extended.
It should mostly be so, because the covered part of the combinatorial space in case of randomized constellations is wider than in case of real measurements
and their combinations. Therefore, it is necessary to be capable to interpret them too.
If real data are available, then it is necessary to compare the to the randomized data assets.
One of the first steps is, to proof, whether the records of the two sources can be seen being elements in the same set,
where anti-discriminative similarity analyses are able to deliver index values.
In ideal case, the average similarity index values for the two groups should be identical.
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