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2017.IX.
Young members of the MY-X team co-operate successfully with PhD-Students in frame of the conference of Szarvas!

2017.VII.
The MY-X team (incl. joung members) participated on the conference 'In memoriam Enyedi György'

2017.VI.
Bárdy-Péter-Prize for a member of the MY-X team concerning his innovation potential!

2017.V.
Gábor-Dénes-Prize (special prize for mathematics) for a member of the MY-X team!

2017.IV.
Special Prize in Minsk in frame of an essay-competition for a member of the MY-X team concerning a study about reform approches in education!

2017.IV.
III. Prize in the Hlavay competition for a member of the MY-X team!

2017.III.
NTP-NFTÖ financial support for a member of the MY-X team!

2017.II.
Successful presentation in TUDOK-competition in Nyíregyháza and later in Székesfehérvár by a member of the MY-X team!



Last modified: 2015.VII.19.14:08 - MIAÚ-RSS
The Journal MIAU has been working for 20 years as a kind of public service!

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

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. More (DOC) *** More (PDF)


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