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Projects / Programmes source: ARIS

Kvalitativno modeliranje na osnovi podatkov (Slovene)

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Research activity

Code Science Field Subfield
2.07.07  Engineering sciences and technologies  Computer science and informatics  Intelligent systems - software 

Code Science Field
1.02  Natural Sciences  Computer and information sciences 
Evaluation (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Researchers (8)
no. Code Name and surname Research area Role Period No. of publications
1.  16324  PhD Janez Demšar  Computer science and informatics  Head  2009 - 2012  340 
2.  32930  Aleš Erjavec    Technical associate  2010 - 2012  12 
3.  34401  Mitar Milutinović  Computer science and informatics  Researcher  2012  16 
4.  31175  Gregor Rot  Computer science and informatics  Researcher  2009 - 2012  45 
5.  20389  PhD Aleksander Sadikov  Computer science and informatics  Researcher  2009  191 
6.  29630  PhD Miha Štajdohar  Computer science and informatics  Junior researcher  2009 - 2012  21 
7.  30142  PhD Marko Toplak  Computer science and informatics  Researcher  2010 - 2012  27 
8.  12536  PhD Blaž Zupan  Computer science and informatics  Researcher  2009 - 2012  531 
Organisations (1)
no. Code Research organisation City Registration number No. of publications
1.  1539  University of Ljubljana, Faculty of Computer and Information Science  Ljubljana  1627023  16,243 
Significance for science
Developed methods represent a pioneering work in the field of qualitative modeling. The work is especially interesting because of innovative connecting of techniques from different fields - topology, symbolic computation, machine learning, numerical analysis, probability and statistics. The core of the project, however, belongs to the field of artificial intelligence. Due to the limited time and resources available we did not expect the project to develop qualitative modeling to the same level of maturity as that of classification and regression learning which have been developing for half a century by a large community. We however believe that we provided a good basis for its future development. To project's results are useful in many other areas of science that rely on machine learning and data mining. These include all sciences that derive hypotheses from empirical data, most notably modern genetics and medicine. Developed algorithms will be, for instance, useful in analysis of dependencies between genes in genetic network, which can be used in modeling and curing diseases on genetic level. Other examples of scientific fields that rely heavily on drawing conclusions from experimental data are social sciences, psychology and economy and also most other areas of modern science.
Significance for the country
The work of Slovenian researchers in AI has always represented the state­of­the­art in the field and was also very successful with regard to obtaining EU­-funded research grants. Our work in the unexplored area of qualitative modeling will help it to maintain this position.
Most important scientific results Annual report 2009, 2010, 2011, final report, complete report on dLib.si
Most important socioeconomically and culturally relevant results Annual report 2010, 2011, final report, complete report on dLib.si
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