Pregled bibliografske jedinice broj: 965204
Interactive visual analysis of families of curves using data aggregation and derivation
Interactive visual analysis of families of curves using data aggregation and derivation // Proceeding i-KNOW '12 Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies
Graz, Austrija: The Association for Computing Machinery (ACM), 2012. 24, 8 doi:10.1145/2362456.2362487 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 965204 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Interactive visual analysis of families of curves using data aggregation and derivation
Autori
Konyha, Zoltán ; Lež, Alan ; Matković, Krešimir ; Jelović, Mario ; Hauser, Helwig
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceeding i-KNOW '12 Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies
/ - : The Association for Computing Machinery (ACM), 2012
ISBN
978-1-4503-1242-4
Skup
I-KNOW '12, 12th International Conference on Knowledge Management and Knowledge Technologies
Mjesto i datum
Graz, Austrija, 05.09.2012. - 07.09.2012
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
attribute derivation, families of curves, interactive visual analysis, knowledge generations
Sažetak
Time-series data are regularly collected and analyzed in a wide range of domains. Multiple simulation runs or multiple measurements of the same physical quantity result in ensembles of curves which we call families of curves. The analysis of time-series data is extensively studied in mathematics, statistics, and visualization ; but less research is focused on the analysis of families of curves. Interactive visual analysis in combination with a complex data model, which supports families of curves in addition to scalar parameters, represents a premium methodology for such an analysis. In this paper we describe the three levels of complexity of interactive visual analysis we identified during several case studies. The first two levels represent the current state of the art. The newly introduced third level makes extracting deeply hidden implicit information from complex data sets possible by adding data derivation and advanced interaction. We seamlessly integrate data derivation and advanced interaction into the visual exploration to facilitate an in-depth interactive visual analysis of families of curves. We illustrate the proposed approach with typical analysis patterns identified in two case studies from automotive industry.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti
