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WAVE: A 3D Online Previewing Framework for Big Data Archives

Jerome, Nicholas Tan; Chilingaryan, Suren; Shkarin, Andrei; Kopmann, Andreas; Zapf, Michael; Lizin, Alexander; Bergmann, Till

Abstract (englisch):
With data sets growing beyond terabytes or even petabytes in scientific experiments, there is a trend of keeping data at storage facilities and providing remote cloud-based services for analysis. However, accessing these data sets remotely is cumbersome due to additional network latency and incomplete metadata description. To ease data browsing on remote data archives, our WAVE framework applies an intelligent cache management to provide scientists with a visual feedback on the large data set interactively. In this paper, we present methods to reduce the data set size while preserving visual quality. Our framework supports volume rendering and surface rendering for data inspection and analysis. Furthermore, we enable a zoom-on-demand approach, where a selected volumetric region is reloaded with higher details. Finally, we evaluated the WAVE framework using a data set from the entomology science research.

Zugehörige Institution(en) am KIT Institut für Prozessdatenverarbeitung und Elektronik (IPE)
Publikationstyp Proceedingsbeitrag
Jahr 2017
Sprache Englisch
Identifikator KITopen ID: 1000078132
HGF-Programm 54.02.02; LK 01
Erschienen in IVAPP 2017 : 8th International Conference on Information Visualization Theory and Applications, Porto, Portugal, 27. Februar - 1. März.2017. Vol.: 3. Ed.: L. Linsen (IVAPP is part of VISIGRAPP, the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications)
Verlag SCITEPRESS, Setúbal
Seiten 152–163
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