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Parallel Coordinates-based Visual Analytics for Materials Property

Topics: Databases and Visualization, Visual Data Mining; Information and Scientific Visualization; Interactive Visual Interfaces for Visualization; Interface and Interaction Techniques for Visualization; Scientific Visualization; Visual Data Analysis and Knowledge Discovery; Visual Representation and Interaction; Visualization Applications; Visualization Tools and Systems for Simulation and Modeling

Authors: Diwas Bhattarai ; Jian Zhang and Bijaya Karki

Affiliation: School of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge, LA 70803 and U.S.A.

ISBN: 978-989-758-354-4

Keyword(s): Parallel Coordinates, Multivariate Visual Analytics, Materials Property, Viscosity Data.

Related Ontology Subjects/Areas/Topics: Abstract Data Visualization ; Computer Vision, Visualization and Computer Graphics ; Databases and Visualization, Visual Data Mining ; General Data Visualization ; Information and Scientific Visualization ; Interactive Visual Interfaces for Visualization ; Interface and Interaction Techniques for Visualization ; Scientific Visualization ; Spatial Data Visualization ; Visual Data Analysis and Knowledge Discovery ; Visual Representation and Interaction ; Visualization Applications ; Visualization Tools and Systems for Simulation and Modeling

Abstract: Because of major advances in experimental and computational techniques, materials data are abundant even for specific classes of materials such as magma-forming silicate melts. A given material property M can be posed as a complex multivariate data problem. The relevant variables or dimensions are the values of the property itself, the factors which influence the property (pressure P, temperature T, multicomponent composition X), and meta data information I. Here we present an innovative visual analytics system for the melt viscosity (η), which can be represented by M (η, P, T, X1, X2, ..., I1, I2, ...). Our system consists of a viscosity data store along with a web-based visualization support. In particular, we enrich the parallel coordinates plot with non-standard features, such as derived axes/sub-axes, dimension merging, binary scaling, and nested plot. It offers many insights of relevance to underlying physics, data modeling, and guiding future experiments/computations. Other mat erial properties such as density can be incorporated as new attributes and corresponding new axes in the plot. Our aim is to collect all published data on various melt properties and develop a framework supporting database, visualization and modelling functions. (More)

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Paper citation in several formats:
Bhattarai, D.; Zhang, J. and Karki, B. (2019). Parallel Coordinates-based Visual Analytics for Materials Property.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2 HUCAPP: IVAPP, ISBN 978-989-758-354-4, pages 83-95. DOI: 10.5220/0007375400830095

@conference{ivapp19,
author={Diwas Bhattarai. and Jian Zhang. and Bijaya B. Karki.},
title={Parallel Coordinates-based Visual Analytics for Materials Property},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2 HUCAPP: IVAPP,},
year={2019},
pages={83-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007375400830095},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2 HUCAPP: IVAPP,
TI - Parallel Coordinates-based Visual Analytics for Materials Property
SN - 978-989-758-354-4
AU - Bhattarai, D.
AU - Zhang, J.
AU - Karki, B.
PY - 2019
SP - 83
EP - 95
DO - 10.5220/0007375400830095

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