Helwig Hauser graduated from Vienna University of Technology, Austria, in 1995 with the degree of a ''Dipl.-Ing.'' (~MSc) in computer science. In 1998, after research on flow visualization, he received the degree of a ''Dr.techn.'' (~PhD) from the same university (see below for a link to the thesis).
He worked as a teaching assistant, then as an assistant professor at the Institute of Computer Graphics [1] at Vienna University of Technology from 1994 until 2000. Afterwards, Helwig Hauser joined the newly founded VRVis Research Center [2], also in Vienna, Austria, as a key researcher in visualization. In 2003, he became the scientific director of VRVis [2].
In 2004, Helwig Hauser was entitled a ''Privatdozent'' at the Vienna University of Technology after his successful Habilitation (see below for a link to the thesis). Also in 2004, he won the IEEE Visualization 2004 Contest (together with H. Doleisch and Ph. Muigg), based on an interactive visual analysis of hurricane Isabel with SimVis [3]. In 2006, he was awarded with the prestigous Heinz Zemanek Award [4] for this work.
Since 2007, Helwig Hauser is professor at the University of Bergen, Norway, where he is leading the research group on visualization [5]. During the first four years, the group grew to a size of 15 researchers, working on projects in medical visualization, the visualization of geological data and models, flow visualization, the visualization of biological data, marine data visualization, and others. Since then, the group is continously contributing to the field (see, for example, the publications of the Bergen VisGroup).
In May 2013, at the Eurographics conference in Girona, Spain, Helwig Hauser - together with his colleagues I. Viola et al. - received the Dirk Bartz Prize for Visual Computing in Medicine (Eurographics Medical Prize) for research on high-quality 3D visualization of in-situ ultrasonography [6].
Helwig Hauser's interests are diverse in visualization and related fields, including interactive visual analysis, illustrative visualization, and the combination of scientific and information visualization, as well as many other related topics. Helwig Hauser is also particularly interested in the application of visualization to the fields of medicine, geoscience, climatology, biology, engineering, and others.
Related links (theses of Helwig Hauser):
Habilitation of Helwig Hauser, entitled ”Generalizing Focus+Context Visualization” (PDF, 152 pages, dated Dec., 2003), together with the related supplement (PDF, 100 pages, dated Dec., 2003)
Dissertation (PhD thesis) of Helwig Löffelmann, entitled ”Visualizing Local Properties and Characteristic Structures of Dynamical Systems” (PDF, 130 pages, dated Nov., 1998 – note that H. Hauser was named H. Löffelmann before his marriage in 1999)
Related links (cited from the short biography above):
[1] Home page of the Institute of Computer Graphics and Algorithms at the Vienna University of Technology in Austria
[2] Home page of the VRVis Research Center in Vienna, Austria
[3] Home page of the IEEE Visualization 2004 Contest at the IEEE CS and our entry
[4] Home page of the Heinz Zemanek Award, given every two years to one outstanding research work in CS or a related field, Hall of Fame
[5] Home page of the visualization group at the Dept. of Informatics, Univ. of Bergen, Norway
[6] Home page of the Dirk Bartz Prize, given every second year for outstanding CG contributions to medicine, 2014 Hall of Fame
Lecturing
VisFoundations (INF250) course about the foundations of data-oriented visual computing (end of Bachelor, beginning of Master).
Computer Graphics (INF251) course about computer graphics which is designed for the end of the UiB Bachelor study program in computer science and which acts as a preliminary for the UiB Master study program on visualization.
Visualization (INF252) course about visualization which is central to the UiB Master study program on visualization.
Project in Visualization (INF219)programming project in visualization, embedded within the UiB Master study program on visualization.
Selected publs. 2007-2015 (filtered list, all)
Interactive Visual Analysis of Large Simulation Ensembles by Kr. Matković, D. Gračanin, M. Jelović, H. Hauser. Proc. Winter Simulation Conference 2015: 517-528, 2015
The State-of-the-Art of Set Visualizationby B. Alsallakh, L. Micallef, W. Aigner, H. Hauser, S. Miksch, P. Rodgers. Computer Graphics Forum, 2015
Expressive Seeding of Multiple Stream Surfaces for Interactive Flow Exploration by A. Brambilla & H. Hauser. Computers & Graphics 47: 123-134, 2015
Interactive Visual Steering of Hierarchical Simulation Ensembles by R. Splechtna, Kr. Matković, D. Gračanin, M. Jelović, H. Hauser. Proc. IEEE VAST:89-96
Interactively Illustrating Polymerization using Three-level Model Fusion by I. Kolesár, J. Parulek, I. Viola, St. Bruckner, A.-Kr. Stavrum, H. Hauser. BMC Bioinformatics 15:345-360, 2014
Attribute Signatures: Dynamic Visual Summaries for Analyzing Multivariate Geographical Databy Ç. Turkay, A. Slingsby, H. Hauser, J. Wood, J. Dykes. IEEE Trans. Vis. & Computer Graphics 20(12):2033-2042, 2014
Characterizing Cancer Subtypes Using Dual Analysis in Caleydo StratomeX by Ç. Turkay, A. Lex, M. Streit, H. Pfister, H. Hauser. IEEE Computer Graphics & Applications 34(2):38-47, 2014
Visual Methods for Analyzing Probabilistic Classification Data by B. Alsallakh, A. Hanbury, H. Hauser, S. Miksch, A. Rauber. IEEE Trans. Vis. & Computer Graphics20(12):1703-1712, 2014
Visual Analytics for Complex Engineering Systems: Hybrid Visual Steering of Simulation Ensembles by Kr. Matković, D. Gracanin, R. Splechtna, M. Jelović, B. Stehno, H. Hauser, W. Purgathofer. IEEE Trans. Vis. & Computer Graphics 20(12):1803-1812, 2014
Interactive Visual Analysis of Heterogeneous Cohort Study Data by P. Angelelli, St. Oeltze, Ç. Turkay, J. Haász, E. Hodneland, A. Lundervold, A. J. Lundervold, B. Preim, H. Hauser. IEEE CG & Appls. 34(5):70-82, 2014
Radial Sets: Interactive Visual Analysis of Large Overlapping Sets by B. Alsallakh, W. Aigner, S. Miksch, and H. Hauser. IEEE Trans. Vis. & Computer Graphics19(12):2496-2505, 2013
Hypothesis Generation by Interactive Visual Exploration of Heterogeneous Medical Databy Ç. Turkay, A. Lundervold, A. J. Lundervold, and H. Hauser.Lecture Notes in Computer Science 7947:1-12, 2013 (often cited)
High-Quality 3D Visualization of In-Situ Ultrasonography by I. Viola, Å. Birkeland, V. Šoltészová, L. Helljesen, H. Hauser, Sp. Kotopoulis, K. Nylund, D. M. Ulvang, O. Kr. Øye, Tr. Hausken, O. H. Gilja. Dirk Bartz Prize of Eurographics 2013, pp. 1-4, 2013
Geological Storytelling by E. Lidal, M. Natali, D. Patel, H. Hauser, I. Viola. Computers & Graphics 37(5):445-459, 2013
Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey by J. Kehrer and H. Hauser.IEEE Trans. Vis. & Computer Graphics 19(3):495-513, 2013 (often cited)
Representative Factor Generation for the Interactive Visual Analysis of High-Dimensional Data by Ç. Turkay, A. Lundervold, A. J. Lundervold, and H. Hauser. IEEE Trans. Vis. & Computer Graphics 18(12):2621-2630, 2012
Interactive Visual Exploration and Analysis of Multivariate Simulation Data by H. Doleisch and H. Hauser. Computing in Science Engineering 14(2):70-77, 2012
Scientific Storytelling Using Visualization by Kw.-L. Ma, I. Liao, J. Frazier, H. Hauser, and H.-N. Kostis. IEEE Computer Graphics and Applications 32(1):12-19, 2012
Straightening Tubular Flow for Side-by-Side Visualization by P. Angelelli and H. Hauser. IEEE Trans. Vis. & Computer Graphics 17(12):2063-2070, 2011
Brushing Dimensions – A Dual Visual Analysis Model for High-dimensional Data by Ç. Turkay, P. Filzmoser, and H. Hauser. Computer Graphics Forum29(3):813-822, 2011
Energy-scale Aware Feature Extraction for Flow Visualization by A. Pobitzer, M. Tutkun, Ø. Andreassen, R. Fuchs, R. Peikert, and H. Hauser. Computer Graphics Forum30(3):771-780, 2011
Interactive Visual Analysis of Temporal Cluster Structures by Ç. Turkay, J. Parulek, N. Reuter, and H. Hauser. Computer Graphics Forum 30(3):711-720, 2011
Curve Density Estimates by O. D. Lampe and H. Hauser.Computer Graphics Forum 30(3):633-642, 2011
The State of the Art in Topology-based Visualization of Unsteady Flow by A. Pobitzer, R. Peikert, R. Fuchs, B. Schindler, A. Kuhn, H. Theisel, Kr. Matković, and H. Hauser. Computer Graphics Forum30(6):1789-1811, 2011
Interactive Visual Analysis of Contrast-enhanced Ultrasound Data based on Small Neighborhood Statistics by P. Angelelli, K. Nylund, O. H. Gilja, and H. Hauser. Computers & Graphics 35(2):218-226, 2011
Interactive Visual Analysis of Heterogeneous Scientific Data across an Interface by J. Kehrer, Ph. Muigg, H. Doleisch, and H. Hauser. IEEE Trans. Vis. & Computer Graphics 17(7):934-946, 2011
Interactive Visual Analysis of Multiple Simulation Runs using the Simulation Model View: Understanding and Tuning of an Electronic Unit Injector by Kr. Matković, D. Gračanin, M. Jelović, A. Ammer, A. Lež, and H. Hauser. IEEE Trans. Vis. & Computer Graphics16(6):1449-1457, 2010
Brushing Moments in Interactive Visual Analysisby J. Kehrer, P. Filzmoser, and H. Hauser. Computer Graphics Forum 29(3):813-822, 2010
Toward a Lagrangian Vector Field Topologyby R. Fuchs, J. Kemmler, B. Schindler, J. Waser, F. Sadlo, H. Hauser, and R. Peikert. Computer Graphics Forum29(3):1163-1172, 2010
On the Way Towards Topology-Based Visualization of Unsteady Flow – the State of the Art by Ar. Pobitzer, R. Peikert, R. Fuchs, B. Schindler, A. Kuhn, H. Theisel, Kr. Matković, and H. Hauser. Eurographics 2010 State-of-the-Art Proceedings, pp. 137-154, 2010
Curve-Centric Volume Reformation for Comparative Visualization by O. Daae Lampe, C. Correa, Kw.-L. Ma, and H. Hauser. IEEE Trans. Vis. & Computer Graphics 15(6):1235-1242, 2009
Knowledge-Assisted Visualization of Seismic Databy D. Patel, Ø. Sture, H. Hauser, Chr. Giertsen, and E. Gröller.Computers & Graphics 33(5):585-596, 2009
Visualization of Multi-Variate Scientific Databy R. Fuchs and H. Hauser. Computer Graphics Forum28(6):1670-1690, 2009 (often cited)
Sonography of the Small Intestine by K. Nylund, Sv. Ødegaard, Tr. Hausken, G. Folvik, G. A. Lied, I. Viola, H. Hauser, and O. H. Gilja. World Journal of Gastroenterology15(11):1319-1330, 2009 (often cited)
Interactive Visual Analysis of Complex Scientific Data as Families of Data Surfaces by Kr. Matković, D. Gračanin, B. Klarin, H. Hauser. IEEE Trans. Vis. & Computer Graphics 15(6):1351-1358, 2009
Path Line Attributes – an Information Visualization Approach to Analyzing the Dynamic Behavior of 3D Time-Dependent Flow Fieldsby K. Shi, H. Theisel, H. Hauser, T. Weinkauf, Kr. Matković, H.-Chr. Hege, and H.-P. Seidel. In Topology-based Methods in Visualization II, Springer, 75-88, 2009 (often cited)
Hypothesis Generation in Climate Research with Interactive Visual Data Exploration by J. Kehrer, Fl. Ladstädter, Ph. Muigg, H. Doleisch, A. Steiner, H. Hauser.IEEE Trans. Vis. & Computer Graphics 14(6):1579-1586, 2008
Interactive Visual Steering – Rapid Visual Prototyping of a Common Rail Injection Systemby Kr. Matković, D. Gračanin, M. Jelović, H. Hauser. IEEE Trans. on Vis. and Computer Graphics 14(6):1699-1706, 2008
Interactive Visual Analysis of Set-Typed Databy W. Freiler, Kr. Matković, H. Hauser. IEEE Transactions on Visualization and Computer Graphics 14(6):1340-1347, 2008
A Four-level Focus+Context Approach to Interactive Visual Analysis of Temporal Features in Large Scientific Data by Ph. Muigg, J. Kehrer, St. Oeltze, H. Piringer, H. Doleisch, B. Preim, H. Hauser.Computer Graphics Forum 27(3):775-782, 2008 (often cited)
Parallel Vectors Criteria for Unsteady Flow Vortices by R. Fuchs, R. Peikert, H. Hauser, F. Sadlo, Ph. Muigg. IEEE Transactions on Visualization and Computer Graphics 14(3):615-626, 2008
Two-Level Approach to Efficient Visualization of Protein Dynamics by O. Daae Lampe, I. Viola, N. Reuter, H. Hauser. IEEE Transactions on Visualization and Computer Graphics, 13(6):1616-1623, 2007 (often cited)
Interactive Visual Analysis of Perfusion Databy St. Oeltze, H. Doleisch, H. Hauser, Ph. Muigg, and B. Preim.IEEE Transactions on Visualization and Computer Graphics, 13(6):1392-1399, 2007 (often cited)
Scalable Hybrid Unstructured and Structured Grid Raycasting by Ph. Muigg, M. Hadwiger, H. Doleisch, and H. Hauser. IEEE Transactions on Visualization and Computer Graphics, 13(6):1592-1599, 2007 (often cited)
Visualization of Multi-variate Scientific Databy R. Bürger and H. Hauser. Eurographics 2007 State-of-the-Art Proceedings, pp. 117-134, 2007 (often cited)
Integrating Local Feature Detectors in the Interactive Visual Analysis of Flow Simulation Data by R. Bürger, Ph. Muigg, M. Ilčík, H. Doleisch, and H. Hauser. EuroVis 2007 Proc., pp. 171-178, 2007
Story Telling for Presentation in Volume Visualization by M. Wohlfart and H. Hauser. EuroVis 2007 Proc., pp. 91-98, 2007 (often cited)
Topology-Based Flow Visualization, The State of the Art by R. Laramee, H. Hauser, L. Zhao, and Fr. Post. In Topology-based Methods in Visualization, Springer, pp. 1-20, 2007 (cited >100*)
Publications
2024
@article{splechtna2024interactive,
title={Interactive design-of-experiments: optimizing a cooling system},
author={Splechtna, Rainer and Behravan, Majid and Jelovic, Mario and Gracanin, Denis and Hauser, Helwig and Matkovic, Kre{\v{s}}imir},
journal={IEEE Transactions on Visualization and Computer Graphics},
year={2024},
publisher={IEEE},
doi = {10.1109/TVCG.2024.3456356},
url = {https://doi.org/10.1109/TVCG.2024.3456356},
images = {images/splechtna2024cooling.png},
thumbnails = {images/splechtna2024cooling.png},
pdf = {pdfs/splechtna2024cooling.pdf},
abstract = {The optimization of cooling systems is important in many cases, for example for cabin and battery cooling in electric cars. Such an optimization is governed by multiple, conflicting objectives and it is performed across a multi-dimensional parameter space.The extent of the parameter space, the complexity of the non-linear model of the system,as well as the time needed per simulation run and factors that are not modeled in the simulation necessitate an iterative, semi-automatic approach. We present an interactive visual optimization approach, where the user works with a p-h diagram to steer an iterative, guided optimization process. A deep learning (DL) model provides estimates for parameters, given a target characterization of the system, while numerical simulation is used to compute system characteristics for an ensemble of parameter sets. Since the DL model only serves as an approximation of the inverse of the cooling system and since target characteristics can be chosen according to different, competing objectives, an iterative optimization process is realized, developing multiple sets of intermediate solutions, which are visually related to each other.The standard p-h diagram, integrated interactively in this approach, is complemented by a dual, also interactive visual representation of additional expressive measures representing the system characteristics. We show how the known four-points semantic of the p-h diagram meaningfully transfers to the dual data representation.When evaluating this approach in the automotive domain, we found that our solution helped with the overall comprehension of the cooling system and that it lead to a faster convergence during optimization.}
}
2022
@ARTICLE {Garrison2022PhysioSTAR,
author = "Laura A. Garrison and Ivan Kolesar and Ivan Viola and Helwig Hauser and Stefan Bruckner",
title = "Trends & Opportunities in Visualization for Physiology: A Multiscale Overview",
journal = "Computer Graphics Forum",
year = "2022",
volume = "41",
number = "3",
publisher = "The Eurographics Association and John Wiley & Sons Ltd.",
pages = "609-643",
doi = "10.1111/cgf.14575",
abstract = "Combining elements of biology, chemistry, physics, and medicine, the science of human physiology is complex and multifaceted. In this report, we offer a broad and multiscale perspective on key developments and challenges in visualization for physiology. Our literature search process combined standard methods with a state-of-the-art visual analysis search tool to identify surveys and representative individual approaches for physiology. Our resulting taxonomy sorts literature on two levels. The first level categorizes literature according to organizational complexity and ranges from molecule to organ. A second level identifies any of three high-level visualization tasks within a given work: exploration, analysis, and communication. The findings of this report may be used by visualization researchers to understand the overarching trends, challenges, and opportunities in visualization for physiology and to provide a foundation for discussion and future research directions in this area. ",
images = "images/garrison-STAR-taxonomy.png",
thumbnails = "images/garrison-STAR-thumb.png",
pdf = "pdfs/Garrison_STAR_cameraready.pdf",
publisher = "The Eurographics Association and John Wiley \& Sons Ltd.",
project = "VIDI"
}
2021
@article{brushingComparison,
author={Fan, Chaoran and Hauser, Helwig},
journal={IEEE Computer Graphics and Applications},
title={On sketch-based selections from scatterplots using KDE, compared to Mahalanobis and CNN brushing},
year={2021},
volume={},
number={},
pages={1-13},
doi={10.1109/MCG.2021.3097889},
abstract = {"Fast and accurate brushing is crucial in visual data exploration and sketch-based solutions are successful methods. In this paper, we detail a solution, based on kernel density estimation (KDE), which computes a data subset selection in a scatterplot from a simple click-and-drag interaction. We explain, how this technique relates to two alternative approaches, i.e., Mahalanobis brushing and CNN brushing. To study this relation, we conducted two user studies and present both a quantitative three-fold comparison as well as additional details about the prevalence of all possible cases in that each technique succeeds/fails. With this, we also provide a comparison between empirical modeling and implicit modeling by deep learning in terms of accuracy, efficiency, generality and interpretability."},
pdf = "pdfs/Fan-2021-brushingComparison.pdf",
images = "images/Fan-2021-brushingComparison.png",
thumbnails = "images/Fan-2021-brushingComparison.png",
}
@ARTICLE {Garrison-2021-DimLift,
author = {Garrison, Laura and M\"{u}ller, Juliane and Schreiber, Stefanie and Oeltze-Jafra, Steffen and Hauser, Helwig and Bruckner, Stefan},
title = {DimLift: Interactive Hierarchical Data Exploration through Dimensional Bundling},
journal={IEEE Transactions on Visualization and Computer Graphics},
year = {2021},
abstract = {The identification of interesting patterns and relationships is essential to exploratory data analysis. This becomes increasingly difficult in high dimensional datasets. While dimensionality reduction techniques can be utilized to reduce the analysis space, these may unintentionally bury key dimensions within a larger grouping and obfuscate meaningful patterns. With this work we introduce DimLift, a novel visual analysis method for creating and interacting with dimensional bundles. Generated through an iterative dimensionality reduction or user-driven approach, dimensional bundles are expressive groups of dimensions that contribute similarly to the variance of a dataset. Interactive exploration and reconstruction methods via a layered parallel coordinates plot allow users to lift interesting and subtle relationships to the surface, even in complex scenarios of missing and mixed data types. We exemplify the power of this technique in an expert case study on clinical cohort data alongside two additional case examples from nutrition and ecology.},
volume = {27},
number = {6},
pages = {2908--2922},
pdf = {pdfs/garrison-2021-dimlift.pdf},
images = {images/garrison_dimlift.jpg},
thumbnails = {images/garrison_dimlift_thumb.jpg},
youtube = {https://youtu.be/JSZuhnDyugA},
doi = {10.1109/TVCG.2021.3057519},
git = {https://github.com/lauragarrison87/DimLift},
project = {VIDI},
}
@ARTICLE {Mueller-2021-IDA,
author = {M\"{u}ller, Juliane and Garrison, Laura and Ulbrich, Philipp and Schreiber, Stefanie and Bruckner, Stefan and Hauser, Helwig and Oeltze-Jafra, Steffen},
title = {Integrated Dual Analysis of Quantitative and Qualitative High-Dimensional Data},
journal={IEEE Transactions on Visualization and Computer Graphics},
year = {2021},
abstract = {The Dual Analysis framework is a powerful enabling technology for the exploration of high dimensional quantitative data by treating data dimensions as first-class objects that can be explored in tandem with data values. In this work, we extend the Dual Analysis framework through the joint treatment of quantitative (numerical) and qualitative (categorical) dimensions. Computing common measures for all dimensions allows us to visualize both quantitative and qualitative dimensions in the same view. This enables a natural joint treatment of mixed data during interactive visual exploration and analysis. Several measures of variation for nominal qualitative data can also be applied to ordinal qualitative and quantitative data. For example, instead of measuring variability from a mean or median, other measures assess inter-data variation or average variation from a mode. In this work, we demonstrate how these measures can be integrated into the Dual Analysis framework to explore and generate hypotheses about high-dimensional mixed data. A medical case study using clinical routine data of patients suffering from Cerebral Small Vessel Disease (CSVD), conducted with a senior neurologist and a medical student, shows that a joint Dual Analysis approach for quantitative and qualitative data can rapidly lead to new insights based on which new hypotheses may be generated.},
volume = {27},
number = {6},
pages = {2953--2966},
pdf = {pdfs/Mueller_2020_IDA.pdf},
images = {images/Mueller_2020_IDA.jpg},
thumbnails = {images/Mueller_2020_IDA.png},
doi = {10.1109/TVCG.2021.3056424},
git = {https://github.com/JulianeMu/IntegratedDualAnalysisAproach_MDA},
project = {VIDI},
}
@ARTICLE{Palenik-2020-IsoTrotter,
author={P\'{a}lenik, Juraj and Spengler, Thomas and Hauser, Helwig},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={{IsoTrotter: Visually Guided Emprical Modelling of Atmospheric Convection}},
abstract={Empirical models, fitted to data from observations, are often used in natural sciences to describe physical behaviour and support discoveries. However, with more complex models, the regression of parameters quickly becomes insufficient, requiring a visual parameter space analysis to understand and optimize the models. In this work, we present a design study for building a model describing atmospheric convection. We present a mixed-initiative approach to visually guided modelling, integrating an interactive visual parameter space analysis with partial automatic parameter optimization. Our approach includes a new, semi-automatic technique called IsoTrotting, where we optimize the procedure by navigating along isocontours of the model. We evaluate the model with unique observational data of atmospheric convection based on flight trajectories of paragliders.},
year={2021},
volume={27},
number={2},
pages={775-784},
doi={10.1109/TVCG.2020.3030389},
pdf={pdfs/2020-10-20-Palenik-IsoTrotter.pdf},
images={images/IsoTrotter2020.png},
thumbnails={images/IsoTrotter2020.png}
}
2020
@article{sketchingQuery,
author={Fan, Chaoran and Matkovic, Kresimir and Hauser, Helwig},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={Sketch-based fast and accurate querying of time series using parameter-sharing LSTM networks},
year={2020},
volume={},
number={},
pages={1-12},
doi={10.1109/TVCG.2020.3002950},
abstract = {"Sketching is one common approach to query time series data for patterns of interest. Most existing solutions for matching the data with the interaction are based on an empirically modeled similarity function between the user's sketch and the time series data with limited efficiency and accuracy. In this paper, we introduce a machine learning based solution for fast and accurate querying of time series data based on a swift sketching interaction. We build on existing LSTM technology (long short-term memory) to encode both the sketch and the time series data in a network with shared parameters. We use data from a user study to let the network learn a proper similarity function. We focus our approach on perceived similarities and achieve that the learned model also includes a user-side aspect. To the best of our knowledge, this is the first data-driven solution for querying time series data in visual analytics. Besides evaluating the accuracy and efficiency directly in a quantitative way, we also compare our solution to the recently published Qetch algorithm as well as the commonly used dynamic time warping (DTW) algorithm.."},
pdf = "pdfs/Fan-2020-sketchingQuery.pdf",
images = "images/Fan-2020-sketchingQuery.png",
thumbnails = "images/Fan-2020-sketchingQuery.png",
}
@ARTICLE{Palenik-2019-Splatting,
author={J. P\'{a}lenik and J. By\v{s}ka and S. Bruckner and H. Hauser},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={Scale-Space Splatting: Reforming Spacetime for Cross-Scale Exploration of Integral Measures in Molecular Dynamics},
year={2020},
volume={26},
number={1},
pages={643--653},
keywords={Data visualization;Computational modeling;Time series analysis;Atmospheric measurements;Particle measurements;Analytical models;Kernel;Scale space;time-series;scientific simulation;multi-scale analysis;space-time cube;molecular dynamics},
doi={10.1109/TVCG.2019.2934258},
ISSN={1077-2626},
month={},
pdf = "pdfs/scale-space-splatting.pdf",
images = "images/scale-space-teaser.png",
thumbnails = "images/scale-space-teaser-thumb.png",
abstract = "Understanding large amounts of spatiotemporal data from particle-based simulations, such as molecular dynamics, often relies on the computation and analysis of aggregate measures. These, however, by virtue of aggregation, hide structural information about the space/time localization of the studied phenomena. This leads to degenerate cases where the measures fail to capture distinct behaviour. In order to drill into these aggregate values, we propose a multi-scale visual exploration technique. Our novel representation, based on partial domain aggregation, enables the construction of a continuous scale-space for discrete datasets and the simultaneous exploration of scales in both space and time. We link these two scale-spaces in a scale-space space-time cube and model linked views as orthogonal slices through this cube, thus enabling the rapid identification of spatio-temporal patterns at multiple scales. To demonstrate the effectiveness of our approach, we showcase an advanced exploration of a protein-ligand simulation.",
}