PhD student
Publications
2013
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[Bibtex] @INPROCEEDINGS {Borgo13GlyphBased,
author = "Rita Borgo and Johannes Kehrer and David H. S. Chung and Eamonn Maguire and Robert S. Laramee and Helwig Hauser and Matthew Ward and Min Chen ",
title = "Glyph-based Visualization: Foundations, Design Guidelines, Techniques and Applications",
booktitle = "EuroGraphics 2013 State-of-the-Art Reports (STARs)",
year = "2013",
pages = "39--63",
address = "Girona, Spain",
publisher = "Eurographics Association",
abstract = "This state of the art report focuses on glyph-based visualization, a common form of visual design where a data set is depicted by a collection of visual objects referred to as glyphs. Its major strength is that patterns of multivariate data involving more than two attribute dimensions can often be more readily perceived in the context of a spatial relationship, whereas many techniques for spatial data such as direct volume rendering find difficult to depict with multivariate or multi-field data, and many techniques for non-spatial data such as parallel coordinates are less able to convey spatial relationships encoded in the data. This report fills several major gaps in the literature, drawing the link between the fundamental concepts in semiotics and the broad spectrum of glyph-based visualization, reviewing existing design guidelines and implementation techniques, and surveying the use of glyph-based visualization in many applications.",
pdf = "pdfs/Borgo13GlyphBased.pdf",
images = "images/Borgo13GlyphBased.jpg",
thumbnails = "images/Borgo13GlyphBased.jpg",
url = "http://diglib.eg.org/EG/DL/conf/EG2013/stars/039-063.pdf",
issn = "1017-4656",
doi = "10.2312/conf/EG2013/stars/039-063"
} [Bibtex]
@MISC {Hauser13VisTutorial,
author = "Steffen Oeltze and Johannes Kehrer and Helwig Hauser",
title = "Interactive Visual Analysis of Scientific Data",
howpublished = "Tutorial at the IEEE VisWeek 2013",
month = "October",
year = "2013",
abstract = "In a growing number of application areas, a subject or phenomenon is investigated by means of multiple datasets being acquired over time (spatiotemporal), comprising several attributes per data point (multi-variate), stemming from different data sources (multi-modal) or multiple simulation runs (multi-run/ensemble) [KH13]. Interactive visual analysis (IVA) comprises concepts and techniques for a user-guided knowledge discovery in such complex data. Through a tight feedback loop of computation, visualization and user interaction, it provides new insight into the data and serves as a vehicle for hypotheses generation or validation. It is often implemented via a multiple coordinated view framework where each view is equipped with interactive drill-down operations for focusing on data features. Two classes of views are integrated: physical views, such as direct volume rendering, show information in the context of the spatiotemporal observation space while attribute views, such as scatter plots and parallel coordinates, show relationships between multiple data attributes. The user may drill-down the data by selecting interesting regions of the observation space or attribute ranges leading to a consistent highlighting of this selection in all other views (brushing-and-linking). Three patterns of explorative/analytical procedures may be accomplished by doing so. In a feature localization, the user searches for places in the 3D/4D observation space where certain attribute values are present. In a multi-variate analysis, relations between data attributes are investigated, e.g., by searching for correlations. In a local investigation, the user inspects the values of selected attributes with respect to certain spatiotemporal subsets of the observation space. In this tutorial, we discuss examples for successful applications of IVA to scientific data from various fields: climate research, medicine, epidemiology, and flow simulation / computation, in particular for automotive engineering. We base our discussions on a theoretical foundation of IVA which helps the tutorial attendees in transferring the subject matter to their own data and application area. In the course of the tutorial, the attendees will become acquainted with techniques from statistics and knowledge discovery, which proved to be particularly useful for a specific IVA application. The tutorial further comprises an overview of off-the-shelf IVA solutions, which may be be particularly interesting for visualization practitioners. It is concluded by a summary of the gained knowledge and a discussion of open problems in IVA of scientific data.",
images = "images/",
thumbnails = "images/iva_scientificdata_proposal_2013_Image.png",
location = "Atlanta (GA), USA",
pres = "pdfs/iva_scientificdata_proposal_2013.pdf"
}@ARTICLE {Kehrer13VisualizationAnd,
author = "Johannes Kehrer and Helwig Hauser",
title = "Visualization and Visual Analysis of Multi-faceted Scientific Data: a Survey",
journal = "IEEE Transactions on Visualization and Computer Graphics",
year = "2013",
volume = "19",
number = "3",
pages = "495-513",
abstract = "Visualization and visual analysis play important roles in exploring, analyzing and presenting scientific data. In many disciplines, data and model scenarios are becoming multi-faceted: data are often spatio-temporal and multi-variate; they stem from different data sources (multi-modal data), from multiple simulation runs (multi-run/ensemble data), or from multi-physics simulations of interacting phenomena (multi-model data resulting from coupled simulation models). Also, data can be of different dimensionality or structured on various types of grids that need to be related or fused in the visualization. This heterogeneity of data characteristics presents new opportunities as well as technical challenges for visualization research. Visualization and interaction techniques are thus often combined with computational analysis. In this survey, we study existing methods for visualization and interactive visual analysis of multi-faceted scientific data. Based on a thorough literature review, a categorization of approaches is proposed. We cover a wide range of fields and discuss to which degree the different challenges are matched with existing solutions for visualization and visual analysis. This leads to conclusions with respect to promising research directions, for instance, to pursue new solutions for multi-run and multi-model data as well as techniques that support a multitude of facets.",
pdf = "pdfs/Kehrer13VisualizationAnd.pdf",
images = "images/Kehrer13VisualizationAnd01.png",
thumbnails = "images/Kehrer13VisualizationAnd01_thumb.png",
issn = "1077-2626",
doi = "http://doi.ieeecomputersociety.org/10.1109/TVCG.2012.110",
publisher = "IEEE Computer Society",
address = "Los Alamitos, CA, USA"
}2011
![[VID]](https://vis.uib.no/wp-content/papercite-data/images/video.png)
[Bibtex] @ARTICLE {kehrer11heterogeneous,
author = "Johannes Kehrer and Philipp Muigg and Helmut Doleisch and Helwig Hauser",
title = "Interactive Visual Analysis of Heterogeneous Scientific Data across an Interface",
journal = "IEEE Transactions on Visualization and Computer Graphics",
year = "2011",
volume = "17",
number = "7",
pages = "934--946",
abstract = "We present a systematic approach to the interactive visual analysis of heterogeneous scientific data. The data consists of two interrelated parts given on spatial grids over time (e.g., atmosphere and ocean part from a coupled climate model). By integrating both data parts in a framework of coordinated multiple views (with linking and brushing), the joint investigation of features across the data parts is enabled. An interface is constructed between the data parts that specifies (a) which grid cells in one part are related to grid cells in the other part, and vice versa, (b) how selections (in terms of feature extraction via brushing) are transferred between the two parts, and (c) how an update mechanism keeps the feature specification in both data parts consistentduring the analysis. We also propose strategies for visual analysis that result in an iterative refinement of features specified across both data parts. Our approach is demonstrated in the context of a complex simulation of fluid--structure interaction and a multi-run climate simulation.",
pdf = "pdfs/kehrer11heterogeneous.pdf",
vid = "vids/kehrer11heterogeneous.html",
images = "images/kehrer11heterogeneous2.jpg, images/kehrer11heterogeneous3.jpg, images/kehrer11heterogeneous0.jpg, images/kehrer11heterogeneous1.jpg",
thumbnails = "images/kehrer11heterogeneous2_thumb.jpg, images/kehrer11heterogeneous3_thumb.jpg, images/kehrer11heterogeneous0_thumb.jpg, images/kehrer11heterogeneous1_thumb.jpg",
event = "IEEE VisWeek 2011",
location = "Providence, RI, USA",
url = "http://dx.doi.org/10.1109/TVCG.2010.111"
}@PHDTHESIS {kehrer11thesis,
author = "Johannes Kehrer",
title = "Interactive Visual Analysis of Multi-faceted Scientific Data",
school = "Department of Informatics, University of Bergen, Norway",
year = "2011",
month = "Mar",
abstract = "Visualization plays an important role in exploring, analyzingand presenting large and heterogeneous scientific data that arise in many disciplines of medicine, research, engineering, and others. We can see that model and data scenarios are becoming increasingly multi-faceted: data are often multi-variate and time-dependent, they stem from different data sources (multi-modal data), from multiple simulation runs (multi-run data), or from multi-physics simulations of interacting phenomena that consist of coupled simulation models (multi-model data). The different data characteristics result in special challenges for visualization research and interactive visual analysis. The data are usually large and come on various types of grids with different resolution that need to be fused in the visual analysis.This thesis deals with different aspects of the interactive visual analysis of multi-faceted scientific data. The main contributions of this thesis are: 1) a number of novel approaches and strategies for the interactive visual analysis of multi-run data; 2) a concept that enables the feature-based visual analysis across an interface between interrelated parts of heterogeneous scientific data (including data from multi-run and multi-physics simulations); 3) a model for visual analysis that is based on the computation of traditionaland robust estimates of statistical moments from higher-dimensional multi-run data; 4) procedures for visual exploration of time-dependent climate data that support the rapid generation of promising hypotheses, which are subsequently evaluated with statistics; and 5) structured design guidelines for glyph-based 3D visualization of multi-variate data together with a novel glyph. All these approaches are incorporated in a single framework for interactive visual analysis that uses powerful concepts such as coordinated multiple views, feature specification via brushing, and focus+context visualization. Especially the data derivation mechanism of the framework has proven to be very useful for analyzing different aspects of the data at different stages of the visual analysis. The proposed concepts and methods are demonstrated in a number of case studies that are based on multi-run climate data and data from a multi-physics simulation.",
pdf = "pdfs/kehrer11thesis.pdf",
images = "images/kehrer11thesis.jpg, images/kehrer11heterogeneous1.jpg, images/kehrer08vis01.jpg, images/kehrer11thesis1.png",
thumbnails = "images/kehrer11thesis_thumb.jpg, images/kehrer11heterogeneous1_thumb.jpg, images/kehrer08vis01_thumb.jpg, images/kehrer11thesis1_thumb.png",
url = "http://www.ii.UiB.no/vis/team/kehrer/thesis/",
isbn = "978-82-308-1733-9"
}2010
[Bibtex]
@MISC {kehrer10edaVis,
author = "Johannes Kehrer",
title = "Selected Opportunities for Integrating Statistics and Visualization in Multi-dimensional Data Exploration",
howpublished = "Talk at EDAVis: Workshop on Exploratory Data Analysis and Visualisation",
month = "May 27",
year = "2010",
abstract = "Visualization and statistics both facilitate the understanding of complex data characteristics, and there is a long history of relations between the two fields. Traditional approaches for data analysis often consider passive visualizations of statistical data properties. Interactive visual analysis, however, as addressed in this talk, allows the iterative exploration and analysis of data in a guided human computer dialog. Graphical representations of the data and well-proven interaction mechanisms are used to concurrently show, explore, and analyze complex (i.e., time-dependent, multi-variate, and/or multi-dimensional) data. Interesting subsets of the data are interactively selected (brushed) directly on the screen, the relations are investigated in other linked views (including 2D scatterplots, histograms, function graph views, parallel coordinates, but also 3D views of volumetric data).In recent work, we have studied the integration of large amounts of locally aggregated statistical data properties as well as measures of outlyingnessin an interactive visual analysis process. The approach is demonstrated on the visual analysis of multi-dimensional climate data. A discussion of possibilities explains how a further combination of interactive statisticalplots and proven interaction schemes from visualization research shows greatpotential for future research.",
images = "images/kehrer10edavis.jpg",
thumbnails = "images/kehrer10edavis_thumb.jpg",
location = "Vienna, Austria"
}@ARTICLE {kehrer10moments,
author = "Johannes Kehrer and Peter Filzmoser and Helwig Hauser",
title = "Brushing Moments in Interactive Visual Analysis",
journal = "Computer Graphics Forum",
year = "2010",
volume = "29",
number = "3",
pages = "813--822",
month = "june",
abstract = "We present a systematic study of opportunities for the interactive visual analysis of multi-dimensional scientific data. This is based on the integration of statistical aggregations along selected data dimensions in a framework of coordinated multiple views (with linking and brushing). Traditional and robust estimates of the four statistical moments (mean, variance, skewness, and kurtosis) as well as measures of outlyingness are integrated in an iterative visual analysis process. Brushing particular statistics, the analyst can investigate data characteristics such as trends and outliers. We present a categorization of beneficial combinations of attributes in 2D scatterplots: (a) k-th vs. (k+1)-th statistical moment of a traditional or robust estimate, (b) traditional vs. robust version of the same moment, (c) two different robust estimates of the same moment. We propose selected view transformations to iteratively construct this multitude of informative views as well as to enhance the depiction of the statistical properties in the scatterplots. In the framework, we interrelate the original distributional data and the aggregated statistics, which allows the analyst to work with both data representations simultaneously. We demonstrate our approach in the context of two visual analysis scenarios of multi-run climate simulations.",
vid = "vids/kehrer10moments.html",
images = "images/kehrer10moments.jpg, images/kehrer10moments1.jpg, images/kehrer10moments2.jpg",
thumbnails = "images/kehrer10moments_thumb.jpg, images/kehrer10moments1_thumb.jpg, images/kehrer10moments2_thumb.jpg",
event = "EuroVis 2010",
location = "Bordeaux, France",
pres = "pdfs/kehrer10moments-presentation.pdf",
url = "http://dx.doi.org/10.1111/j.1467-8659.2009.01697.x"
} [Bibtex]
@ARTICLE {ladstaedter10explorationClimateData,
author = "Florian Ladst{\"a}dter and Andrea K. Steiner and Bettina C. Lackner and Barbara Pirscher and Gottfried Kirchengast and Johannes Kehrer and Helwig Hauser and Philipp Muigg and Helmut Doleisch",
title = "Exploration of Climate Data Using Interactive Visualization",
journal = "Journal of Atmospheric and Oceanic Technology",
year = "2010",
volume = "27",
number = "4",
pages = "667--679",
month = "April",
abstract = "In atmospheric and climate research, the increasing amount of data available from climate models and observations provides new challenges for data analysis. We present interactive visual exploration as an innovative approach to handle large datasets. Visual exploration does not require any previous knowledge about the data as is usually the case with classical statistics. It facilitatesiterative and interactive browsing of the parameter space in order to quickly understand the data characteristics, to identify deficiencies, to easily focus on interesting features, and to come up with new hypotheses about the data. These properties extend the common statistical treatment of data, and provide a fundamentally different approach. We demonstrate the potential of this technology by exploring atmospheric climate data from different sources including reanalysis datasets, climate models, and radio occultation satellite data. Results are compared to those from classical statistics revealing the complementary advantages of visual exploration. Combining both, the analytical precision of classical statistics and the holistic power of interactive visual exploration, the usual work flow of studying climate data can be enhanced.",
images = "images/ladstaedter10exploration.jpg, images/ladstaedter10exploration1.jpg, images/ladstaedter10exploration3.jpg, images/ladstaedter10exploration2.jpg",
thumbnails = "images/ladstaedter10exploration_thumb.jpg, images/ladstaedter10exploration1_thumb.jpg, images/ladstaedter10exploration3_thumb.jpg, images/ladstaedter10exploration2_thumb.jpg",
url = "http://dx.doi.org/10.1175/2009JTECHA1374.1"
}