bluuebunny/crossref_metadata_2025_split huggingface dataset:
The World's Languages Explorer: Visual Analysis of Language Features in Genealogical and Areal Contexts
Authors: Rohrdantz Christian , Hund Michael , Mayer Thomas , Wälchli Bernhard , Keim Daniel A. 
Published: June 2012

AbstractThis paper presents a novel Visual Analytics approach that helps linguistic researchers to explore the world's languages with respect to several important tasks: (1) The comparison of manually and automatically extracted language features across languages and within the context of language genealogy, (2) the exploration of interrelations among several of such features as well as their homogeneity and heterogeneity within subtrees of the language genealogy, and (3) the exploration of genealogical and areal influences on the features. We introduce theWorld'sLanguagesExplorer, which provides the required functionalities in one single Visual Analytics environment. Contributions are made for different parts of the system: We introduce an extended Sunburst visualization whose so‐called feature‐rings allow for a cross‐comparison of a large number of features at once, within the hierarchical context of the language genealogy. We suggest a mapping of homogeneity measures to all levels of the hierarchy. In addition, we suggest an integration of information from the areal data space into the hierarchical data space. With our approach we bring Visual Analytics research to a new application field, namely Historical Comparative Linguistics, and Linguistic and Areal Typology. Finally, we provide evidence of the good performance of our system in this area through two application case studies conducted by domain experts.
Magnetic Flux Topology of 2D Point Dipoles
Authors: Bachthaler Sven , Sadlo Filip , Weeber Rudolf , Kantorovich Sofia , Holm Christian , Weiskopf Daniel 
Published: June 2012

AbstractMagnetic fields exhibit higher‐order, nonlinear singularities in the form of point‐dipole singularities. In addition, due to absence of divergence, they feature only a subset of invariant structures from traditional vector field topology. For magnetic fields of sets of point dipoles—widely present in physics and often used as an approximation—we present a technique revealing the topology of magnetic flux. The flux topology is identified with areas covered by field lines that directly connect pairs of dipoles. We introduce the dipole connectrix as a reduced one‐manifold representation of those areas. The set of connectrices serves as our concise visualization of the global structure of magnetic flux. In addition, the quantitative values of flux are displayed by the thickness of the connectrices. We evaluate our technique for simulations of ferroparticle monolayers and magnetic gels.
Parallel Computation of 3D Morse‐Smale Complexes
Authors: Shivashankar Nithin , Natarajan Vijay 
Published: June 2012

AbstractThe Morse‐Smale complex is a topological structure that captures the behavior of the gradient of a scalar function on a manifold. This paper discusses scalable techniques to compute the Morse‐Smale complex of scalar functions defined on large three‐dimensional structured grids. Computing the Morse‐Smale complex of three‐dimensional domains is challenging as compared to two‐dimensional domains because of the non‐trivial structure introduced by the two types of saddle criticalities. We present a parallel shared‐memory algorithm to compute the Morse‐Smale complex based on Forman's discrete Morse theory. The algorithm achieves scalability via synergistic use of the CPU and the GPU. We first prove that the discrete gradient on the domain can be computed independently for each cell and hence can be implemented on the GPU. Second, we describe a two‐step graph traversal algorithm to compute the 1‐saddle‐2‐saddle connections efficiently and in parallel on the CPU. Simultaneously, the extremasaddle connections are computed using a tree traversal algorithm on the GPU.
Using Signposts for Navigation in Large Graphs
Authors: May T. , Steiger M. , Davey J. , Kohlhammer J. 
Published: June 2012

AbstractIn this paper we present a new Focus & Context technique for the exploration of large, abstract graphs. Most Focus & Context techniques present context in a visual way. In contrast, our technique uses a symbolic representation: while the focus is a set of visible nodes, labelled signposts provide cues for the context — off‐screen regions of the graph — and indicate the direction of the shortest path linking the visible nodes to these regions. We show how the regions are defined and how they are selected dynamically, depending on the visible nodes. To define the set of visible nodes we use an approach developed by van Ham and Perer that dynamically extracts a subgraph based on an initial focal node and a degree‐of‐interest function. This approach is extended to support multiple focal nodes. With the symbolic visualization, potentially interesting regions of a graph may be represented with a very small visual footprint. We conclude the paper with an initial user study to evaluate the effectiveness of the signposts for navigation tasks.
Comparative Evaluation of an Interactive Time‐Series Visualization that Combines Quantitative Data with Qualitative Abstractions
Authors: Aigner W. , Rind A. , Hoffmann S. 
Published: June 2012

AbstractIn many application areas, analysts have to make sense of large volumes of multivariate time‐series data. Explorative analysis of this kind of data is often difficult and overwhelming at the level of raw data. Temporal data abstraction reduces data complexity by deriving qualitative statements that reflect domain‐specific key characteristics. Visual representations of abstractions and raw data together with appropriate interaction methods can support analysts in making their data easier to understand. Such a visualization technique that applies smooth semantic zooming has been developed in the context of patient data analysis. However, no empirical evidence on its effectiveness and efficiency is available. In this paper, we aim to fill this gap by reporting on a controlled experiment that compares this technique with another visualization method used in the well‐known KNAVE‐II framework. Both methods integrate quantitative data with qualitative abstractions whereas the first one uses a composite representation with color‐coding to display the qualitative data and spatial position coding for the quantitative data. The second technique uses juxtaposed representations for quantitative and qualitative data with spatial position coding for both. Results show that the test persons using the composite representation were generally faster, particularly for more complex tasks that involve quantitative values as well as qualitative abstractions.
A Qualitative Study on the Exploration of Temporal Changes in Flow Maps with Animation and Small‐Multiples
Authors: Boyandin Ilya , Bertini Enrico , Lalanne Denis 
Published: June 2012

AbstractWe present a qualitative user study analyzing findings made while exploring changes over time in spatial interactions. We analyzed findings made by the study participants with flow maps, one of the most popular representations of spatial interactions, using animation and small‐multiples as two alternative ways of representing temporal changes. Our goal was not to measure the subjects’ performance with the two views, but to find out whether there are qualitative differences between the types of findings users make with these two representations. To achieve this goal we performed a deep analysis of the collected findings, the interaction logs, and the subjective feedback from the users. We observed that with animation the subjects tended to make more findings concerning geographically local events and changes between subsequent years. With small‐multiples more findings concerning longer time periods were made. Besides, our results suggest that switching from one view to the other might lead to an increase in the numbers of findings of specific types made by the subjects which can be beneficial for certain tasks.
Conceptualizing Visual Uncertainty in Parallel Coordinates
Authors: Dasgupta Aritra , Chen Min , Kosara Robert 
Published: June 2012

AbstractUncertainty is an intrinsic part of any visual representation in visualization, no matter how precise the input data. Existing research on uncertainty in visualization mainly focuses on depicting data‐space uncertainty in a visual form. Uncertainty is thus often seen as a problem to deal with, in the data, and something to be avoided if possible. In this paper, we highlight the need for analyzing visual uncertainty in order to design more effective visual representations. We study various forms of uncertainty in the visual representation of parallel coordinates and propose a taxonomy for categorizing them. By building a taxonomy, we aim to identify different sources of uncertainty in the screen space and relate them to different effects of uncertainty upon the user. We examine the literature on parallel coordinates and apply our taxonomy to categorize various techniques for reducing uncertainty. In addition, we consider uncertainty from a different perspective by identifying cases where increasing certain forms of uncertainty may even be useful, with respect to task, data type and analysis scenario. This work suggests that uncertainty is a feature that can be both useful and problematic in visualization, and it is beneficial to augment an information visualization pipeline with a facility for visual uncertainty analysis.
Visualization of Global Correlation Structures in Uncertain 2D Scalar Fields
Authors: Pfaffelmoser Tobias , Westermann Rüdiger 
Published: June 2012

AbstractVisualizing correlations, i.e., the tendency of uncertain data values at different spatial positions to change contrarily or according to each other, allows inferring on the possible variations of structures in the data. Visualizing global correlation structures, however, is extremely challenging, since it is not clear how the visualization of complicated long‐range dependencies can be integrated into standard visualizations of spatial data. Furthermore, storing correlation information imposes a memory requirement that is quadratic in the number of spatial sample positions. This paper presents a novel approach for visualizing both positive and inverse global correlation structures in uncertain 2D scalar fields, where the uncertainty is modeled via a multivariate Gaussian distribution. We introduce a new measure for the degree of dependency of a random variable on its local and global surroundings, and we propose a spatial clustering approach based on this measure to classify regions of a particular correlation strength. The clustering performs a correlation filtering, which results in a representation that is only linear in the number of spatial sample points. Via cluster coloring the correlation information can be embedded into visualizations of other statistical quantities, such as the mean and the standard deviation. We finally propose a hierarchical cluster subdivision scheme to further allow for the simultaneous visualization of local and global correlations.
Vortex Analysis in Uncertain Vector Fields
Authors: Otto Mathias , Theisel Holger 
Published: June 2012

AbstractWe present an approach to extract and visualize vortex structures in uncertain vector fields. For this, we generalize the concepts of the most common vortex detectors to uncertain vector fields, namely the λ2‐criterion, Q‐criterion, and the concept of parallel vectors at the example of the method by Sujudi and Haimes. All these methods base on the computation of derivatives of the uncertain vector field which are uncertain fields as well. Since they generally cannot be computed in a closed form, we provide a Monte Carlo algorithm to compute the respective probability distributions. Based on this, uncertain versions of vortex regions and core structures are introduced. We present results of our approach on three real world data sets in order to give a proof of concept.
Probabilistic Local Features in Uncertain Vector Fields with Spatial Correlation
Authors: Petz Christoph , Pöthkow Kai , Hege Hans‐Christian 
Published: June 2012

AbstractIn this paper methods for extraction of local features in crisp vector fields are extended to uncertain fields. While in a crisp field local features are either present or absent at some location, in an uncertain field they are present with some probability. We model sampled uncertain vector fields by discrete Gaussian random fields with empirically estimated spatial correlations. The variability of the random fields in a spatial neighborhood is characterized by marginal distributions. Probabilities for the presence of local features are formulated in terms of low‐dimensional integrals over such marginal distributions. Specifically, we define probabilistic equivalents for critical points and vortex cores. The probabilities are computed by Monte Carlo integration. For identification of critical points and cores of swirling motion we employ the Poincaré index and the criterion by Sujudi and Haimes. In contrast to previous global methods we take a local perspective and directly extract features in divergence‐free fields as well. The method is able to detect saddle points in a straight forward way and works on various grid types. It is demonstrated by applying it to simulated unsteady flows of biofluid and climate dynamics.
Employing 2D Projections for Fast Visual Exploration of Large Fiber Tracking Data
Authors: Poco Jorge , Eler Danilo M. , Paulovich Fernando V. , Minghim Rosane 
Published: June 2012

AbstractFiber tracts detection is an increasingly common technology for diagnosis and also understanding of brain function. Although tools for tracing and presenting brain fibers are advanced, it is still difficult for physicians or students to explore the dataset in 3D due to their intricate topology. In this work we present a visual exploration approach for fiber tracts data aimed at supporting exploration of such data. The work employs a local, precise and fast 2D multidimensional projection technique that allows a large number of fibers to be handled simultaneously and to select groups of bundled fibers for further exploration. In this approach, a DTI feature dataset, including curvature as well as spatial features, is projected on a 2D or 3D view. By handling groups formed in this view, exploration is linked to corresponding brain fibers in object space. The link exists in both directions and fibers selected in object space are also mapped to feature space. Our approach also allows users to modify the projection, controlling and improving, if necessary, the definition of groups of fibers for small and large datasets, due to the local nature of the projection. Compared to other related work, the method presented here is faster for creating visual representations, making it possible to explore complete sets of fibers tracts up to 250K fibers, which was not possible previously. Additionally, the ability to change configuration of the feature space representation adds a high degree of flexibility to the process.
Biopsy Planner – Visual Analysis for Needle Pathway Planning in Deep Seated Brain Tumor Biopsy
Authors: Herghelegiu P. C. , Manta V. , Perin R. , Bruckner S. , Gröller E. 
Published: June 2012

AbstractBiopsies involve taking samples from living tissue using a biopsy needle. In current clinical practice they are a first mandatory step before any further medical actions are planned. Performing a biopsy on a deep seated brain tumor requires considerable time for establishing and validating the desired biopsy needle pathway to avoid damage. In this paper, we present a system for the visualization, analysis, and validation of biopsy needle pathways. Our system uses a multi‐level approach for identifying stable needle placements which minimize the risk of hitting blood vessels. This is one of the major dangers in this type of intervention. Our approach helps in identifying and visualizing the point on the pathway that is closest to a surrounding blood vessel, requiring a closer inspection by the neurosurgeon. An evaluation by medical experts is performed to demonstrate the utility of our system.
Nearly Recurrent Components in 3D Piecewise Constant Vector Fields
Authors: Szymczak Andrzej , Brunhart‐Lupo Nicholas 
Published: June 2012

AbstractWe present an algorithm for computing nearly recurrent components, that represent areas of close to circulating or stagnant flow, for 3D piecewise constant (PC) vector fields defined on regular grids. Using a number of analytical and simulated data sets, we demonstrate that nearly recurrent components can provide interesting insight into the topological structure of 3D vector fields.Our approach is based on prior work on Morse decompositions for PC vector fields on surfaces and extends concepts previously developed with this goal in mind to the case of 3D vector fields defined on regular grids. Our contributions include a description of trajectories of 3D piecewise constant vector fields and an extension of the transition graph, a finite directed graph that represents all trajectories, to the 3D case. Nearly recurrent components are defined by strongly connected components of the transition graph.
Interface Exchange as an Indicator for Eddy Heat Transport
Authors: Williams S. , Petersen M. , Hecht M. , Maltrud M. , Patchett J. , Ahrens J. , Hamann B. 
Published: June 2012

AbstractThe ocean contains many large‐scale, long‐lived vortices, called mesoscale eddies, that are believed to have a role in the transport and redistribution of salt, heat, and nutrients throughout the ocean. Determining this role, however, has proven to be a challenge, since the mechanics of eddies are only partly understood; a standard definition for these ocean eddies does not exist and, therefore, scientifically meaningful, robust methods for eddy extraction, characterization, tracking and visualization remain a challenge. To shed light on the nature and potential roles of eddies, we extend our previous work on eddy identification and tracking to construct a new metric to characterize the transfer of water into and out of eddies across their boundary, and produce several visualizations of this new metric to provide clues about the role eddies play in the global ocean.
Rolled‐out Wordles: A Heuristic Method for Overlap Removal of 2D Data Representatives
Authors: Strobelt H. , Spicker M. , Stoffel A. , Keim D. , Deussen O. 
Published: June 2012

AbstractWhen representing 2D data points with spacious objects such as labels, overlap can occur. We present a simple algorithm which modifies the (Mani‐) Wordle idea with scan‐line based techniques to allow a better placement. We give an introduction to common placement techniques from different fields and compare our method to these techniques w.r.t. euclidean displacement, changes in orthogonal ordering as well as shape and size preservation. Especially in dense scenarios our method preserves the overall shape better than known techniques and allows a good trade‐off between the other measures. Applications on real world data are given and discussed.
iVisClustering: An Interactive Visual Document Clustering via Topic Modeling
Authors: Lee Hanseung , Kihm Jaeyeon , Choo Jaegul , Stasko John , Park Haesun 
Published: June 2012

AbstractClustering plays an important role in many large‐scale data analyses providing users with an overall understanding of their data. Nonetheless, clustering is not an easy task due to noisy features and outliers existing in the data, and thus the clustering results obtained from automatic algorithms often do not make clear sense. To remedy this problem, automatic clustering should be complemented with interactive visualization strategies. This paper proposes an interactive visual analytics system for document clustering, called iVisClustering, based on a widely‐used topic modeling method, latent Dirichlet allocation (LDA). iVisClustering provides a summary of each cluster in terms of its most representative keywords and visualizes soft clustering results in parallel coordinates. The main view of the system provides a 2D plot that visualizes cluster similarities and the relation among data items with a graph‐based representation. iVisClustering provides several other views, which contain useful interaction methods. With help of these visualization modules, we can interactively refine the clustering results in various ways. Keywords can be adjusted so that they characterize each cluster better. In addition, our system can filter out noisy data and re‐cluster the data accordingly. Cluster hierarchy can be constructed using a tree structure and for this purpose, the system supports cluster‐level interactions such as sub‐clustering, removing unimportant clusters, merging the clusters that have similar meanings, and moving certain clusters to any other node in the tree structure. Furthermore, the system provides document‐level interactions such as moving mis‐clustered documents to another cluster and removing useless documents. Finally, we present how interactive clustering is performed via iVisClustering by using real‐world document data sets.
StratomeX: Visual Analysis of Large‐Scale Heterogeneous Genomics Data for Cancer Subtype Characterization
Authors: Lex A. , Streit M. , Schulz H.‐J. , Partl C. , Schmalstieg D. , Park P.J. , Gehlenborg N. 
Published: June 2012

AbstractIdentification and characterization of cancer subtypes are important areas of research that are based on the integrated analysis of multiple heterogeneous genomics datasets. Since there are no tools supporting this process, much of this work is done using ad‐hoc scripts and static plots, which is inefficient and limits visual exploration of the data. To address this, we have developed StratomeX, an integrative visualization tool that allows investigators to explore the relationships of candidate subtypes across multiple genomic data types such as gene expression, DNA methylation, or copy number data. StratomeX represents datasets as columns and subtypes as bricks in these columns. Ribbons between the columns connect bricks to show subtype relationships across datasets. Drill‐down features enable detailed exploration. StratomeX provides insights into the functional and clinical implications of candidate subtypes by employing small multiples, which allow investigators to assess the effect of subtypes on molecular pathways or outcomes such as patient survival. As the configuration of viewing parameters in such a multi‐dataset, multi‐view scenario is complex, we propose a meta visualization and configuration interface for dataset dependencies and data‐view relationships. StratomeX is developed in close collaboration with domain experts. We describe case studies that illustrate how investigators used the tool to explore subtypes in large datasets and demonstrate how they efficiently replicated findings from the literature and gained new insights into the data.