Visual Parameter Space Analysis: A Conceptual Framework

Predicting the Interactive Rendering Time Threshold of Gaussian Process Models With HyperSlice

In this paper we present a method for predicting the rendering time to display multi-dimensional data for the analysis of computer simulations using the HyperSlice [36] method with Gaussian process model reconstruction. Our method relies on a theoretical understanding of how the data points are drawn on slices and then fits the formula to a user's machine using practical experiments. We also describe the typical characteristics of data when analyzing deterministic computer simulations as described by the statistics community. We then show the advantage of carefully considering how many data points can be drawn in real time by proposing two approaches of how this predictive formula can be used in a real-world system.

Vis-A-Ware: Integrating Spatial and Non-Spatial Visualization for Visibility-Aware Urban Planning

3D visibility analysis plays a key role in urban planning for assessing the visual impact of proposed buildings on the cityscape. A call for proposals typically yields around 30 candidate buildings that need to be evaluated with respect to selected viewpoints. Current visibility analysis methods are very time-consuming and limited to a small number of viewpoints. Further, analysts neither have measures to evaluate candidates quantitatively, nor to compare them efficiently. The primary contribution of this work is the design study of Vis-A-Ware, a visualization system to qualitatively and quantitatively evaluate, rank, and compare visibility data of candidate buildings with respect to a large number of viewpoints. Vis-A-Ware features a 3D spatial view of an urban scene and non-spatial views of data derived from visibility evaluations, which are tightly integrated by linked interaction. To enable a quantitative evaluation we developed four metrics in accordance with experts from urban planning. We illustrate the applicability of Vis-A-Ware on the basis of a use case scenario and present results from informal feedback sessions with domain experts from urban planning and development. This feedback suggests that Vis-A-Ware is a valuable tool for visibility analysis allowing analysts to answer complex questions more efficiently and objectively.

Multi-Resolution Climate Ensemble Parameter Analysis with Nested Parallel Coordinates Plots

Due to the uncertain nature of weather prediction, climate simulations are usually performed multiple times with different spatial resolutions. The outputs of simulations are multi-resolution spatial temporal ensembles. Each simulation run uses a unique set of values for multiple convective parameters. Distinct parameter settings from different simulation runs in different resolutions constitute a multi-resolution high-dimensional parameter space. Understanding the correlation between the different convective parameters, and establishing a connection between the parameter settings and the ensemble outputs are crucial to domain scientists. The multi-resolution high-dimensional parameter space, however, presents a unique challenge to the existing correlation visualization techniques. We present Nested Parallel Coordinates Plot (NPCP), a new type of parallel coordinates plots that enables visualization of intra-resolution and inter-resolution parameter correlations. With flexible user control, NPCP integrates superimposition, juxtaposition and explicit encodings in a single view for comparative data visualization and analysis. We develop an integrated visual analytics system to help domain scientists understand the connection between multi-resolution convective parameters and the large spatial temporal ensembles. Our system presents intricate climate ensembles with a comprehensive overview and on-demand geographic details. We demonstrate NPCP, along with the climate ensemble visualization system, based on real-world use-cases from our collaborators in computational and predictive science.

A Fractional Cartesian Composition Model for Semi-Spatial Comparative Visualization Design

The study of spatial data ensembles leads to substantial visualization challenges in a variety of applications. In this paper, we present a model for comparative visualization that supports the design of according ensemble visualization solutions by partial automation. We focus on applications, where the user is interested in preserving selected spatial data characteristics of the data as much as possible-even when many ensemble members should be jointly studied using comparative visualization. In our model, we separate the design challenge into a minimal set of user-specified parameters and an optimization component for the automatic configuration of the remaining design variables. We provide an illustrated formal description of our model and exemplify our approach in the context of several application examples from different domains in order to demonstrate its generality within the class of comparative visualization problems for spatial data ensembles.

What do Constraint Programming Users Want to See? Exploring the Role of Visualisation in Profiling of Models and Search

Constraint programming allows difficult combinatorial problems to be modelled declaratively and solved automatically. Advances in solver technologies over recent years have allowed the successful use of constraint programming in many application areas. However, when a particular solver's search for a solution takes too long, the complexity of the constraint program execution hinders the programmer's ability to profile that search and understand how it relates to their model. Therefore, effective tools to support such profiling and allow users of constraint programming technologies to refine their model or experiment with different search parameters are essential. This paper details the first user-centred design process for visual profiling tools in this domain. We report on: our insights and opportunities identified through an on-line questionnaire and a creativity workshop with domain experts carried out to elicit requirements for analytical and visual profiling techniques; our designs and functional prototypes realising such techniques; and case studies demonstrating how these techniques shed light on the behaviour of the solvers in practice.

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a "human in the loop" process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities.

WeightLifter: Visual Weight Space Exploration for Multi-Criteria Decision Making

A common strategy in Multi-Criteria Decision Making (MCDM) is to rank alternative solutions by weighted summary scores. Weights, however, are often abstract to the decision maker and can only be set by vague intuition. While previous work supports a point-wise exploration of weight spaces, we argue that MCDM can benefit from a regional and global visual analysis of weight spaces. Our main contribution is WeightLifter, a novel interactive visualization technique for weight-based MCDM that facilitates the exploration of weight spaces with up to ten criteria. Our technique enables users to better understand the sensitivity of a decision to changes of weights, to efficiently localize weight regions where a given solution ranks high, and to filter out solutions which do not rank high enough for any plausible combination of weights. We provide a comprehensive requirement analysis for weight-based MCDM and describe an interactive workflow that meets these requirements. For evaluation, we describe a usage scenario of WeightLifter in automotive engineering and report qualitative feedback from users of a deployed version as well as preliminary feedback from decision makers in multiple domains. This feedback confirms that WeightLifter increases both the efficiency of weight-based MCDM and the awareness of uncertainty in the ultimate decisions.

Visual Trends Analysis in Time-Varying Ensembles

Visualization and analysis techniques play a key role in the discovery of relevant features in ensemble data. Trends, in the form of persisting commonalities or differences in time-varying ensemble datasets, constitute one of the most expressive feature types in ensemble analysis. We develop a flow-graph representation as the core of a system designed for the visual analysis of trends in time-varying ensembles. In our interactive analysis framework, this graph is linked to a representation of ensemble parameter-space and the ensemble itself. This facilitates a detailed examination of trends and their correlations to properties of input-space. We demonstrate the utility of the proposed trends analysis framework in several benchmark data sets, highlighting its capability to support goal-driven design of time-varying simulations.

Visual analytics for concept exploration in subspaces of patient groups : Making sense of complex datasets with the Doctor-in-the-loop

Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.

Visualizing Multiple Variables Across Scale and Geography

Comparing multiple variables to select those that effectively characterize complex entities is important in a wide variety of domains - geodemographics for example. Identifying variables that correlate is a common practice to remove redundancy, but correlation varies across space, with scale and over time, and the frequently used global statistics hide potentially important differentiating local variation. For more comprehensive and robust insights into multivariate relations, these local correlations need to be assessed through various means of defining locality. We explore the geography of this issue, and use novel interactive visualization to identify interdependencies in multivariate data sets to support geographically informed multivariate analysis. We offer terminology for considering scale and locality, visual techniques for establishing the effects of scale on correlation and a theoretical framework through which variation in geographic correlation with scale and locality are addressed explicitly. Prototype software demonstrates how these contributions act together. These techniques enable multiple variables and their geographic characteristics to be considered concurrently as we extend visual parameter space analysis (vPSA) to the spatial domain. We find variable correlations to be sensitive to scale and geography to varying degrees in the context of energy-based geodemographics. This sensitivity depends upon the calculation of locality as well as the geographical and statistical structure of the variable.

LiteVis: Integrated Visualization for Simulation-Based Decision Support in Lighting Design

State-of-the-art lighting design is based on physically accurate lighting simulations of scenes such as offices. The simulation results support lighting designers in the creation of lighting configurations, which must meet contradicting customer objectives regarding quality and price while conforming to industry standards. However, current tools for lighting design impede rapid feedback cycles. On the one side, they decouple analysis and simulation specification. On the other side, they lack capabilities for a detailed comparison of multiple configurations. The primary contribution of this paper is a design study of LiteVis, a system for efficient decision support in lighting design. LiteVis tightly integrates global illumination-based lighting simulation, a spatial representation of the scene, and non-spatial visualizations of parameters and result indicators. This enables an efficient iterative cycle of simulation parametrization and analysis. Specifically, a novel visualization supports decision making by ranking simulated lighting configurations with regard to a weight-based prioritization of objectives that considers both spatial and non-spatial characteristics. In the spatial domain, novel concepts support a detailed comparison of illumination scenarios. We demonstrate LiteVis using a real-world use case and report qualitative feedback of lighting designers. This feedback indicates that LiteVis successfully supports lighting designers to achieve key tasks more efficiently and with greater certainty.

3D Regression Heat Map Analysis of Population Study Data

Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease.

Visual parameter optimisation for biomedical image processing

Background

Biomedical image processing methods require users to optimise input parameters to ensure high-quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output.

Results

We present a visualisation method that transforms users' ability to understand algorithm behaviour by integrating input and output, and by supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm.

Conclusions

The visualisation method presented here provides analysis capability for multiple inputs and outputs in biomedical image processing that is not supported by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches.

Opening the Black Box: Strategies for Increased User Involvement in Existing Algorithm Implementations

An increasing number of interactive visualization tools stress the integration with computational software like MATLAB and R to access a variety of proven algorithms. In many cases, however, the algorithms are used as black boxes that run to completion in isolation which contradicts the needs of interactive data exploration. This paper structures, formalizes, and discusses possibilities to enable user involvement in ongoing computations. Based on a structured characterization of needs regarding intermediate feedback and control, the main contribution is a formalization and comparison of strategies for achieving user involvement for algorithms with different characteristics. In the context of integration, we describe considerations for implementing these strategies either as part of the visualization tool or as part of the algorithm, and we identify requirements and guidelines for the design of algorithmic APIs. To assess the practical applicability, we provide a survey of frequently used algorithm implementations within R regarding the fulfillment of these guidelines. While echoing previous calls for analysis modules which support data exploration more directly, we conclude that a range of pragmatic options for enabling user involvement in ongoing computations exists on both the visualization and algorithm side and should be used.