Adaptive privacy-preserving visualization using parallel coordinates

Current information visualization techniques assume unrestricted access to data. However, privacy protection is a key issue for a lot of real-world data analyses. Corporate data, medical records, etc. are rich in analytical value but cannot be shared without first going through a transformation step where explicit identifiers are removed and the data is sanitized. Researchers in the field of data mining have proposed different techniques over the years for privacy-preserving data publishing and subsequent mining techniques on such sanitized data. A well-known drawback in these methods is that for even a small guarantee of privacy, the utility of the datasets is greatly reduced. In this paper, we propose an adaptive technique for privacy preservation in parallel coordinates. Based on knowledge about the sensitivity of the data, we compute a clustered representation on the fly, which allows the user to explore the data without breaching privacy. Through the use of screen-space privacy metrics, the technique adapts to the user's screen parameters and interaction. We demonstrate our method in a case study and discuss potential attack scenarios.     

Brushing dimensions--a dual visual analysis model for high-dimensional data

In many application fields, data analysts have to deal with datasets that contain many expressions per item. The effective analysis of such multivariate datasets is dependent on the user's ability to understand both the intrinsic dimensionality of the dataset as well as the distribution of the dependent values with respect to the dimensions. In this paper, we propose a visualization model that enables the joint interactive visual analysis of multivariate datasets with respect to their dimensions as well as with respect to the actual data values. We describe a dual setting of visualization and interaction in items space and in dimensions space. The visualization of items is linked to the visualization of dimensions with brushing and focus+context visualization. With this approach, the user is able to jointly study the structure of the dimensions space as well as the distribution of data items with respect to the dimensions. Even though the proposed visualization model is general, we demonstrate its application in the context of a DNA microarray data analysis.     

A view-dependent spatiotemporal saliency-driven approach for time varying volumetric data in geovisualization

Geospatial datasets from satellite observations and model simulations are becoming more accessible. These spatiotemporal datasets are relatively massive for visualization to support advanced analysis and decision making. A challenge to visualizing massive geospatial datasets is identifying critical spatial and temporal changes reflected in the data while maintaining high interactive rendering speed, even when data are accessed remotely. We propose a view-dependent spatiotemporal saliency-driven approach that facilitates the discovery of regions showing high levels of spatiotemporal variability and reduces the rendering intensity of interactive visualization. Our method is based on a novel definition of data saliency, a spatiotemporal tree structure to store visual saliency values, as well as a saliency-driven view-dependent level-of-detail (LOD) control. To demonstrate its applicability, we have implemented the approach with an open-source remote visualization package and conducted experiments with spatiotemporal datasets produced by a regional dust storm simulation model. The results show that the proposed method may not be outstanding in some specific situations, but it consistently performs very well across different settings according to different criteria.     

Fast, memory-efficient cell location in unstructured grids for visualization

Applying certain visualization techniques to datasets described on unstructured grids requires the interpolation of variables of interest at arbitrary locations within the dataset's domain of definition. Typical solutions to the problem of finding the grid element enclosing a given interpolation point make use of a variety of spatial subdivision schemes. However, existing solutions are memory- intensive, do not scale well to large grids, or do not work reliably on grids describing complex geometries. In this paper, we propose a data structure and associated construction algorithm for fast cell location in unstructured grids, and apply it to the interpolation problem. Based on the concept of bounding interval hierarchies, the proposed approach is memory-efficient, fast and numerically robust. We examine the performance characteristics of the proposed approach and compare it to existing approaches using a number of benchmark problems related to vector field visualization. Furthermore, we demonstrate that our approach can successfully accommodate large datasets, and discuss application to visualization on both CPUs and GPUs.     

Visualizing High‐Dimensional Structures by Dimension Ordering and Filtering using Subspace Analysis

High‐dimensional data visualization is receiving increasing interest because of the growing abundance of high‐dimensional datasets. To understand such datasets, visualization of the structures present in the data, such as clusters, can be an invaluable tool. Structures may be present in the full high‐dimensional space, as well as in its subspaces. Two widely used methods to visualize high‐dimensional data are the scatter plot matrix (SPM) and the parallel coordinate plot (PCP). SPM allows a quick overview of the structures present in pairwise combinations of dimensions. On the other hand, PCP has the potential to visualize not only bi‐dimensional structures but also higher dimensional ones. A problem with SPM is that it suffers from crowding and clutter which makes interpretation hard. Approaches to reduce clutter are available in the literature, based on changing the order of the dimensions. However, usually this reordering has a high computational complexity. For effective visualization of high‐dimensional structures, also PCP requires a proper ordering of the dimensions. In this paper, we propose methods for reordering dimensions in PCP in such a way that high‐dimensional structures (if present) become easier to perceive. We also present a method for dimension reordering in SPM which yields results that are comparable to those of existing approaches, but at a much lower computational cost. Our approach is based on finding relevant subspaces for clustering using a quality criterion and cluster information. The quality computation and cluster detection are done in image space, using connected morphological operators. We demonstrate the potential of our approach for synthetic and astronomical datasets, and show that our method compares favorably with a number of existing approaches.     

平行散点图:基于GPU的可视化分析方法

提出一种分析多维数据集之间关系的信息可视化方法--平行散点图.结合平行坐标、散点图方法,综合了焦点 背景、多视角、多视图、刷子等交互与可视化策略,使人有效地观察与分析多维数据集之间的连接关系;利用统一渲染GPU对粒子、线段、公告牌等的绘制与输出功能,在千万级数据集上达到较强的深度感与交互级的绘制性能;提出GPU上基于空间填充曲线的聚类算法,可交互式地降低连接线的视觉杂乱度;将连接、聚类与可视化整合为一个基于GPU的系统,在千万级数据集上达到交互级的可视化分析.     

Interactive visual analysis of set-typed data

While it is quite typical to deal with attributes of different data types in the visualization of heterogeneous and multivariate datasets, most existing techniques still focus on the most usual data types such as numerical attributes or strings. In this paper we present a new approach to the interactive visual exploration and analysis of data that contains attributes which are of set type. A set-typed attribute of a data item--like one cell in a table--has a list of n > or = 0 elements as its value. We present the set'o'gram as a new visualization approach to represent data of set type and to enable interactive visual exploration and analysis. We also demonstrate how this approach is capable to help in dealing with datasets that have a larger number of dimensions (more than a dozen or more), especially also in the context of categorical data. To illustrate the effectiveness of our approach, we present the interactive visual analysis of a CRM dataset with data from a questionnaire on the education and shopping habits of about 90000 people.     

Visualization and inference based on wavelet coefficients, SiZer and SiNos

SiZer (SIgnificant ZERo crossing of the derivatives) and SiNos (SIgnificant NOn-Stationarities) are scale-space based visualization tools for statistical inference. They are used to discover meaningful structure in data through exploratory analysis involving statistical smoothing techniques. Wavelet methods have been successfully used to analyze various types of time series. In this paper, we propose a new time series analysis approach, which combines the wavelet analysis with the visualization tools SiZer and SiNos. We use certain functions of wavelet coefficients at different scales as inputs, and then apply SiZer or SiNos to highlight potential non-stationarities. We show that this new methodology can reveal hidden local non-stationary behavior of time series, that are otherwise difficult to detect.     

Survey of glyph-based visualization techniques for spatial multivariate medical data

In this survey article, we review glyph-based visualization techniques that have been exploited when visualizing spatial multivariate medical data. To classify these techniques, we derive a taxonomy of glyph properties that is based on classification concepts established in information visualization. Considering both the glyph visualization as well as the interaction techniques that are employed to generate or explore the glyph visualization, we are able to classify glyph techniques into two main groups: those supporting pre-attentive and those supporting attentive processing. With respect to this classification, we review glyph-based techniques described in the medical visualization literature. Based on the outcome of the literature review, we propose design guidelines for glyph visualizations in the medical domain.     

Interactive focus + context medical data exploration and editing

Volumetric datasets are increasingly used in medical applications. In many of these applications, visualization and interaction is generally performed on cross‐sectional two‐dimensional (2D) views of three‐dimensional (3D) imaging modalities. Displaying 3D volumetric medical datasets on traditional 2D screens can present problems such as occlusion and information overload, especially when multiple data sources are present. Displaying desired information while showing the relationship to the rest of the dataset(s) can be challenging. In this paper, we present an interactive focus + context visualization approach that uses the volumetric Magic Lens interaction paradigm. We propose to use the Magic Lens as a volumetric brush to perform volume editing tasks, therefore combining data exploration with volumetric editing. Polygon‐assisted ray casting methods are used for real‐time rendering and editing frame rates, while providing compact storage of editing states for undo/redo operations. We discuss the application of our methods to radiation therapy, which is an important cancer treatment modality. We envision that this approach will improve the treatment planning process by improving the therapists' understanding of information from various sources and will help identify if the alignment of the patient in the treatment room coincides with the prepared treatment plan. Copyright © 2013 John Wiley & Sons, Ltd.     

Quantitative Evaluation of Time-Dependent Multidimensional Projection Techniques

Dimensionality reduction methods are an essential tool for multidimensional data analysis, and many interesting processes can be studied as time-dependent multivariate datasets. There are, however, few studies and proposals that leverage on the concise power of expression of projections in the context of dynamic/temporal data. In this paper, we aim at providing an approach to assess projection techniques for dynamic data and understand the relationship between visual quality and stability. Our approach relies on an experimental setup that consists of existing techniques designed for time-dependent data and new variations of static methods. To support the evaluation of these techniques, we provide a collection of datasets that has a wide variety of traits that encode dynamic patterns, as well as a set of spatial and temporal stability metrics that assess the quality of the layouts. We present an evaluation of 9 methods, 10 datasets, and 12 quality metrics, and elect the best-suited methods for projecting time-dependent multivariate data, exploring the design choices and characteristics of each method. Additional results can be found in the online benchmark repository. We designed our evaluation pipeline and benchmark specifically to be a live resource, open to all researchers who can further add their favorite datasets and techniques at any point in the future.     

Quality metrics in high-dimensional data visualization: an overview and systematization

In this paper, we present a systematization of techniques that use quality metrics to help in the visual exploration of meaningful patterns in high-dimensional data. In a number of recent papers, different quality metrics are proposed to automate the demanding search through large spaces of alternative visualizations (e.g., alternative projections or ordering), allowing the user to concentrate on the most promising visualizations suggested by the quality metrics. Over the last decade, this approach has witnessed a remarkable development but few reflections exist on how these methods are related to each other and how the approach can be developed further. For this purpose, we provide an overview of approaches that use quality metrics in high-dimensional data visualization and propose a systematization based on a thorough literature review. We carefully analyze the papers and derive a set of factors for discriminating the quality metrics, visualization techniques, and the process itself. The process is described through a reworked version of the well-known information visualization pipeline. We demonstrate the usefulness of our model by applying it to several existing approaches that use quality metrics, and we provide reflections on implications of our model for future research.     

Human Activity Recognition Based on Parallel Approximation Kernel K-Means Algorithm

Recently, owing to the capability of mobile and wearable devices to sense daily human activity, human activity recognition (HAR) datasets have become a large-scale data resource. Due to the heterogeneity and nonlinearly separable nature of the data recorded by these sensors, the datasets generated require special techniques to accurately predict human activity and mitigate the considerable heterogeneity. Consequently, classic clustering algorithms do not work well with these data. Hence, kernelization, which converts the data into a new feature vector representation, is performed on nonlinearly separable data. This study aims to present a robust method to perform HAR data clustering to mitigate heterogeneity in data with minimal resource consumption. Therefore, we propose a parallel approximated clustering approach to handle the computational cost of big data by addressing noise, heterogeneity, and nonlinearity in data using data reduction, filtering, and approximated clustering methods on parallel computing environments that have not been previously addressed. Our key contribution is to treat HAR as big data implemented by approximation kernel K-means approaches and fill the gap between the HAR clustering cost and parallel computing fields. We implemented our approach on Google cloud on a parallel spark cluster, which helped us to process large-scale HAR data across multiple machines of clusters. The normalized mutual information is used as validation metric to assess the quality of the clustering algorithm. Additionally, the precision, recall, f-score metrics values are obtained somehow to compare the results with a classification technique. The experimental results of our clustering approach prove its effectiveness compared with a classification technique and can efficiently detect physical activity and mitigate the heterogeneity of the datasets.     

Semisupervised visualization of high-dimensional data

High-dimensional data visualization is a more complex process than the ordinary dimensionality reduction to two or three dimensions. Therefore, we propose and evaluate a novel four-step visualization approach that is built upon the combination of three components: metric learning, intrinsic dimensionality estimation, and feature extraction. Although many successful applications of dimensionality reduction techniques for visualization are known, we believe that the sophisticated nature of high-dimensional data often needs a combination of several machine learning methods to solve the task. Here, this is provided by a novel framework and experiments with real-world data.     

Flowstrates: An Approach for Visual Exploration of Temporal Origin‐Destination Data

Many origin‐destination datasets have become available in the recent years, e.g. flows of people, animals, money, material, or network traffic between pairs of locations, but appropriate techniques for their exploration still have to be developed. Especially, supporting the analysis of datasets with a temporal dimension remains a significant challenge. Many techniques for the exploration of spatio‐temporal data have been developed, but they prove to be only of limited use when applied to temporal origin‐destination datasets. We present Flowstrates , a new interactive visualization approach in which the origins and the destinations of the flows are displayed in two separate maps, and the changes over time of the flow magnitudes are represented in a separate heatmap view in the middle. This allows the users to perform spatial visual queries, focusing on different regions of interest for the origins and destinations, and to analyze the changes over time provided with the means of flow ordering, filtering and aggregation in the heatmap. In this paper, we discuss the challenges associated with the visualization of temporal origin‐destination data, introduce our solution, and present several usage scenarios showing how the tool we have developed supports them.     

A dimensionality reduction algorithm and its application for interactive visualization

Visualization is one of the most effective methods for analyzing how high-dimensional data are distributed. Dimensionality reduction techniques, such as PCA, can be used to map high dimensional data to a two- or three-dimensional space. In this paper, we propose an algorithm called HyperMap that can be effectively applied to visualization. Our algorithm can be seen as a generalization of FastMap. It preserves its linear computation complexity, and overcomes several main shortcomings, especially in visualization. Since there are more than two pivot objects in each axis of a target space, more distance information needs to be preserved in each dimension. Then in visualization, the number of pivot objects can go beyond the limitation of six (2-pivot objects × 3-dimensions). Our HyperMap algorithm also gives more flexibility to the target space, such that the data distribution can be observed from various viewpoints. Its effectiveness is confirmed by empirical evaluations on both real and synthetic datasets.     

Mining latent patterns in geoMobile data via EPIC

We coin the term geoMobile data to emphasize datasets that exhibit geo-spatial features reflective of human behaviors. We propose and develop an EPIC framework to mine latent patterns from geoMobile data and provide meaningful interpretations: we first ‘E’xtract latent features from high dimensional geoMobile datasets via Laplacian Eigenmaps and perform clustering in this latent feature space; we then use a state-of-the-art visualization technique to ‘P’roject these latent features into 2D space; and finally we obtain meaningful ‘I’nterpretations by ‘C’ulling cluster-specific significant feature-set. We illustrate that the local space contraction property of our approach is most superior than other major dimension reduction techniques. Using diverse real-world geoMobile datasets, we show the efficacy of our framework via three case studies.

     

Selective Visualization of Vector Fields

In this paper, we present an approach to selective vector field visualization. This selective visualization approach consists of three stages: selectdon creation, selection processing and selective visualization mapping. It is described how selected regions, called selections, can be represented and created, how selections can be processed and how they can be used in the visualization mapping. Combination of these techniques with a standard visualization pipeline improves the visualization process and offers new facilities for visualization. Examples of selective visualization of fluid flow datasets are provided.     

Particle-based non-photorealistic volume visualization

Non-photorealistic techniques are usually applied to produce stylistic renderings. In visualization, these techniques are often able to simplify data, producing clearer images than traditional visualization methods. We investigate the use of particle systems for visualizing volume datasets using non-photorealistic techniques. In our VolumeFlies framework, user-selectable rules affect particles to produce a variety of illustrative styles in a unified way. The techniques presented do not require the generation of explicit intermediary surfaces.     

The FLOWLENS: a focus-and-context visualization approach for exploration of blood flow in cerebral aneurysms

Blood flow and derived data are essential to investigate the initiation and progression of cerebral aneurysms as well as their risk of rupture. An effective visual exploration of several hemodynamic attributes like the wall shear stress (WSS) and the inflow jet is necessary to understand the hemodynamics. Moreover, the correlation between focus-and-context attributes is of particular interest. An expressive visualization of these attributes and anatomic information requires appropriate visualization techniques to minimize visual clutter and occlusions. We present the FLOWLENS as a focus-and-context approach that addresses these requirements. We group relevant hemodynamic attributes to pairs of focus-and-context attributes and assign them to different anatomic scopes. For each scope, we propose several FLOWLENS visualization templates to provide a flexible visual filtering of the involved hemodynamic pairs. A template consists of the visualization of the focus attribute and the additional depiction of the context attribute inside the lens. Furthermore, the FLOWLENS supports local probing and the exploration of attribute changes over time. The FLOWLENS minimizes visual cluttering, occlusions, and provides a flexible exploration of a region of interest. We have applied our approach to seven representative datasets, including steady and unsteady flow data from CFD simulations and 4D PC-MRI measurements. Informal user interviews with three domain experts confirm the usefulness of our approach.