Enabling decision trend analysis with interactive scatter plot matrices visualization

Wang, Wen Bo; Huang, Mao Lin; Nguyễn, Quang Vinh; Huang, Weidong; Zhang, Kang; Huang, Tze-Haw

doi:10.1016/j.jvlc.2015.11.002

Cited by 7 publications

(10 citation statements)

References 25 publications

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“…The bubble charts in Figure 47b dynamically present the user-clustering results calculated by selected topic model as smooth transitions [125]. Figure 48a shows all the pairwise scatter plots of attributes on a single view, with multiple scatter plots in a matrix format [126], as the result of dimensionality reduction. However, Multiclass SPLOMs also have problems with over-plotting, and most existing techniques to counter this problem only focus on individual scatter plots with a single class.…”

Section: Bubble Chartsmentioning

confidence: 99%

“…An animated SPLOM by Chen et al [127] that uses a flicker-based animation exhibits better performance in identifying dense areas and is even more easily interpreted and more powerful than static scatter matrices. Figure 48b shows this animated approach on several typical multiclass and multivariable datasets of different scales (accessed from Figure 48a shows all the pairwise scatter plots of attributes on a single view, with multiple scatter plots in a matrix format [126], as the result of dimensionality reduction. However, Multiclass SPLOMs also have problems with over-plotting, and most existing techniques to counter this problem only focus on individual scatter plots with a single class.…”

Section: Bubble Chartsmentioning

confidence: 99%

“…SPLOM [122], proposed by Elmqvist et al from INRIA, France, is derived from the idea of scatter approach in the domain of multidimensional data visualization. The scatter plot matrix in Figure 48a shows all the pairwise scatter plots of attributes on a single view, with multiple scatter plots in a matrix format [126], as the result of dimensionality reduction. However, Multiclass SPLOMs also have problems with over-plotting, and most existing techniques to counter this problem only focus on individual scatter plots with a single class.…”

Section: Scatter Plot Matrix (Splom) and Dynamically Interactive Visumentioning

confidence: 99%

“…SPLOM visualization examples: (a) exploration of a seven-dimensional car dataset[126]; and (b) system interface of the flicker-based animated SPLOM[127].…”

mentioning

confidence: 99%

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Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Chen

et al. 2019

IJGI

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Trajectory big data have significant applications in many areas, such as traffic management, urban planning and military reconnaissance. Traditional visualization methods, which are represented by contour maps, shading maps and hypsometric maps, are mainly based on the spatiotemporal information of trajectories, which can macroscopically study the spatiotemporal conditions of the entire trajectory set and microscopically analyze the individual movement of each trajectory; such methods are widely used in screen display and flat mapping. With the improvement of trajectory data quality, these data can generally describe information in the spatial and temporal dimensions and involve many other attributes (e.g., speed, orientation, and elevation) with large data amounts and high dimensions. Additionally, these data have relatively complicated internal relationships and regularities, whose analysis could cause many troubles; the traditional approaches can no longer fully meet the requirements of visualizing trajectory data and mining hidden information. Therefore, diverse visualization methods that present the value of massive trajectory information are currently a hot research topic. This paper summarizes the research status of trajectory data-visualization techniques in recent years and extracts common contemporary trajectory data-visualization methods to comprehensively cognize and understand the fundamental characteristics and diverse achievements of trajectory-data visualization.First, we should fully understand the conceptual distinction between "multidimensional" and "multivariate". Generally, "multidimensional" is used to describe the dimensionality of independent variables in physical space and concentrates on expressing spatial and temporal concepts, while "multivariate" is used for attribute-count description that indicates the attribute amount involved in the data. In fact, trajectory data are typically enormous, high-dimensional, spatiotemporal vector attribute data. We are interested not only in comprehending dimensional information for spatiotemporal-element depictions, but also in the visualization research with attribute-feature expression. Starting in Section 2, we present relevant visualization techniques and methods that function with multisource trajectory data. The chosen examples are for illustration and the list is not exhaustive-we only focus on small but representative choices. Universal Multivariate VisualizationThe trajectory visualization reflects the changing patterns of time-varying geolocations and corresponding attribute information, including basic-dimensional information and the attached thematic variables of space-time objects. However, these variables usually do not exist independently but are combined with each other, and the trajectory data of different sources, scales, or types are plotted in a hybrid manner. We separately list visualization techniques that integrate multiple attributes. As a powerful and portable carrier that encodes abundant information in methods of visual cha...

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Section: Bubble Chartsmentioning

confidence: 99%

Section: Bubble Chartsmentioning

confidence: 99%

Section: Scatter Plot Matrix (Splom) and Dynamically Interactive Visumentioning

confidence: 99%

“…SPLOM visualization examples: (a) exploration of a seven-dimensional car dataset[126]; and (b) system interface of the flicker-based animated SPLOM[127].…”

mentioning

confidence: 99%

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Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Chen

et al. 2019

IJGI

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“…Another classical example is that of sequence alignment. Generally-speaking, computer vision applications are emerging trends for such a context, and, recently, the research community has devoted a lot of attention to this topic (e.g., [2,3,4,5,6,7,8,9,10,11]). DP has been applied to various tasks in pattern recognition and computer vision [12,13].…”

Section: Introductionmentioning

confidence: 99%

An effective and efficient approximate two-dimensional dynamic programming algorithm for supporting advanced computer vision applications

Cuzzocrea

Mumolo

Grasso

et al. 2017

Journal of Visual Languages & Computing

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Dynamic programming is a popular optimization technique, developed in the 60's and still widely used today in several fields for its ability to find global optimum. Dynamic Programming Algorithms (DPAs) can be developed in many dimension. However, it is known that if the DPA dimension is greater or equal to two, the algorithm is an NP complete problem. In this paper we present an approximation of the fully two-dimensional DPA (2D-DPA) with polynomial complexity. Then, we describe an implementation of the algorithm on a recent parallel device based on CUDA architecture. We show that our parallel implementation presents a speed-up of about 25 with respect to a sequential implementation on an Intel I7 CPU. In particular, our system allows a speed of about ten 2D-DPA executions per second for 85 × 85 pixels images. Experiments and case studies support our thesis.

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Towards a visual guide for communicating uncertainty in Visual Analytics

Seipp

Gutiérrez

Ochôa

et al. 2019

Journal of Computer Languages

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This article presents a first step towards the definition of a visual guide for communicating uncertainty which is to fit into existing visualisation frameworks and toolkits. The first entry in our guide is made by a set of visual variables appropriate for representing areal uncertainty in algorithm mechanics. Such visualisations show users how data points are distributed in the classification space and allow them to understand the "goodness-of-fit" of their data to the algorithm. This is important for Visual Analytics applications, which combine Information Visualisation with information mining techniques in an interactive decision-making process. Model uncertainties stemming from widely spread data points need to be visualised so that the user can make adjustments and improve the analysis. To capitalise on established knowledge and meaning, we explore whether popular visual variables for representing areal uncertainty in the domain of geospatial visualisation may also be effective for representing uncertainty in the visualisation of the mechanics of K-means clustering and Linear Regression algorithms, as both use a spatial distribution of data points. In a study with 500 participants we find that overall the visual means opacity performs best, followed by texture, but that grid and blur may be unsuitable for quantifying uncertainty. The performance of contour lines appears to depend on the algorithm visualisation. Using this study, we extend the validity of a set of domain-specific findings from geospatial visualisation to the visualisation of algorithm mechanics and use these to form the first building blocks of a cross-disciplinary visual guide for representing uncertainty, laying promising foundations for future work.

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Enabling decision trend analysis with interactive scatter plot matrices visualization

Cited by 7 publications

References 25 publications

Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

Diverse Visualization Techniques and Methods of Moving-Object-Trajectory Data: A Review

An effective and efficient approximate two-dimensional dynamic programming algorithm for supporting advanced computer vision applications

Towards a visual guide for communicating uncertainty in Visual Analytics

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