Exploring the Design Space of BioFabric Visualization for Multivariate Network Analysis

The curse of dimensionality, i.e., the fact that feature spaces of increasing dimensionality with finite sample sizes tend to be empty, has given incentive to a plethora of research activities in various disciplines and diverse application fields, e.g., statistics or neural networks. Three major application fields are multivariate data classification, data analysis and data visualization. In this contribution, methods for dimensionality reduction from three decades of interdisciplinary research are browsed and their applicability in the above application domains is briefly discussed. Complementing techniques for ensuing interactive data visualization, data navigation and visual exploratory data analysis are presented, which exploit the remarkable human perceptive and associative capabilities for interactive visual exploratory data analysis and systematic recognition system design. The main focus of this paper is on the comparison of feature selection and feature extraction techniques and the potential benefit of their combination. Further, the interesting implications of dimensionality reduction for VLSI design and related area and power consumption are pointed out.

In this work, we present an effective and scalable system for multivariate volume data visualization and analysis with a novel Transfer Function (TF) interface design that tightly couples parallel coordinates plots (PCP) and MDS-based dimension projection plots. In our system, the PCP visualizes the data distribution of each variate and the MDS plots project features. Together, they are integrated seamlessly to provide flexible feature classification without context switching between different data presentations during the user interaction. The proposed interface enables users to identify relevant correlation clusters and assign optical properties on them. To further support large scale multivariate volume data visualization and analysis, we develop three integrated parallel systems to accelerate the rendering of PCP, the layout of MDS, as well as parallel rendering of multivarite volume data. Our experiments show that the system is effective in multivariate volume data visualization and its performance is scalable for data sets with different sizes and number of variates.

Data with spatio-temporal structure can arise in many contexts, therefore a considerable interest in modelling these data has been generated, but the complexity of spatio-temporal models, together with the size of the dataset, results in a challenging task. The modelization is even more complex in presence of multivariate data. Since some modelling problems are more natural to think through in functional terms, even if only a finite number of observations is available, treating the data as functional can be useful (Berrendero et al. in Comput Stat Data Anal 55:2619–2634, 2011). Although in Ramsay and Silverman (Functional data analysis, 2nd edn. Springer, New York, 2005) the case of multivariate functional data is also contemplated, they do not cope with more than one dimension (only time is considered as covariate) in the domain of the considered functions. In estimating functional data through smoothing methods, a proper framework for incorporating space-time structures can be found in the generalized additive models (GAM), while classical dimension reduction techniques for functional data lead to functional principal component analysis (FPCA). In a previous work Ruggieri et al. (J Appl Stat 40(4):795–807, 2013) extended temporal FPCA, that is FPCA on data modelled as functions of the one-dimensional time, to multivariate (more than one variable, in our case pollutants) context. In this paper the computational aspects of FPCA are extended to more than one dimension: space (long, lat) and/or space-time; moreover, multidimensional (spatial, spatio-temporal) FPCA is extended to multivariate case. In order to provide a generalization of FPCA to multidimensional (spatio-temporal) and simultaneously multivariate data, we link GAM models together with the approach proposed by Ramsay and Silverman (2005). The paper describes all the computational details useful to implement this approach, while its effectiveness will be shown by a multivariate spatio-temporal environmental dataset, whose structure is actually very common in literature.

The article presents and investigates new possibilities of multivariate data visualization and their analytical convenience. We demonstrate elementary principles, algorithms and graphical outputs of modern visualization methods with particular focus on Bertin matrices, RADVIZ, Projection pursuit and parallel coordinates. Illustrative examples show their practical implementation into the process of multivariate data analysis, hence providing the reader with an idea of the wide range of their application.

Multidimensional Projections (MPs) have become popular as visual data analysis tools in several application domains, including Scientific Visualization. Current techniques are fast, precise and capable of handling local and global data features, having successfully supported spatial and abstract data visualizations. However, two major shortcomings hinder their application for exploratory analysis of time-varying multivariate volumetric data. Current techniques lack visual coherence when applied to data collected across consecutive time stamps and offer little support to investigating attribute-specific questions. Both are relevant properties when analysing time varying volumes. In this paper we revisit projection methods from this perspective and introduce modifications into two existing high-performance techniques to ensure temporal coherence. We also propose a hybrid visualization strategy that can assist users investigating the role of a specific attribute on data behavior through time. We illustrate how our approaches enhance projection-based visual exploration of time-varying multivariate volume data with their application to data sets from three distinct simulations, made available for editions of the IEEE Visualization Contest.

Multivariate data analysis for Raman imaging of a model pharmaceutical tablet

Data clustering is an important technique for data visualization and statistical data analysis. Using data clustering technique for geo-located data analysis and visualization has been shown to be useful in many practical domains. Although there are numerous studies for data clustering algorithms, the implementations and applications based on these algorithms are rare and inadequate, especially for web-based data visualization services. In this research, a new web-based interactive data clustering prototype is proposed for geo-located data visualization. The necessary web-based infrastructure for geo-located data analysis and visualization is designed and implemented at first. And then the new interactive data clustering prototype using multi-dimensional K-Means algorithm is implemented on the existing infrastructure. In order to assess whether the new prototype can contribute meaningfully to architectural design and urban studies, qualified users with urban study or architectural backgrounds are invited to use the prototype and then make their comments and evaluations. To make it clear how this new prototype might apply in real world settings, an application scenario in which the user might interact with the prototype is provided and analyzed.

Currently, empirical mode decomposition (EMD) has become a popular data-driven time-frequency analysis method for nonstationary and nonlinear data. However, it is still limited to univariate data due to the number and/or scale misalignment for multivariate data. A newly developed multivariate EMD (MEMD) scheme decomposes multivariate data simultaneously and thus leads to mode alignment and minimizes mode mixing. In addition, an improved amplitude and frequency modulation (AM-FM) decomposition algorithm presented here provides an estimation of a more meaningful instantaneous amplitude and frequency than the widely used Hilbert transform (HT). Both of these facilitate development of a time-frequency analysis framework for multivariate nonstationary and nonlinear data analysis. In this paper, MEMD-based scalogram and coscalogram, and instantaneous frequency spectra and cospectra are proposed to characterize a multivariate nonstationary process. The scalogram and instantaneous frequency spectra capture spectral evolution of each component while the coscalogram and instantaneous frequency cospectra reveal embedded intermittent correlation between two components. Compared with scale-based scalogram and coscalogram, frequency-based instantaneous frequency spectra and cospectra provide a more detailed portrait of multivariate data. The effectiveness of the proposed MEMD-based time-frequency analysis framework is demonstrated by numerical examples of a thunderstorm downburst and an earthquake ground motion. Also, the results from the MEMD-based approach are compared with those based on a continuous wavelet transform, which further reinforces the effectiveness of the proposed framework.

It is well known that the correlation matrix has a central role in the analysis of multivariate data. The inverse of the correlation matrix, however, also has important interpretations. An exposition of some of the properties connected with the inverse of the correlation matrix is provided and its use in the analysis of multivariate data is described.

SUMMARY This paper presents a Bayesian method for the analysis of toxicological multivariate mortality data when the discrete mortality rate for each family of subjects at a given time depends on familial random effects and the toxicity level experienced by the family. Our aim is to model and analyse one set of such multivariate mortality data with large family sizes: the potassium thiocyanate (KSCN) tainted fish tank data of O’Hara Hines. The model used is based on a discretized hazard with additional time-varying familial random effects. A similar previous study (using sodium thiocyanate (NaSCN)) is used to construct a prior for the parameters in the current study. A simulation-based approach is used to compute posterior estimates of the model parameters and mortality rates and several other quantities of interest. Recent tools in Bayesian model diagnostics and variable subset selection have been incorporated to verify important modelling assumptions regarding the effects of time and heterogeneity among the families on the mortality rate. Further, Bayesian methods using predictive distributions are used for comparing several plausible models.

In order to meet the needs of data analysis and visualization to assist mobile network operation decision-making, telecom operators have established several mobile network index analysis tools or platforms. However, the network data analysis efficiency of planning, construction, maintenance and optimization is still low, and demand-oriented visualization means are still insufficient. This paper designs a mobile network data analysis and visualization system. The designed system aims at addressing the problems that mobile network has various types of data. The designed system can make data easy to manage, improve the data analysis efficiency and the flexibility of data visualization for telecom operators.

This paper introduces a probabilistic self-organizing map for clustering, analysis and visualization of multivariate binary data. We propose a probabilistic formalism dedicated to binary data in which cells are represented by a Bernoulli distribution. Each cell is characterized by a prototype with the same binary coding as used in the data space and the probability of being different from this prototype. The learning algorithm, BeSOM, that we propose is an application of the EM standard algorithm. We illustrate the power of this method with two data sets taken from a public data set repository: a handwritten digit data set and a zoo data set. The results show a good quality of the topological ordering and homogenous clustering.

Role of dynamic cartography in simulations of landscape processes based on multivariate fields

The visual analysis of temporal network data is often hindered by the cognitively demanding nature of dynamic graphic visualizations. Addressing this issue, the graph visualization tool HOTVis generates time-aware static network visualizations that highlight the causal topology of temporal networks, i.e. which nodes can directly and indirectly influence each other, and are thus considerably easier to interpret than state-of-the-art dynamic graph visualizations.

Deep-learning-based visual data analytics for smart construction management