Interactive analysis and visualization of geoscience data with Ocean Data View

Ocean Data View (ODV) is a freeware package for the interactive exploration and graphical display of multi-parameter profile or sequence data. Although originally developed for oceanographic observations only, the underlying concept is more general, and data or model output from other areas of geosciences, like for instance geology, geophysics, geography and atmospheric research can be maintained and explored with ODV as well. The data format of ODV is designed for dense storage and direct data access, and allows the construction of very large datasets, even on affordable and portable hardware. ODV supports display of original data by colored dots or actual data values at the measurement locations. In addition, two fast and reliable variable-resolution gridding algorithms allow color shading and contouring of gridded fields along sections and on general 3D surfaces. A large number of derived quantities can be selected and calculated online. These variables are displayed and analyzed in the same way as the basic variables stored in disk files. ODV runs on PCs under Windows and on UNIX workstations under SUN Solaris. The software and extensive sets of coastline, topography, river-, lake- and border outlines as well as various gazetteers of topographic features are available at no cost over the Internet. In addition, the electronic atlas eWOCE that consists of oceanographic data from the World Ocean Circulation Experiment (WOCE) is also available free of charge over the Internet. A gallery of prepared plots of property distributions along WOCE sections provides a quick overview over hydrographic, nutrient, oxygen and transient tracer fields in the ocean and, apart from the scientific use for oceanographic research, can serve as tutorial material for introductory or advanced courses on oceanography.     

A hierarchical latent variable model for data visualization

Visualization has proven to be a powerful and widely-applicable tool for the analysis and interpretation of multivariate data. Most visualization algorithms aim to find a projection from the data space down to a two-dimensional visualization space. However, for complex data sets living in a high-dimensional space, it is unlikely that a single two-dimensional projection can reveal all of the interesting structure. We therefore introduce a hierarchical visualization algorithm which allows the complete data set to be visualized at the top level, with clusters and subclusters of data points visualized at deeper levels. The algorithm is based on a hierarchical mixture of latent variable models, whose parameters are estimated using the expectation-maximization algorithm. We demonstrate the principle of the approach on a toy data set, and we then apply the algorithm to the visualization of a synthetic data set in 12 dimensions obtained from a simulation of multiphase flows in oil pipelines, and to data in 36 dimensions derived from satellite images     

Information visualization and visual data mining

Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data is becoming increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques which is based on the data type to be visualized, the visualization technique, and the interaction and distortion technique. We exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special section     

Exploratory spatio-temporal data mining and visualization

Spatio-temporal data sets are often very large and difficult to analyze and display. Since they are fundamental for decision support in many application contexts, recently a lot of interest has arisen toward data-mining techniques to filter out relevant subsets of very large data repositories as well as visualization tools to effectively display the results. In this paper we propose a data-mining system to deal with very large spatio-temporal data sets. Within this system, new techniques have been developed to efficiently support the data-mining process, address the spatial and temporal dimensions of the data set, and visualize and interpret results. In particular, two complementary 3D visualization environments have been implemented. One exploits Google Earth to display the mining outcomes combined with a map and other geographical layers, while the other is a Java3D-based tool for providing advanced interactions with the data set in a non-geo-referenced space, such as displaying association rules and variable distributions.     

Visual data mining modeling techniques for the visualization of mining outcomes

The visual senses for humans have a unique status, offering a very broadband channel for information flow. Visual approaches to analysis and mining attempt to take advantage of our abilities to perceive pattern and structure in visual form and to make sense of, or interpret, what we see. Visual Data Mining techniques have proven to be of high value in exploratory data analysis and they also have a high potential for mining large databases. In this work, we try to investigate and expand the area of visual data mining by proposing new visual data mining techniques for the visualization of mining outcomes.     

Interactive three-dimensional visualization of seismic array data

We present a set of tools developed for the analysis of three-component seismic array data. The first tool involves the application of three-dimensional volume visualization techniques for displaying the results of single-component, array, slowness analysis calculations. This technique has proven useful for analysis of both body waves and surface waves recorded by broadband arrays with a wide range of apertures. The second tool presented is a tool for animation and interactive analysis of three-dimensional particle motions observed by an array of three-component instruments. This tool displays a three-dimensional track of particle motions over a specified time window as recorded by three-component array stations. In order to compare the amplitudes, waveforms, polarization and time delays of signals on different array stations the records are aligned by the time of arrival of a plane wave with the azimuth and the apparent slowness determined from two-dimensional semblance analysis. This technique provides an improved understanding of the spatial variability of seismic wave fields that is not possible with conventional, two-dimensional graphics.     

Semisupervised learning of hierarchical latent trait models for data visualization

Recently, we have developed the hierarchical generative topographic mapping (HGTM), an interactive method for visualization of large high-dimensional real-valued data sets. We propose a more general visualization system by extending HGTM in three ways, which allows the user to visualize a wider range of data sets and better support the model development process. 1) We integrate HGTM with noise models from the exponential family of distributions. The basic building block is the latent trait model (LTM). This enables us to visualize data of inherently discrete nature, e.g., collections of documents, in a hierarchical manner. 2) We give the user a choice of initializing the child plots of the current plot in either interactive, or automatic mode. In the interactive mode, the user selects "regions of interest", whereas in the automatic mode, an unsupervised minimum message length (MML)-inspired construction of a mixture of LTMs is employed. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. 3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualization plots, since they can highlight the boundaries between data clusters. We illustrate our approach on a toy example and evaluate it on three more complex real data sets.     

Interactive visualization of 3D medical data

Techniques for rendering 3-D medical data are described. They consist of (1) surface-based techniques, which apply a surface detector to the sample array, then fit geometric primitives to the detected surfaces, and finally render the resulting geometric representation; (2) binary voxel techniques, which begin by thresholding the volume data to produce a three-dimensional binary array; the cuberille algorithm then renders this array by treating 1's as opaque cubes having six polygonal faces; and (3) volume-rendering techniques, a variant of the binary voxel techniques in which a color and a partial opacity are assigned to each voxel; images are formed from the resulting colored, semitransparent volume by blending together voxels projecting to the same pixel on the picture plane. Specialized display devices (stereo viewers, varifocal mirrors, cine sequences, real-time image-generation systems, and head-mounted displays) are described. Topics for future research are identified     

Spatial ordering and encoding for geographic data mining and visualization

Geographic information (e.g., locations, networks, and nearest neighbors) are unique and different from other aspatial attributes (e.g., population, sales, or income). It is a challenging problem in spatial data mining and visualization to take into account both the geographic information and multiple aspatial variables in the detection of patterns. To tackle this problem, we present and evaluate a variety of spatial ordering methods that can transform spatial relations into a one-dimensional ordering and encoding which preserves spatial locality as much possible. The ordering can then be used to spatially sort temporal or multivariate data series and thus help reveal patterns across different spaces. The encoding, as a materialization of spatial clusters and neighboring relations, is also amenable for processing together with aspatial variables by any existing (non-spatial) data mining methods. We design a set of measures to evaluate nine different ordering/encoding methods, including two space-filling curves, six hierarchical clustering based methods, and a one-dimensional Sammon mapping (a multidimensional scaling approach). Evaluation results with various data distributions show that the optimal ordering/encoding with the complete-linkage clustering consistently gives the best overall performance, surpassing well-known space-filling curves in preserving spatial locality. Moreover, clustering-based methods can encode not only simple geographic locations, e.g., x and y coordinates, but also a wide range of other spatial relations, e.g., network distances or arbitrarily weighted graphs.     

Interactive visualization and analysis of transitional flow

A stand-alone visualization application has been developed by a multi-disciplinary, collaborative team with the sole purpose of creating an interactive exploration environment allowing turbulent flow researchers to experiment and validate hypotheses using visualization. This system has specific optimizations made in data management, caching computations, and visualization allowing for the interactive exploration of datasets on the order of 1TB in size. Using this application, the user (co-author Calo) is able to interactively visualize and analyze all regions of a transitional flow volume, including the laminar, transitional and fully turbulent regions. The underlying goal of the visualizations produced from these transitional flow simulations is to localize turbulent spots in the laminar region of the boundary layer, determine under which conditions they form, and follow their evolution. The initiation of turbulent spots, which ultimately lead to full turbulence, was located via a proposed feature detection condition and verified by experimental results. The conditions under which these turbulent spots form and coalesce are validated and presented.     

面向地下钱庄洗钱行为的可视化交互分析平台

针对经济犯罪侦查中线索信息数据量极大、信息质量不高(数据缺失、内容不一致、结构复杂等),并且目前大部分系统都着重于从宏观的角度对数据进行分析统计以及决策,无法针对某个具体领域的案件梳理出线索,找出破案关键的问题,研究了地下钱庄洗钱的交易方式以及交易数据的特点、犯罪网络中的结构特征,提出设计并实现了一个面向地下钱庄洗钱行为的可视化交互分析平台。该平台支持对异构数据的同一化,建立了数据模型以及可视结构,资金链的可视化分析,允许用户交互操作,并结合地下钱庄洗钱的交易特征改进了DBSCAN算法;讨论了该平台的系统框架及其关键技术,并结合实际数据给出了应用实例。该平台能够给予办案人员更直观清晰的思路,提高办案效率。     

电子商务数据挖掘可视化系统模型研究及应用

将数据挖掘与相关的数据可视化技术和联机分析处理技术集成,构造一个应用于电子商务Web环境中的以数据挖掘技术为基础的数据可视化分析系统模型——电子商务数据挖掘可视化模型(EDVM),并技术实现主要模块功能,使之能够进行挖掘结果的动态更新与可视化输出,并通过实验初步验证了EDVM模型的有效性。     

Spatial data mining and visualization based on self-organizing map

Based on the traditional spatial data analysis, a novel mode of spatial data mining and visualization is proposed which integrates the self-organizing map for the actual problem. Simulations for IRIS data show that this method （computational and visual） can collaboratively discover complex pattems in large spatial datasets, in an effective and efficient way.     

Probabilistic principal component subspaces: a hierarchical finitemixture model for data visualization

Visual exploration has proven to be a powerful tool for multivariate data mining and knowledge discovery. Most visualization algorithms aim to find a projection from the data space down to a visually perceivable rendering space. To reveal all of the interesting aspects of multimodal data sets living in a high-dimensional space, a hierarchical visualization algorithm is introduced which allows the complete data set to be visualized at the top level, with clusters and subclusters of data points visualized at deeper levels. The methods involve hierarchical use of standard finite normal mixtures and probabilistic principal component projections, whose parameters are estimated using the expectation-maximization and principal component neural networks under the information theoretic criteria. We demonstrate the principle of the approach on several multimodal numerical data sets, and we then apply the method to the visual explanation in computer-aided diagnosis for breast cancer detection from digital mammograms.     

Interactive 3D/2D visualization for geophysical data processing and interpretation

A new 3D/2D interactive display server was developed for the IGeoS geophysical data processing framework presented earlier. With introduction of this major component, the framework becomes conceptually complete and potentially bridges the gap between traditional processing and interpretation geophysical software.The display server utilizes Qt toolkit and OpenGL graphics libraries while taking advantage of the object-oriented design of the core data processing system. It operates by creating image object trees that are automatically propagated to the server(s) residing on the same or remote hosts and producing complex, structured, and interactive data displays. The displays support custom interactive graphical user interfaces, which are constructed entirely by the user and do not require computer coding. With over 200 specialized processing tools available, this approach allows creating 3D visualizations and building custom interactive data analysis, interpretation, and modeling tools in many areas of application. In particular, we show examples of integration of seismic ray tracing, gravity, and receiver function modeling and inversion in deep crustal studies.     

Interactive simulation and visualization

Most researchers who perform data analysis and visualization do so only after everything else is finished, which often means that they don't discover errors invalidating the results of their simulation until post-processing. A better approach would be to improve the integration of simulation and visualization into the entire process so that they can make adjustments along the way. This approach, called computational steering, is the capacity to control all aspects of the computational science pipeline. Recently, several tools and environments for computational steering have begun to emerge. These tools range from those that modify an application's performance characteristics (either by automated means or by user interaction) to those that modify the underlying computational application. A refined problem-solving environment should facilitate everything from algorithm development to application steering. The authors discuss some tools that provide a mechanism to integrate modeling, simulation, data analysis and visualization     

Interactive mining of high utility patterns over data streams

High utility pattern (HUP) mining over data streams has become a challenging research issue in data mining. When a data stream flows through, the old information may not be interesting in the current time period. Therefore, incremental HUP mining is necessary over data streams. Even though some methods have been proposed to discover recent HUPs by using a sliding window, they suffer from the level-wise candidate generation-and-test problem. Hence, they need a large amount of execution time and memory. Moreover, their data structures are not suitable for interactive mining. To solve these problems of the existing algorithms, in this paper, we propose a novel tree structure, called HUS-tree (high utility stream tree) and a new algorithm, called HUPMS (high utility pattern mining over stream data) for incremental and interactive HUP mining over data streams with a sliding window. By capturing the important information of stream data into an HUS-tree, our HUPMS algorithm can mine all the HUPs in the current window with a pattern growth approach. Furthermore, HUS-tree is very efficient for interactive mining. Extensive performance analyses show that our algorithm is very efficient for incremental and interactive HUP mining over data streams and significantly outperforms the existing sliding window-based HUP mining algorithms.     

Hierarchical edge bundles: visualization of adjacency relations in hierarchical data

A compound graph is a frequently encountered type of data set. Relations are given between items, and a hierarchy is defined on the items as well. We present a new method for visualizing such compound graphs. Our approach is based on visually bundling the adjacency edges, i.e., non-hierarchical edges, together. We realize this as follows. We assume that the hierarchy is shown via a standard tree visualization method. Next, we bend each adjacency edge, modeled as a B-spline curve, toward the polyline defined by the path via the inclusion edges from one node to another. This hierarchical bundling reduces visual clutter and also visualizes implicit adjacency edges between parent nodes that are the result of explicit adjacency edges between their respective child nodes. Furthermore, hierarchical edge bundling is a generic method which can be used in conjunction with existing tree visualization techniques. We illustrate our technique by providing example visualizations and discuss the results based on an informal evaluation provided by potential users of such visualizations     

Multidimensional data analysis and visualization for time-dependent CFD problems

Programming and Computer Software - This paper is devoted to analysis and visualization of multidimensional data in problems of computational fluid dynamics (CFD). Multidimensional data are...