Parallel Coordinates Research Articles

Research on Data Mining Technique Based on Visualization of Parallel Coordinate Method

As a confrontational-main sport, with the increase of social and political problems, there are more and more technical fouls in competitions of basketball. The hot problem we must to solve is the control of the stadium information, the analysis of basketball player's technical fouls and the implement of targeted management. Firstly, this paper introduces the status and applications of visual data mining technique based on parallel coordinates briefly. Then, it analyzes the advantage of this method which has been used in basketball player's technical fouls based on mathematical modeling thinking. Then, it establishes the optimized data mining model of basketball player's technical fouls based on visualization of parallel coordinate method which provide theoretical basis and new ideas for the research and practice of this field.

MViewer: mobile phone spatiotemporal data viewer

Nowadays movement patterns and people’s behavioral models are needed for traffic engineers and city planners. These observations could be used to reason about mobility and its sustainability and to support decision makers with reliable information. The very same knowledge about human diaspora and behavior extracted from these data is also valuable to the urban planner, so as to localize new services, organize logistics systems and to detect changes as they occur in the movement behavior. Moreover, it is interesting to investigate movement in places like a shopping area or a working district either for commercial purposes or for improving the service quality. These kinds of tracking data are made available by wireless and mobile communication technologies. It is now possible to record and collect a large amount of mobile phone calls in a city. Technologies for object tracking have recently become affordable and reliable and hence we were able to collect mobile phone data from a city in China from January 1, 2008 to December 31, 2008. The large amount of phone call records from mobile operators can be considered as life mates and sensors of persons to inform howmany people are present in any given area and how many are entering or leaving. Each phone call record usually contains the caller and callee IDs, date and time, and the base station where the phone calls are made. As mobile phones are widely used in our daily life, many human behaviors can be revealed by analyzing mobile phone data. Through mobile phones, we can learn the information about locations, communications between mobile phone users during their daily lives. In this work, we propose a comprehensive visual analysis system named as MViewer, Mobile phone spatiotemporal data Viewer, which is the first system to visualize and analyze the population’smobility patterns from millions of phone call records. Our system consists of three major components: 1) visual analysis of user groups in a base station; 2) visual analysis of the mobility patterns on different user groups making phone calls in certain base stations; 3) visual analysis of handoff phone call records. Some well-established visualization techniques such as parallel coordinates and pixelbased representations have been integrated into our system. We also develop a novel visualization schemes, Voronoidiagram-based visual encoding to reveal the unique features of mobile phone data. We have applied our system to real mobile phone datasets that are kindly provided by our project partners and obtained some interesting findings regarding people’s mobility patterns.

Exploring population pharmacokinetic modeling with resampling visualization.

Background. In the last decade, population pharmacokinetic (PopPK) modeling has spread its influence in the whole process of drug research and development. While targeting the construction of the dose-concentration of a drug based on a population of patients, it shows great flexibility in dealing with sparse samplings and unbalanced designs. The resampling approach has been considered an important statistical tool to assist in PopPK model validation by measuring the uncertainty of parameter estimates and evaluating the influence of individuals. Methods. The current work describes a graphical diagnostic approach for PopPK models by visualizing resampling statistics, such as case deletion and bootstrap. To examine resampling statistics, we adapted visual methods from multivariate analysis, parallel coordinate plots, and multidimensional scaling. Results. Multiple models were fitted, the information of parameter estimates and diagnostics were extracted, and the results were visualized. With careful scaling, the dependencies between different statistics are revealed. Using typical examples, the approach proved to have great capacity to identify influential outliers from the statistical perspective, which deserves special attention in a dosing regimen. Discussion. By combining static graphics with interactive graphics, we are able to explore the multidimensional data from an integrated and systematic perspective. Complementary to current approaches, our proposed method provides a new way for PopPK modeling analysis.

Information visualisation in clinical medicine using 3D parallel diagrams: a case history.

An information visualisation tool was implemented and tested as a solution to the problem of visualising clinical experience derived from large amounts of formalised clinical data. The tool was based on the idea of dynamic 3D parallel diagrams with support for direct manipulation, an idea similar to the notion of 3D parallel coordinates. The tool was tested on a knowledge base containing about 1500 examinations obtained from different clinics. Clinical practice showed that the basic idea is conceptually appealing to the involved clinicians as the tool can be used for generating and testing of hypothesis.

Visualising intrinsic disorder and conformational variation in protein ensembles.

Intrinsically disordered regions (IDRs) in proteins are still not well understood, but are increasingly recognised as important in key biological functions, as well as in diseases. IDRs often confound experimental structure determination-however, they are present in many of the available 3D structures, where they exhibit a wide range of conformations, from ill-defined and highly flexible to well-defined upon binding to partner molecules, or upon post-translational modifications. Analysing such large conformational variations across ensembles of 3D structures can be complex and difficult; our goal in this paper is to improve this situation by augmenting traditional approaches (molecular graphics and principal components) with methods from human-computer interaction and information visualisation, especially parallel coordinates. We present a new tool integrating these approaches, and demonstrate how it can dissect ensembles to reveal functional insights into conformational variation and intrinsic disorder.

Knowledge Discovery for Car-Body Weight Reduction with Multidisciplinary Design Optimization and Trade off Analysis

Multidisciplinary design optimization (MDO) using design of experiments (DoE) and response surface method (RSM) is one of the effective methods for reducing weight of car body structure. However, large amount of information obtained from the optimization makes the optimization itself not useful. This problem is caused by large number of variables of vehicle model. This paper proposes a trade-off analysis process using sensitivity analysis, multi-objective optimization, hierarchical cluster analysis and parallel coordinate plot for analyzing optimization information. As a result, it was clarified very important parts for reducing body weight. Accordingly, even the designers without the expert knowledge may easily be able to make a knowledge discovery for car-body weight reduction.

Nanocubes for Real-Time Exploration of Spatiotemporal Datasets

Consider real-time exploration of large multidimensional spatiotemporal datasets with billions of entries, each defined by a location, a time, and other attributes. Are certain attributes correlated spatially or temporally? Are there trends or outliers in the data? Answering these questions requires aggregation over arbitrary regions of the domain and attributes of the data. Many relational databases implement the well-known data cube aggregation operation, which in a sense precomputes every possible aggregate query over the database. Data cubes are sometimes assumed to take a prohibitively large amount of space, and to consequently require disk storage. In contrast, we show how to construct a data cube that fits in a modern laptop's main memory, even for billions of entries; we call this data structure a nanocube. We present algorithms to compute and query a nanocube, and show how it can be used to generate well-known visual encodings such as heatmaps, histograms, and parallel coordinate plots. When compared to exact visualizations created by scanning an entire dataset, nanocube plots have bounded screen error across a variety of scales, thanks to a hierarchical structure in space and time. We demonstrate the effectiveness of our technique on a variety of real-world datasets, and present memory, timing, and network bandwidth measurements. We find that the timings for the queries in our examples are dominated by network and user-interaction latencies.

An Information-Aware Framework for Exploring Multivariate Data Sets

Information theory provides a theoretical framework for measuring information content for an observed variable, and has attracted much attention from visualization researchers for its ability to quantify saliency and similarity among variables. In this paper, we present a new approach towards building an exploration framework based on information theory to guide the users through the multivariate data exploration process. In our framework, we compute the total entropy of the multivariate data set and identify the contribution of individual variables to the total entropy. The variables are classified into groups based on a novel graph model where a node represents a variable and the links encode the mutual information shared between the variables. The variables inside the groups are analyzed for their representativeness and an information based importance is assigned. We exploit specific information metrics to analyze the relationship between the variables and use the metrics to choose isocontours of selected variables. For a chosen group of points, parallel coordinates plots (PCP) are used to show the states of the variables and provide an interface for the user to select values of interest. Experiments with different data sets reveal the effectiveness of our proposed framework in depicting the interesting regions of the data sets taking into account the interaction among the variables.

SketchPadN-D: WYDIWYG Sculpting and Editing in High-Dimensional Space

High-dimensional data visualization has been attracting much attention. To fully test related software and algorithms, researchers require a diverse pool of data with known and desired features. Test data do not always provide this, or only partially. Here we propose the paradigm WYDIWYGS (What You Draw Is What You Get). Its embodiment, SketchPadND, is a tool that allows users to generate high-dimensional data in the same interface they also use for visualization. This provides for an immersive and direct data generation activity, and furthermore it also enables users to interactively edit and clean existing high-dimensional data from possible artifacts. SketchPadND offers two visualization paradigms, one based on parallel coordinates and the other based on a relatively new framework using an N-D polygon to navigate in high-dimensional space. The first interface allows users to draw arbitrary profiles of probability density functions along each dimension axis and sketch shapes for data density and connections between adjacent dimensions. The second interface embraces the idea of sculpting. Users can carve data at arbitrary orientations and refine them wherever necessary. This guarantees that the data generated is truly high-dimensional. We demonstrate our tool's usefulness in real data visualization scenarios.

GPLOM: The Generalized Plot Matrix for Visualizing Multidimensional Multivariate Data

Scatterplot matrices (SPLOMs), parallel coordinates, and glyphs can all be used to visualize the multiple continuous variables (i.e., dependent variables or measures) in multidimensional multivariate data. However, these techniques are not well suited to visualizing many categorical variables (i.e., independent variables or dimensions). To visualize multiple categorical variables, 'hierarchical axes' that 'stack dimensions' have been used in systems like Polaris and Tableau. However, this approach does not scale well beyond a small number of categorical variables. Emerson et al. [8] extend the matrix paradigm of the SPLOM to simultaneously visualize several categorical and continuous variables, displaying many kinds of charts in the matrix depending on the kinds of variables involved. We propose a variant of their technique, called the Generalized Plot Matrix (GPLOM). The GPLOM restricts Emerson et al.'s technique to only three kinds of charts (scatterplots for pairs of continuous variables, heatmaps for pairs of categorical variables, and barcharts for pairings of categorical and continuous variable), in an effort to make it easier to understand. At the same time, the GPLOM extends Emerson et al.'s work by demonstrating interactive techniques suited to the matrix of charts. We discuss the visual design and interactive features of our GPLOM prototype, including a textual search feature allowing users to quickly locate values or variables by name. We also present a user study that compared performance with Tableau and our GPLOM prototype, that found that GPLOM is significantly faster in certain cases, and not significantly slower in other cases.

Circular Visualization Enhancement through Complementary Interaction

In today’s scenario, most of the applications like web repository generate bulk of heterogeneous data. Visualization helps in the interpretation of meaningful information from huge data generated by these applications. There are many visualization techniques available in literature. Parallel Coordinates, Glyph and Projection techniques are some of the famous data visualization techniques. Projection techniques are unable to illustrate the details of data like intertuple variations. Parallel Coordinates have good capacity to express the details, but require more space as well as it is affected from clutter. Trend figure is also affected from clutter up to some extent. Considering the heterogeneous nature of data and limitation of visualization techniques, single visualization technique does not fulfill the analytical requirements of different application domains. But combination of these visualization techniques may fulfill the analytical requirements of applications from different domain. In this work, we have developed a tool, in which Parallel coordinates and trend figure used as interactive techniques with projection technique. The proposed combination reduces the limitation like intertuple variations of projection technique. Analysis of data with proposed tool has the potential to uncover other relationships and non-trivial structures.

Identifying and measuring stakeholder preferences for disease prioritisation: A case study of the pig industry in Australia

We describe stakeholder preference modelling using a combination of new and recently developed techniques to elicit criterion weights to incorporate into a multi-criteria decision analysis framework to prioritise exotic diseases for the pig industry in Australia.Australian pig producers were requested to rank disease scenarios comprising nine criteria in an online questionnaire. Parallel coordinate plots were used to visualise stakeholder preferences, which aided identification of two diverse groups of stakeholders – one group prioritised diseases with impacts on livestock, and the other group placed more importance on diseases with zoonotic impacts. Probabilistic inversion was used to derive weights for the criteria to reflect the values of each of these groups, modelling their choice using a weighted sum value function. Validation of weights against stakeholders’ rankings for scenarios based on real diseases showed that the elicited criterion weights for the group who prioritised diseases with livestock impacts were a good reflection of their values, indicating that the producers were able to consistently infer impacts from the disease information in the scenarios presented to them. The highest weighted criteria for this group were attack rate and length of clinical disease in pigs, and market loss to the pig industry. The values of the stakeholders who prioritised zoonotic diseases were less well reflected by validation, indicating either that the criteria were inadequate to consistently describe zoonotic impacts, the weighted sum model did not describe stakeholder choice, or that preference modelling for zoonotic diseases should be undertaken separately from livestock diseases. Limitations of this study included sampling bias, as the group participating were not necessarily representative of all pig producers in Australia, and response bias within this group.The method used to elicit criterion weights in this study ensured value trade-offs between a range of potential impacts, and that the weights were implicitly related to the scale of measurement of disease criteria. Validation of the results of the criterion weights against real diseases – a step rarely used in MCDA – added scientific rigour to the process. The study demonstrated that these are useful techniques for elicitation of criterion weights for disease prioritisation by stakeholders who are not disease experts. Preference modelling for zoonotic diseases needs further characterisation in this context.

Visualization of cluster structure and separation in multivariate mixed data: A case study of diversity faultlines in work teams

In organizational management, researchers and managers study separations or faultlines that occur in diverse teams when members form subgroups based on the alignment of multiple demographic characteristics. The team faultline concept is operationalized using multivariate cluster analysis—analysts use faultline measures to identify subgroups/clusters in a team and to quantify how subgroups/clusters are separated. Unfortunately, these measures have limited capacity to enable users to observe and explore faultlines and subgroup structure across the examined attributes efficiently. We address this problem and make three contributions. First, we propose a visual representation for communicating faultline information that is based on multiple linked, stacked histograms in an axis-parallel layout. Second, we evaluate the effectiveness of the proposed technique in a controlled user study, comparing it to the two other common multivariate representations of clusters: parallel coordinates and scatter plot matrices. While we chose faultline-related tasks based on the requirements by domain experts in organizational management, the study findings can be generalized to representations and tasks involving distributions of clusters of multivariate objects in mixed-type data. Finally, inspired by geological faultlines, we propose several visual enhancements to stacked histograms to further facilitate the task of identifying “cracks” within work teams.

Multivariate monitoring of a carbon dioxide removal process

Process hardware improvements have significantly increased the amount of information collected in industrial facilities, which allows the use of tools such as multivariable statistical analysis for process monitoring. Nevertheless, such statistical models tend to be static and extremely general when implemented in process facilities with several modes of operation, as is the case of carbon dioxide removal processes. This work demonstrates the use of multivariable statistical analysis for process transitions between different modes of operation. Continuous process analytics are used to define key variables, named “state variables”, to determine the current mode of operation. This work also makes use of parallel coordinates to illustrate the simultaneous visualization of several transition paths and statistical tests. Such tests apply confidence limits that are appropriate to the current mode of operation. This methodology is successfully tested in the CO2 capture plant at the University of Texas in Austin. The results show the effectiveness of using this type of application to detect abnormal operating conditions at different levels of operations.

Multi-objective optimisation of horizontal axis wind turbine structure and energy production using aerofoil and blade properties as design variables

The design of wind turbine blades is a true multi-objective engineering task. The aerodynamic effectiveness of the turbine needs to be balanced with the system loads introduced by the rotor. Moreover the problem is not dependent on a single geometric property, but besides other parameters on a combination of aerofoil family and various blade functions. The aim of this paper is therefore to present a tool which can help designers to get a deeper insight into the complexity of the design space and to find a blade design which is likely to have a low cost of energy. For the research we use a Computational Blade Optimisation and Load Deflation Tool (CoBOLDT) to investigate the three extreme point designs obtained from a multi-objective optimisation of turbine thrust, annual energy production as well as mass for a horizontal axis wind turbine blade. The optimisation algorithm utilised is based on Multi-Objective Tabu Search which constitutes the core of CoBOLDT. The methodology is capable to parametrise the spanning aerofoils with two-dimensional Free Form Deformation and blade functions with two tangentially connected cubic splines. After geometry generation we use a panel code to create aerofoil polars and a stationary Blade Element Momentum code to evaluate turbine performance. Finally, the obtained loads are fed into a structural layout module to estimate the mass and stiffness of the current blade by means of a fully stressed design. For the presented test case we chose post optimisation analysis with parallel coordinates to reveal geometrical features of the extreme point designs and to select a compromise design from the Pareto set. The research revealed that a blade with a feasible laminate layout can be obtained, that can increase the energy capture and lower steady state systems loads. The reduced aerofoil camber and an increased L/D-ratio could be identified as the main drivers. This statement could not be made with other tools of the research community before.

Measuring Privacy and Utility in Privacy‐Preserving Visualization

AbstractIn previous work, we proposed a technique for preserving the privacy of quasi‐identifiers in sensitive data when visualized using parallel coordinates. This paper builds on that work by introducing a number of metrics that can be used to assess both the level of privacy and the amount of utility that can be gained from the resulting visualizations. We also generalize our approach beyond parallel coordinates to scatter plots and other visualization techniques. Privacy preservation generally entails a trade‐off between privacy and utility: the more the data are protected, the less useful the visualization. Using a visually‐oriented approach, we can provide a higher amount of utility than directly applying data anonymization techniques used in data mining. To demonstrate this, we use the visual uncertainty framework for systematically defining metrics based on cluster artifacts and information theoretic principles. In a case study, we demonstrate the effectiveness of our technique as compared to standard data‐based clustering in the context of privacy‐preserving visualization.

Cartographic Visualization of Vulnerability to Natural Hazards

Vulnerability to natural hazards has many components. It is about exposure to various natural disasters, but a place's vulnerability also depends on its capacity to prepare for, respond to, and recover from shocks resulting from natural extreme events. To avoid increased place vulnerability due to the anticipated negative effects of climate change, local authorities need to know which places are the most vulnerable and what makes these areas vulnerable. We have developed ViewExposed to provide this information. Knowing where the most vulnerable areas are is very useful for local stakeholders, since these places may be most in need of adaptation strategies. However, stakeholders also need to have an understanding of what makes these areas vulnerable. ViewExposed provides this information using a parallel coordinates plot, a table view, sparklines, and a profile report. Although vulnerability assessment data are complex, ViewExposed has an easy-to-use interface facilitating a high degree of user interaction through multiple and linked views. An improved understanding of the many aspects of vulnerability has a far-reaching potential to inform users efficiently about factors that influence the overall vulnerability and, as a consequence, can help raise people's awareness of what makes places vulnerable to natural threats.

Visualization of flow past a marine turbine: the information-assisted search for sustainable energy

Interest in renewable, green, and sustainable energy has risen sharply in recent years. The use of marine turbines to extract kinetic energy from the tidal current is gaining popularity. CFD modeling is carried out to investigate the surrounding flow behavior and thus develop effective marine turbine systems. However, visualizing the simulation results remains a challenging task for engineers. In this paper, we develop, explore and present customized visualization techniques in order to help engineers gain a fast overview and intuitive insight into the flow past the marine turbine. The system exploits multiple-coordinated information-assisted views of the CFD simulation data. Our application consists of a tabular histogram, velocity histogram, parallel coordinate plot, streamline plot and spatial views. Information-based streamline seeding is used to investigate the behavior of the flow deemed interesting to the engineer. Specialized, application-specific information based on swirling flow is derived and visualized in order to evaluate turbine blade design. To demonstrate the usage of our system, a selection of specialized case scenarios designed to answer the core questions brought out by engineers is described. We also report feedback on our system from CFD experts researching marine turbine simulations.

Procedural meta-models for architectural design praxis

This article discusses a procedure for the exploration of options in preliminary design. The procedure is based on the application of morphing procedures, which are typical of animation software, to building parametric analyses. The procedure is based on partially overlapping sequences of evaluations targeted on dynamic ad-hoc test-models and is aimed at the creation of data fields for the representation of the performance consequences of competing design sceneries. This representation is necessarily multidimensional and is based on parallel coordinates plots. The implementation of a specific test procedure of the kind described above is also discussed. The procedure has been supported by the use of contemporary analytical and representational systems and tools; namely, ESP-r, Radiance, Ggobi, and an extensible tool for the dynamic morphing of models through user-specified criteria, named OPTS, by the author.

Experimental design for the optimization of the derivatization reaction in determining chlorophenols and chloroanisoles by headspace-solid-phase microextraction–gas chromatography/mass spectrometry

The paper shows some tools (its interpretation and usefulness) to optimize a derivatization reaction and to more easily interpret and visualize the effect that some experimental factors exert on several analytical responses of interest when these responses are in conflict. The entire proposed procedure has been applied in the optimization of equilibrium/extraction temperature and extraction time in the acetylation reaction of 2,4,6-trichlorophenol; 2,3,4,6-tetrachlorophenol, pentachlorophenol and 2,4,6-tribromophenol as internal standard (IS) in presence of 2,4,6-trichloroanisole, 2,3,5,6-tetrachloroanisole, pentachloroanisole and 2,4,6-trichloroanisole-d5 as IS. The procedure relies on the second order advantage of PARAFAC (parallel factor analysis) that allows the unequivocal identification and quantification, mandatory according international regulations (in this paper the EU document SANCO/12495/2011), of the acetyl-chlorophenols and chloroanisoles that are determined by means of a HS-SPME–GC/MS automated device. The joint use of a PARAFAC decomposition and a Doehlert design provides the data to fit a response surface for each analyte. With the fitted surfaces, the overall desirability function and the Pareto-optimal front are used to describe the relation between the conditions of the derivatization reaction and the quantity extracted of each analyte. The visualization by using a parallel coordinates plot allows a deeper knowledge about the problem at hand as well as the wise selection of the conditions of the experimental factors for achieving specific goals about the responses. In the optimal experimental conditions (45°C and 25min) the determination by means of an automated HS-SPME–GC/MS system is carried out. By using the regression line fitted between calculated and true concentrations, it has been checked that the procedure has neither proportional nor constant bias. The decision limits, CCa, for probability a of false positive set to 0.05, vary between 0.221 and 0.420µgL-1.