Data Visualization Applications Research Articles

A Comparative Analysis of Breast Cancer Detection and Diagnosis Using Data Visualization and Machine Learning Applications.

In the developing world, cancer death is one of the major problems for humankind. Even though there are many ways to prevent it before happening, some cancer types still do not have any treatment. One of the most common cancer types is breast cancer, and early diagnosis is the most important thing in its treatment. Accurate diagnosis is one of the most important processes in breast cancer treatment. In the literature, there are many studies about predicting the type of breast tumors. In this research paper, data about breast cancer tumors from Dr. William H. Walberg of the University of Wisconsin Hospital were used for making predictions on breast tumor types. Data visualization and machine learning techniques including logistic regression, k-nearest neighbors, support vector machine, naïve Bayes, decision tree, random forest, and rotation forest were applied to this dataset. R, Minitab, and Python were chosen to be applied to these machine learning techniques and visualization. The paper aimed to make a comparative analysis using data visualization and machine learning applications for breast cancer detection and diagnosis. Diagnostic performances of applications were comparable for detecting breast cancers. Data visualization and machine learning techniques can provide significant benefits and impact cancer detection in the decision-making process. In this paper, different machine learning and data mining techniques for the detection of breast cancer were proposed. Results obtained with the logistic regression model with all features included showed the highest classification accuracy (98.1%), and the proposed approach revealed the enhancement in accuracy performances. These results indicated the potential to open new opportunities in the detection of breast cancer.

Efficient Pharmacokinetic Modeling Workflow With the MonolixSuite: A Case Study of Remifentanil.

MonolixSuite is a software widely used for model‐based drug development. It contains interconnected applications for data visualization, noncompartmental analysis, nonlinear mixed effect modeling, and clinical trial simulations. Its main assets are ease of use via an interactive graphical interface, computation speed, and efficient parameter estimation even for complex models. This tutorial presents a step‐by‐step pharmacokinetic (PK) modeling workflow using MonolixSuite, including how to visualize the data, set up a population PK model, estimate parameters, and diagnose and improve the model incrementally.

Argovis: A Web Application for Fast Delivery, Visualization, and Analysis of Argo Data

AbstractSince the mid-2000s, the Argo oceanographic observational network has provided near-real-time four-dimensional data for the global ocean for the first time in history. Internet (i.e., the “web”) applications that handle the more than two million Argo profiles of ocean temperature, salinity, and pressure are an active area of development. This paper introduces a new and efficient interactive Argo data visualization and delivery web application named Argovis that is built on a classic three-tier design consisting of a front end, back end, and database. Together these components allow users to navigate 4D data on a world map of Argo floats, with the option to select a custom region, depth range, and time period. Argovis’s back end sends data to users in a simple format, and the front end quickly renders web-quality figures. More advanced applications query Argovis from other programming environments, such as Python, R, and MATLAB. Our Argovis architecture allows expert data users to build their own functionality for specific applications, such as the creation of spatially gridded data for a given time and advanced time–frequency analysis for a space–time selection. Argovis is aimed to both scientists and the public, with tutorials and examples available on the website, describing how to use the Argovis data delivery system—for example, how to plot profiles in a region over time or to monitor profile metadata.

Application of Big Data Visualization in Urban Planning

During the past ten years, with the process of urbanization accelerates, urban population has increased sharply. Traffic congestion, noise pollution, garbage treatment and other problems, which make the traditional urban planning methods difficult to shoulder this heavy responsibility. At the same time, intelligent city and the construction of informationize is also developing rapidly, the practice and research of big data in urban planning industry are widely used. Large scale data visualization is one of the most important steps in big data. Not only can the data be expressed in visible form to improve the efficiency of data analysis, but also human-computer interaction can be carried out. To overcome these problems, this paper studies the problem of urban planning based on large scale data visualization. Firstly, it extends the concept and model of big data visualization to the significance and framework of applying big data visualization to urban planning. Then through the case of visualization of traffic data and environmental sound data, we can see that both quality and efficiency have been significantly improved. Furthermore in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potential important research directions for urban planning.

Introducing Undergraduates to Concepts of Survey Data Analysis

Increasingly students, particularly those in the social sciences, work with survey data collected through a more complex sampling method than a simple random sample. Failing to understand how to properly approach survey data can lead to inaccurate results. In this article, we describe a series of online data visualization applications and corresponding student lab activities designed to help students and teachers of statistics better understand survey design and analysis. The introductory and advanced materials presented are designed to focus on a conceptual understanding of survey data and provide an awareness of the challenges and potential misuse of survey data. Suggestions and examples of how to incorporate these materials are also included. Supplementary materials for this article are available online.

An Application of Data Visualization in Electronic Health Records

An Application of Data Visualization in Electronic Health Records Asikur Rahman, Sherrie Komiak Abstract Recent development in Electronic Health Record (HER) systems has resulted in producing data at an unprecedented rate. The complex, growing, and high-dimensional big data available in EHRs represent great opportunities and challenges for researchers and practitioners in the healthcare section. To deal with such a challenge, we can take the advantage of advanced technology like information visualization techniques. This paper presents an outline of how we will apply different visualization techniques such as zooming, focusing, multiple coordinated views (MCVs), and details on demand etc. on the e-health data to visualize the medical and health information in a very interactive way. The proposed application can help the doctors and medical students with their decision making. Doctors or medical students will be able to visualize the information based on their needs which will improve the decision accuracy and decision time as well. Full Text: PDF DOI: 10.15640/jmise.v7n2a1

数据可视化在非晶结构教学的应用

数据可视化在非晶结构教学的应用

Feature Extraction Methods in Quantitative Structure–Activity Relationship Modeling: A Comparative Study

Computational approaches for synthesizing new chemical compounds have resulted in a major explosion of chemical data in the field of drug discovery. The quantitative structure–activity relationship (QSAR) is a widely used classification and regression method used to represent the relationship between a chemical structure and its activities. This research focuses on the effect of dimensionality-reduction techniques on a high-dimensional QSAR dataset. Because of the multi-dimensional nature of QSAR, dimensionality-reduction techniques have become an integral part of its modeling process. Principal component analysis (PCA) is a feature extraction technique with several applications in exploratory data analysis, visualization and dimensionality reduction. However, linear PCA is inadequate to handle the complex structure of QSAR data. In light of the wide array of current feature-extraction techniques, we perform a comparative empirical study to investigate five feature-extraction techniques: PCA, kernel PCA, deep generalized autoencoder (dGAE), Gaussian random projection (GRP), and sparse random projection (SRP). The experiments are performed on a high-dimensional QSAR dataset, which comprises 6394 features. The transformed low-dimensional dataset is inputted into a deep learning classification model to predict a QSAR biological activity. Three approaches are adopted to validate and measure the proposed techniques: (i) comparing the performance of the classification models, (ii) visualizing the relationship (correlation) between features in the low-dimension Euclidean space, and (iii) validating the proposed techniques using an external dataset. To the best of our knowledge, this study is the first to investigate and compare the aforementioned feature-extraction techniques in QSAR modeling context. The results obtained provide invaluable insights regarding the behavior of different techniques with both negative and positive classes. With linear PCA as a baseline, we prove that the investigated techniques substantially outperform the baseline in multiple accuracy measures and demonstrate useful ways of extracting significant features.

Quasiconformal rectilinear map

This paper presents a novel method to compute the quasiconformal rectilinear map for a 2D polygonal subdivision, which keeps the original topology and best preserves the shape of the original input in the elasticity sense by the curve-driven quasiconformal mapping. We first automatically compute the straightening styles of the subdivision curves, which are then utilized to generate the optimal rectilinear map with the minimal harmonic energy. The quasiconformal rectilinear map is aesthetically pleasing for human perception, and hierarchically and progressively flexible for the design of spatial data structures. Based on the quasiconformal rectilinear map, an approach is given to compute the polyomino map. We evaluate the proposed structural maps in the data visualization application.

CroP-Coordinated Panel visualization for biological networks analysis.

CroP is a data visualization application that focuses on the analysis of relational data that changes over time. While it was specifically designed for addressing the preeminent need to interpret large scale time series from gene expression studies, CroP is prepared to analyze datasets from multiple contexts. Multiple datasets can be uploaded simultaneously and viewed through dynamic visualization models, which are contained within flexible panels that allow users to adapt the workspace to their data. Through clustering and the time curve visualization it is possible to quickly identify groups of data points with similar proprieties or behaviors, as well as temporal patterns across all points, such as periodic waves of expression. Additionally, it integrates a public biomedical database for gene annotation. CroP will be of major interest to biologists who seek to extract relations from complex sets of data. CroP is freely available for download as an executable jar at https://cdv.dei.uc.pt/crop/.

An interactive data visualisation application to investigate nosocomial transmission of infections.

Background: Healthcare-associated infections represent a major threat to patient, staff and visitor safety. Identification of episodes that are likely to have resulted from nosocomial transmission has important implications for infection control. Routinely collected data on ward admissions and sample dates, combined with pathogen genomic information could provide useful insights. We describe a novel, open-source, application for visualising these data, and demonstrate its utility for investigating nosocomial transmission using a case study of a large outbreak of norovirus infection. Methods: We developed the application using Shiny, a web application framework for R. For the norovirus case study, cases were defined as patients who had a faecal sample collected at the hospital in a winter season that tested positive for norovirus. Patient demographics and ward admission dates were extracted from hospital systems. Detected norovirus strains were genotyped and further characterised through sequencing of the hypervariable P2 domain. The most commonly detected sub-strain was visualised using the interactive application. Results: There were 156 norovirus-positive specimens collected from 107 patients. The most commonly detected sub-strain affected 30 patients in five wards. We used the interactive application to produce three visualisations: a bar chart, a timeline, and a schematic ward plan highlighting plausible transmission links. Visualisations showed credible links between cases on the elderly care ward. Conclusions: Use of the interactive application provided insights into transmission in this large nosocomial outbreak of norovirus, highlighting where infection control practices worked well or could be improved. This is a flexible tool that could be used for investigation of any infection in any hospital by interactively changing parameters. Challenges include integration with hospital systems for extracting data. Prospective use of this application could inform better infection control in real time.

Smart City System Design

Recent global smart city efforts resemble the establishment of electricity networks when electricity was first invented, which meant the start of a new era to sell electricity as a utility. A century later, in the smart era, the network to deliver services goes far beyond a single entity like electricity. Supplemented by a well-established Internet infrastructure that can run an endless number of applications, abundant processing and storage capabilities of clouds, resilient edge computing, and sophisticated data analysis like machine learning and deep learning, an already-booming Internet of Things movement makes this new era far more exciting. In this article, we present a multi-faceted survey of machine intelligence in modern implementations. We partition smart city infrastructure into application, sensing, communication, security, and data planes and put an emphasis on the data plane as the mainstay of computing and data storage. We investigate (i) a centralized and distributed implementation of data plane’s physical infrastructure and (ii) a complementary application of data analytics, machine learning, deep learning, and data visualization to implement robust machine intelligence in a smart city software core. We finalize our article with pointers to open issues and challenges.

GenomeGraphR: A user-friendly open-source web application for foodborne pathogen whole genome sequencing data integration, analysis, and visualization

Food safety risk assessments and large-scale epidemiological investigations have the potential to provide better and new types of information when whole genome sequence (WGS) data are effectively integrated. Today, the NCBI Pathogen Detection database WGS collections have grown significantly through improvements in technology, coordination, and collaboration, such as the GenomeTrakr and PulseNet networks. However, high-quality genomic data is not often coupled with high-quality epidemiological or food chain metadata. We have created a set of tools for cleaning, curation, integration, analysis and visualization of microbial genome sequencing data. It has been tested using Salmonella enterica and Listeria monocytogenes data sets provided by NCBI Pathogen Detection (160,000 sequenced isolates in 2018). GenomeGraphR presents foodborne pathogen WGS data and associated curated metadata in a user-friendly interface that allows a user to query a variety of research questions such as, transmission sources and dynamics, global reach, and persistence of genotypes associated with contamination in the food supply and foodborne illness across time or space. The application is freely available (https://fda-riskmodels.foodrisk.org/genomegraphr/).

Overcoming Limitations of the HoloLens for Use in Product Assembly

Augmented Reality (AR) is an emerging technology that could greatly increase training efficiency and effectiveness for assembly processes. In fact, studies show that AR may reduce time and errors by as much as 50%. While many devices are available to display AR content, the use of Head Mounted Displays (HMDs) is the most interesting for manufacturing and assembly as they free up the user’s hands. Due to the emerging nature of this technology, there are many limitations including input, field of view, tracking and occlusion. The work presented in this paper explores the use of the Microsoft HoloLens to deliver AR work instructions for product assembly. An AR assembly application was developed to guide a trainee through the assembly of a mock aircraft wing. To ensure accurate instructions were displayed, novel techniques were developed to mitigate the HoloLens’ tracking and display limitations. Additionally, a data visualization application was developed to validate the training session and explore trends through data collected from the HoloLens along with wearable physiological sensor data.

Analysis and Visualization Implementation of Medical Big Data Resource Sharing Mechanism Based on Deep Learning

With the development of information technology, the informationization of the medical industry is also constantly developing rapidly, and medical data is growing exponentially. In the context of “Big Data +”, people began to study the application of data visualization to medical data. Data visualization can make full use of the human sensory vision system to guide users through data analysis and present information hidden behind the data in an intuitive and easy-to-use manner. This paper first introduces the workflow of DBN, a deep learning algorithm, and summarizes the computational characteristics of the algorithm. The classification function is translated into an assembler using an instruction set-based assembly language, and the program is evaluated for performance. Secondly, based on the Hadoop ecosystem, this paper analyzes the BDMISS system for big data medical information resource sharing. Based on the system's requirements and functional positioning, from the medical information collection and sharing, data mining and knowledge management level, the big data medical service system is constructed. Based on the semantic network and ontology theory, big data mining technology and the design of “medical cloud”, the resource sharing mechanism is analyzed. Based on the Spring MVC framework, using Echarts, HCharts and other data visualization technology, according to the design of specific modules, the visualization and display of medical data is realized, which has certain promotion effect on the research and development of medical big data visualization analysis.

Проектування програми візуалізації земної поверхні з використанням алгебро-алгоритмічного інструментарію

One of the important challenges in meteorological forecasting is a computer visualization of prognosis data, in particular, three-dimensional modeling of terrain surface relief. One of the most popular software tools for developing data visualization applications is OpenGL, a cross-language, cross-platform application programming interface for rendering two-dimensional and three-dimensional vector graphics. Data visualization is a fairly complex task, therefore it is necessary to develop special tools intended for automated generation of source code for such tasks. The paper deals with the further development of previously developed algebra-algorithmic tools in the direction of automated design and synthesis of OpenGL programs. Automated construction of programs is implemented using a high-level language based on Glushkov’s system of algorithmic algebra. The approach is illustrated on designing a terrain surface relief visualization program for weather forecasting task.

3D GEOSPATIAL INDOOR NAVIGATION FOR DISASTER RISK REDUCTION AND RESPONSE IN URBAN ENVIRONMENT

Abstract. Disaster management for urban environments with complex structures requires 3D extensions of indoor applications to support better risk reduction and response strategies. The paper highlights the need for assessment and explores the role of 3D geospatial information and modeling regarding the indoor structure and navigational routes which can be utilized as disaster risk reduction and response strategy. The reviewed models or methods are analysed testing parameters in the context of indoor risk and disaster management. These parameters are level of detail, connection to outdoor, spatial model and network, handling constraints. 3D reconstruction of indoors requires the structural data to be collected in a feasible manner with sufficient details. Defining the indoor space along with obstacles is important for navigation. Readily available technologies embedded in smartphones allow development of mobile applications for data collection, visualization and navigation enabling access by masses at low cost. The paper concludes with recommendations for 3D modeling, navigation and visualization of data using readily available smartphone technologies, drones as well as advanced robotics for Disaster Management.

Epiviz Web Components: reusable and extensible component library to visualize functional genomic datasets

Interactive and integrative data visualization tools and libraries are integral to exploration and analysis of genomic data. Web based genome browsers allow integrative data exploration of a large number of data sets for a specific region in the genome. Currently available web-based genome browsers are developed for specific use cases and datasets, therefore integration and extensibility of the visualizations and the underlying libraries from these tools is a challenging task. Genomic data visualization and software libraries that enable bioinformatic researchers and developers to implement customized genomic data viewers and data analyses for their application are much needed. Using recent advances in core web platform APIs and technologies including Web Components, we developed the Epiviz Component Library, a reusable and extensible data visualization library and application framework for genomic data. Epiviz Components can be integrated with most JavaScript libraries and frameworks designed for HTML. To demonstrate the ease of integration with other frameworks, we developed an R/Bioconductor epivizrChart package, that provides interactive, shareable and reproducible visualizations of genomic data objects in R, Shiny and also create standalone HTML documents. The component library is modular by design, reusable and natively extensible and therefore simplifies the process of managing and developing bioinformatic applications.

Epiviz Web Components: reusable and extensible component library to visualize functional genomic datasets.

Interactive and integrative data visualization tools and libraries are integral to exploration and analysis of genomic data. Web based genome browsers allow integrative data exploration of a large number of data sets for a specific region in the genome. Currently available web-based genome browsers are developed for specific use cases and datasets, therefore integration and extensibility of the visualizations and the underlying libraries from these tools is a challenging task. Genomic data visualization and software libraries that enable bioinformatic researchers and developers to implement customized genomic data viewers and data analyses for their application are much needed. Using recent advances in core web platform APIs and technologies including Web Components, we developed the Epiviz Component Library, a reusable and extensible data visualization library and application framework for genomic data. Epiviz Components can be integrated with most JavaScript libraries and frameworks designed for HTML. To demonstrate the ease of integration with other frameworks, we developed an R/Bioconductor epivizrChart package, that provides interactive, shareable and reproducible visualizations of genomic data objects in R, Shiny and also create standalone HTML documents.The component library is modular by design, reusable and natively extensible and therefore simplifies the process of managing and developing bioinformatic applications.

HapTable: An Interactive Tabletop Providing Online Haptic Feedback for Touch Gestures

We present HapTable; a multi-modal interactive tabletop that allows users to interact with digital images and objects through natural touch gestures, and receive visual and haptic feedback accordingly. In our system, hand pose is registered by an infrared camera and hand gestures are classified using a Support Vector Machine (SVM) classifier. To display a rich set of haptic effects for both static and dynamic gestures, we integrated electromechanical and electrostatic actuation techniques effectively on tabletop surface of HapTable, which is a surface capacitive touch screen. We attached four piezo patches to the edges of tabletop to display vibrotactile feedback for static gestures. For this purpose, the vibration response of the tabletop, in the form of frequency response functions (FRFs), was obtained by a laser Doppler vibrometer for 84 grid points on its surface. Using these FRFs, it is possible to display localized vibrotactile feedback on the surface for static gestures. For dynamic gestures, we utilize the electrostatic actuation technique to modulate the frictional forces between finger skin and tabletop surface by applying voltage to its conductive layer. To our knowledge, this hybrid haptic technology is one of a kind and has not been implemented or tested on a tabletop. It opens up new avenues for gesture-based haptic interaction not only on tabletop surfaces but also on touch surfaces used in mobile devices with potential applications in data visualization, user interfaces, games, entertainment, and education. Here, we present two examples of such applications, one for static and one for dynamic gestures, along with detailed user studies. In the first one, user detects the direction of a virtual flow, such as that of wind or water, by putting their hand on the tabletop surface and feeling a vibrotactile stimulus traveling underneath it. In the second example, user rotates a virtual knob on the tabletop surface to select an item from a menu while feeling the knob's detents and resistance to rotation in the form of frictional haptic feedback.