Evaluation of 1D Selection Techniques for Mobile Visualizations

This paper focuses on interactive data visualization techniques and their applications in healthcare systems. Interactive data visualization is a collection of techniques translating data from its numeric format to graphic presentation dynamically for easy understanding and visual impact. Compared to conventional static data visualization techniques, interactive data visualization techniques allow users to self-explore the entire data set by instant slice and dice, quick switching among multiple data sources. Adjustable granularity of interactive data visualization allows for both detailed micro information and aggregated macro information displayed in a single chart. Animated transition adds extra visual impact that describes how system transits from one state to another. When applied to healthcare system, interactive visualization techniques are useful in areas such as information integration, flow or trajectory presentation and location related visualization, etc. In this paper, three case studies are shared to illustrate how interactive data visualization techniques are applied to various aspects of healthcare systems. The first case study shows a pathway visualization representing longitudinal disease progression of a patient cohort. The second case study shows a dashboard profiling different patient cohorts from multiple perspectives. The third case study shows an interactive map illustrating patient geographical distribution at adjustable granularity. All three case studies illustrate that interactive data visualization techniques help quick information access, fast knowledge sharing and better decision making in healthcare system.

The two visual systems hypothesis in neuroscience suggests that pointing without visual feedback may be less affected by spatial visual illusions than cognitive interactions such as judged target location. Our study examined predictions of this theory for target localization on a large-screen display. We contrasted pointing interactions under varying levels of visual feedback with location judgments of targets that were surrounded by an offset frame. As predicted by the theory, the frame led to systematic errors in verbal report of target location but not in pointing without visual feedback for some participants. We also found that pointing with visual feedback produced a similar level of error as location judgments, while temporally lagged visual feedback appeared to reduce these errors somewhat. This suggests that pointing without visual feedback may be a useful interaction technique in situations described by the two visual systems literature, especially with large-screen displays and immersive environments.

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.

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.

In the context of the deepening implementation of the principles of Output-oriented Education (OBE) in engineering education, the analysis of course goal achievement plays a crucial role in enhancing teaching quality. Aiming at the shortcomings of traditional analytical methods in modeling assessment indicators and diagnosing teaching improvements, this study constructs an analysis system for course goal achievement that integrates the Analytic Hierarchy Process (AHP), weighted comprehensive evaluation models, and data visualization techniques. This research scientifically quantifies the weights of assessment indicators by using AHP. It establishes a nonlinear weighted comprehensive evaluation model to depict the relationships among dimensions such as "theoretical knowledge" and "practical skills", breaking through the independence assumption of traditional linear models. Additionally, it designs a structured processing workflow for multi-source performance data. By integrating multidimensional data from regular performance, final assessments, and extension training according to their weights, it forms a quantitative calculation framework for the achievement of sub-goals and overall goals. Moreover, a data visualization module is developed to intuitively present the achievement status of each goal and individual student differences through bar charts, scatter plots, etc. Taking the teaching data of 116 students in the course "Digital Signal Processing A" at North China University of Science and Technology as a sample, the effectiveness of this system in quantitatively evaluating Course Goal 1 (achievement degree of 0.73), Goal 2 (achievement degree of 0.73), and Goal 3 (achievement degree of 0.72) has been verified. The relevant results provide data support for optimizing teaching content (such as enhancing the integration of theory and practice) and personalized guidance (such as focusing on students with weaker performance). Practice shows that the system provides a replicable technical solution for the assessment of course goal achievement in colleges and universities through the closed-loop design of "indicator weighting--achievement calculation--visual feedback". Its data-driven precision diagnosis mechanism can effectively contribute to the implementation of OBE and the continuous improvement goal under the concept of "student-centered".

<p>Over the last few years, the amount of large and complex data in the public domain has increased enormously and new challenges arose in the representation, analysis and visualization of such data. Considering the number of space missions that provided and will provide remote sensing data, there is still the need of a system that can be dispatched in several remote repositories and being accessible from a single client of commodity hardware.</p><p>To tackle this challenge, at the DLR Institute for Software Technology we have defined a dual backend frontend system, enabling the interactive analysis and visualization of large-scale remote sensing data. The basis for all visualization and interaction approaches is CosmoScout VR, a visualization tool developed internally at DLR, and publicly available on Github, that allows the visualization of complex planetary data and large simulation data in real-time. The dual component of this system is based on an MPI framework, called Viracocha, that enables the analysis of large data remotely, and allows the efficient network usage about sending compact and partial results for interactive visualization in CosmoScout as soon as they are computed.</p><p>A node-based interface is defined within the visualization tool, and this lets a domain expert to easily define customized pipelines for processing and visualizing the remote data. Each “node” of this interface is either linked with a feature extraction module, defined in Viracocha, or to a rendering module defined directly in CosmoScout. Being this interface completely customizable by a user, multiple pipelines can be defined over the same dataset to enhance even more the visualization feedback for analysis purposes.</p><p>Being an ongoing project, on top of these tools, as a novel strategy in EO data processing and visualization, we plan to define and implement strategies based on Topological Data Analysis (TDA). TDA is an emerging set of technique for processing the data considering its topological features. These include both the geometric information associated to a point, as well all the non-geometric scalar values, like temperature and pressure, to name a few, that can be captured during a monitoring mission. One of the major theories behind TDA is Discrete Morse Theory, that, given a scalar value, is used to define a gradient on such function, extract the critical points, identify the region-of-influence of each critical point, and so on. This strategy is parameter free and enables a domain scientist to process large datasets without a prior knowledge of it.</p><p>An interesting research question, that it will be investigated during this project is the correlation of changes of critical points at different time steps, and the identification of deformation (or changes) across time in the original dataset.</p>

In this study, an experiment is conducted to measure the performance in speed and accuracy of interactive visualizations. A platform for interactive data visualizations was implemented using Django, D3, and Angular. Using this platform, a questionnaire was designed to measure a difference in performance between interactive and noninteractive data visualizations. In this questionnaire consisting of 12 questions, participants were given tasks in which they had to identify trends or patterns. Other tasks were directed at comparing and selecting algorithms with a certain outcome based on visualizations. All tasks were performed on high content screening data sets with the help of visualizations. The difference in time to carry out tasks and accuracy of performance was measured between a group viewing interactive visualizations and a group viewing noninteractive visualizations. The study shows a significant advantage in time and accuracy in the group that used interactive visualizations over the group that used noninteractive visualizations. In tasks comparing results of different algorithms, a significant decrease in time was observed in using interactive visualizations over noninteractive visualizations.

ABSTRACT: Many companies today utilize interactive data visualization to present accounting information to external users on their investor relations websites and to internal users in applications such as enterprise resource planning, Balanced Scorecard, network security, and fraud detection systems. We develop a taxonomy for examining the current state of interactive data visualization research related to accounting decision making. We organize our review around three themes: the relationship between task characteristics and interactive data visualization techniques, the relationship between decision maker characteristics and interactive data visualization techniques, and the impact of interactive data visualization techniques on decision processes and outcomes. The review categorizes relevant research, describes the research questions addressed, and suggests avenues for further research.

Intuitive and efficient interaction techniques are a fundamental component of most visualization tools. The integration of interaction techniques into visual representations (and automatic analysis methods in visual analytics) supports the human-information discourse and can be realized in various ways. But we also have to take care that our interaction and visual representation techniques are validated in order to get a clear understanding of their efficiency and usability. In this talk, I will explore current and identify future trends in the development of novel interaction and evaluation techniques for information visualization and related fields. Here, I especially want to highlight recent findings in the use of brain-computer interfaces to adapt and evaluate visualizations.

Background: A key strength of population sciences and epidemiology is the breadth and depth of the data these studies, especially prospective cohorts, collect over time. However, publications from and websites of these studies tend to present collapsed or aggregate data in traditional tables, often underestimating the true complexity of information in those data. One goal of the NIH Strategic Plan for Data Science is to “support the development and dissemination of advanced data management, analytics, and visualization tools.” We recently developed a robust, innovative, and scalable data visualization environment for the California Teachers Study (CTS), a prospective cancer epidemiology cohort. Methods: We identified the need to eliminate the practice of storing CTS data in a collection of disparate data silos and constructed a data warehouse to compile two decades of study data, eliminating additional harmonization steps. We then developed a data commons, a remotely accessible environment where cohort data could be utilized by our researchers and study collaborators. This allowed questionnaire-based data, cancer registry information, and hospitalization and mortality data from our study participants to be easily accessed in a secure environment. Serving as a single unified source of information, the data warehouse, coupled with the industry-leading data visualization tool Tableau, was configured using the following key epidemiologic cohort activities: reports, cohort selection, calculated values, and exploring and transforming data variables for analysis. Results: With the ability to sort and filter on specific covariates we can define subsets of populations for cohort selection and endpoint-specific analysis. Using interactive reports with drags and drops, and creating calculated variables within Tableau, gives us the ability to visualize time between event data, such as time from study entry to a cancer diagnosis, multiple cancer diagnoses, hospitalizations, or mortality without requiring programmatic back-end coding. Conclusion: The creation of a data warehouse and data commons has given the CTS the ability to visualize information about the demographics and health-related patterns of our study participants, leading to faster answers to complex questions about our data. The interactive data visualizations that we have created allow users the ability to identify new patterns easily, and drill down into the data for a more detailed look. Defining analytic populations through cohort selection and adding geospatial data to our data warehouse has already resulted in the discovery of new patterns that had been unidentified before—simply by providing the same information but utilizing a more accessible and innovative approach to presenting it. As epidemiology uses and shares more of its data, it’s essential to help the widest possible community understand those data in detail. Data visualization can provide specific benefits to key, essential population sciences research activities. Citation Format: Nadia T. Chung, James V. Lacey Jr., Emma Spielfogel, Paul Hughes, Sandeep Chandra, Elena Martinez, Jennifer Benbow. Opportunities for and benefits of incorporating data visualization in population sciences [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A01.

The paper investigates the use of dashboards and data visualizations as a teaching tools in accounting units. Accounting has a growing demand for data analytics and visualization and current graduates usually lack understanding and skills in this area. The paper addresses this gap by introducing dashboards and data visualizations in teaching accounting units. Approaching visualization as a cognitive tool, the study develops a model aligning conceptual blocks in the accounting domain with the dashboard visualization environment. The proposed model is instantiated in the undergraduate and postgraduate accounting courses. The model is evaluated using a survey and a focus group with accounting academics, graduates and industry representatives. The paper extends the information technology literature on visualization and demonstrates the use of dashboards and data visualizations in the domain of accounting education. The paper also contributes to the education literature by extending the project-based approach to analyse visualization environment as a cognitive tool in accounting education. The paper suggests directions on implementation of visualization in accounting reporting and accounting education and provides recommendations for researchers in those areas.

Several Visualization techniques have been applied in traffic information area. This work is the basic research for applying of interactive visualization techniques when the traffic information is provided to users. Design of interactive visualization techniques were applied to traffic information services. Previous studies for information visualization with interactive visualization techniques were investigated, and the traffic information services currently provided were analyzed. There are many types of interactive visualization techniques, but this study is mainly focused on selecting, querying, linking, filtering and rearranging techniques. The users can obtain the proper and more suitable information for theie needs, because each interactive visualization techniques support interaction between information and users. The traffic information which has one of interactive techniques can be more effective for decision making and utilization. More attention is given to interactive visualization of data and information techniques in transportation field. By this research, it is expected that traffic information services is more effective and can be a foundation work for various interactive visualization techniques in traffice inforamtion.

We propose an interactive visualization approach to finding a mathematical model for a real world process, commonly known in the field of control theory as system identification. The use of interactive visualization techniques provides the modeller with instant visual feedback which facilitates the model validation process. When working interactively with such large data sets, as are common in system identification, methods to handle this data efficiently are required. We are developing approaches based on data streaming to meet this need.

The world is full of data which is increasing by leaps and bounds. In health care, big data is becoming common with increased electronic health data accumulation and/or accessibility to public data previously held under lock and key. At the same time, health data visualization applications have become popular over recent years. Against this background, a review was done to summarize the application of data visualization in public health & the challenges faced. Peer-reviewed original research articles and review articles searched in Google Scholar and Pubmed databases that were indexed in the last ten years period, using the keywords “Big data” or “data visualization” or “Interactive visualization techniques.” Other related information in books, blogs, and published documents were searched in Google search engine using the same keywords. Contents from the downloaded documents were presented and discussed under three headings viz. (a) the visualizations that are still current and how they have evolved further, (b) tools or methods that can be used by end-users to make their own modifications, (c) the platforms to disseminate them. Usage of different plots in public health is explained with suitable examples using the data from public health datasets. From the discussion it can be understood that when big data is visualized well, it can identify implementation gaps and disparities and accelerate implementation strategies to reach the population groups in most need for interventions. As health administrator may come from diverse specialties, robust training and career development for big data in public health is the need of the hour.

Balloon selection is a 3D interaction technique that is modeled after the real world metaphor of manipulating a helium balloon attached to a string. Balloon selection allows for precise 3D selection in the volume above a tabletop surface by using multiple fingers on a multi-touch-sensitive surface. The 3DOF selection tasks is decomposed in part into a 2DOF positioning task performed by one finger on the tabletop in an absolute 2D Cartesian coordinate system and a 1DOF positioning task performed by another finger on the tabletop in a relative 2D polar coordinate system. We have evaluated balloon selection in a formal user study that compared it to two well-known interaction techniques for selecting a static 3D target: a 3DOF tracked wand and keyboard cursor keys. We found that balloon selection was significantly faster than using cursor keys and had a significantly lower error rate than the wand. The lower error rate appeared to result from the user's hands being supported by the tabletop surface, resulting in significantly reduced hand tremor and arm fatigue.