98 research outputs found

    Statistical Anomaly Discovery Through Visualization

    Get PDF
    Developing a deep understanding of data is a crucial part of decision-making processes. It often takes substantial time and effort to develop a solid understanding to make well-informed decisions. Data analysts often perform statistical analyses through visualization to develop such understanding. However, applicable insight can be difficult due to biases and anomalies in data. An often overlooked phenomenon is mix effects, in which subgroups of data exhibit patterns opposite to the data as a whole. This phenomenon is widespread and often leads inexperienced analysts to draw contradictory conclusions. Discovering such anomalies in data becomes challenging as data continue to grow in volume, dimensionality, and cardinality. Effectively designed data visualizations empower data analysts to reveal and understand patterns in data for studying such paradoxical anomalies. This research explores several approaches for combining statistical analysis and visualization to discover and examine anomalies in multidimensional data. It starts with an automatic anomaly detection method based on correlation comparison and experiments to determine the running time and complexity of the algorithm. Subsequently, the research investigates the design, development, and implementation of a series of visualization techniques to fulfill the needs of analysis through a variety of statistical methods. We create an interactive visual analysis system, Wiggum, for revealing various forms of mix effects. A user study to evaluate Wiggum strengthens understanding of the factors that contribute to the comprehension of statistical concepts. Furthermore, a conceptual model, visual correspondence, is presented to study how users can determine the identity of items between visual representations by interpreting the relationships between their respective visual encodings. It is practical to build visualizations with highly linked views informed by visual correspondence theory. We present a hybrid tree visualization technique, PatternTree, which applies the visual correspondence theory. PatternTree supports users to more readily discover statistical anomalies and explore their relationships. Overall, this dissertation contributes a merging of new visualization theory and designs for analysis of statistical anomalies, thereby leading the way to the creation of effective visualizations for statistical analysis

    Faculty Of Education UNHI

    Get PDF
    Faculty Of Education UNH

    A Fast and Scalable System to Visualize Contour Gradient from Spatio-temporal Data

    Get PDF
    Changes in geological processes that span over the years may often go unnoticed due to their inherent noise and variability. Natural phenomena such as riverbank erosion, and climate change in general, is invisible to humans unless appropriate measures are taken to analyze the underlying data. Visualization helps geological sciences to generate scientific insights into such long-term geological events. Commonly used approaches such as side-by-side contour plots and spaghetti plots do not provide a clear idea about the historical spatial trends. To overcome this challenge, we propose an image-gradient based approach called ContourDiff. ContourDiff overlays gradient vector over contour plots to analyze the trends of change across spatial regions and temporal domain. Our approach first aggregates for each location, its value differences from the neighboring points over the temporal domain, and then creates a vector field representing the prominent changes. Finally, it overlays the vectors (differential trends) along the contour paths, revealing the differential trends that the contour lines (isolines) experienced over time. We designed an interface, where users can interact with the generated visualization to reveal changes and trends in geospatial data. We evaluated our system using real-life datasets, consisting of millions of data points, where the visualizations were generated in less than a minute in a single-threaded execution. We show the potential of the system in detecting subtle changes from almost identical images, describe implementation challenges, speed-up techniques, and scope for improvements. Our experimental results reveal that ContourDiff can reliably visualize the differential trends, and provide a new way to explore the change pattern in spatiotemporal data. The expert evaluation of our system using real-life WRF (Weather Research and Forecasting) model output reveals the potential of our technique to generate useful insights on the spatio-temporal trends of geospatial variables

    Intelligent Systems

    Get PDF
    This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

    Interactive exploration of millions of healthcare records in Brazil

    Get PDF
    A análise de dados de saúde é desafiadora devido ao seu grande volume, complexidade e heterogeneidade. Técnicas de visualização de dados interativos são indispensáveis para auxiliar a análise desses grandes sistemas de saúde. Nesta dissertação, propomos estudos de caso desenvolvidos a partir de dados do SUS, o Sistema Único de Saúde, um dos maiores sistemas públicos de saúde do mundo. Apresentamos protótipos de análise visual em uma estrutura de cubos de dados de última geração que oferece suporte à exploração visual interativa de milhões de registros. Demonstramos como a exploração de dados fornecida por nossos protótipos pode auxiliar as tarefas essenciais na análise de grandes dados de assistência médica, incluindo dados da COVID-19 no Brasil.The analysis of healthcare data is challenging due to its large volume, complexity, and heterogeneity. Interactive data visualization techniques are indispensable to support the analysis of such large healthcare systems. In this dissertation, we propose case studies developed for data from SUS, the Brazilian Unified Healthcare System, one of the largest public healthcare systems in the world. We present visual analytics prototypes on a state of-the-art datacube structure that supports the interactive visual exploration of millions of records. We demonstrate how the data exploration provided by our prototypes can help the essential tasks in analyzing big healthcare data, including data from COVID-19 in Brazil

    The Public Innovations Explorer: A Geo-Spatial & Linked-Data Visualization Platform For Publicly Funded Innovation Research In The United States

    Full text link
    The Public Innovations Explorer (https://sethsch.github.io/innovations-explorer/app/index.html) is a web-based tool created using Node.js, D3.js and Leaflet.js that can be used for investigating awards made by Federal agencies and departments participating in the Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) grant-making programs between 2008 and 2018. By geocoding the publicly available grants data from SBIR.gov, the Public Innovations Explorer allows users to identify companies performing publicly-funded innovative research in each congressional district and obtain dynamic district-level summaries of funding activity by agency and year. Applying spatial clustering techniques on districts\u27 employment levels across major economic sectors provides users with a way of examining patterns in the underlying economic activities of districts alongside Federally-funded innovation research activities taking place in a district. Finally, mathematical and dictionary-based text-mining techniques are used to derive district-level keyword details and provide users with access to some basic keyword stats for each district. Among other sources, the Explorer utilizes vocabulary sources from the European Commission, the United Nations and Leibniz Information Centre for Economics and builds on the National Institute of Health Office of Portfolio Analysis’s NLPre Pipeline available on Github to index keywords extracted from the text of grant records. The project seeks to contribute to work in research fields like scientometrics, economic geography, and in the nonprofit and philanthropy sector by developing and documenting data processing techniques and a user-interface fit for exploring geographic and thematic trends across grant datasets

    Survey on geographic visual display techniques in epidemiology: Taxonomy and characterization

    Get PDF
    Many works have been done on the topic of Geographic Visual Display with different objectives and approaches. There are studies to compare the traditional cartography techniques (the traditional term of Geographic Visual Display (GVD) without Human-Computer Interaction (HCI)) to Modern GIS which are also known as Geo-visualization, some literature differentiates and highlight the commonalities of features and architectures of different Geographic Visual Display tools (from layers and clusters to dot and color and more). Furthermore, with the existence of more advanced tools which support data exploration, few tasks are done to evaluate how those tools are used to handle complex and multivariate spatial-temporal data. Several test on usability and interactivity of tools toward user's needs or preferences, some even develop frameworks that address user's concern in a wide array of tasks, and others prove how these tools are able to stimulate the visual thought process and help in decision making or event prediction amongst decision-makers. This paper surveyed and categorized these research articles into 2 categories: Traditional Cartography (TC) and Geo-visualization (G). This paper will classify each category by their techniques and tasks that contribute to the significance of data representation in Geographic Visual Display and develop perspectives of each area and evaluating trends of Geographic Visual Display Techniques. Suggestions and ideas on what mechanisms can be used to improve and diversify Geographic Visual Display Techniques are provided at the end of this survey

    Visualisation of Large-Scale Call-Centre Data

    Get PDF
    The contact centre industry employs 4% of the entire United King-dom and United States’ working population and generates gigabytes of operational data that require analysis, to provide insight and to improve efficiency. This thesis is the result of a collaboration with QPC Limited who provide data collection and analysis products for call centres. They provided a large data-set featuring almost 5 million calls to be analysed. This thesis utilises novel visualisation techniques to create tools for the exploration of the large, complex call centre data-set and to facilitate unique observations into the data.A survey of information visualisation books is presented, provid-ing a thorough background of the field. Following this, a feature-rich application that visualises large call centre data sets using scatterplots that support millions of points is presented. The application utilises both the CPU and GPU acceleration for processing and filtering and is exhibited with millions of call events.This is expanded upon with the use of glyphs to depict agent behaviour in a call centre. A technique is developed to cluster over-lapping glyphs into a single parent glyph dependant on zoom level and a customizable distance metric. This hierarchical glyph repre-sents the mean value of all child agent glyphs, removing overlap and reducing visual clutter. A novel technique for visualising individually tailored glyphs using a Graphics Processing Unit is also presented, and demonstrated rendering over 100,000 glyphs at interactive frame rates. An open-source code example is provided for reproducibility.Finally, a novel interaction and layout method is introduced for improving the scalability of chord diagrams to visualise call transfers. An exploration of sketch-based methods for showing multiple links and direction is made, and a sketch-based brushing technique for filtering is proposed. Feedback from domain experts in the call centre industry is reported for all applications developed
    • …
    corecore