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Annual Hawaii International conference on System Sciences (HICSS)

作者： Chul Hwee Kim John Hosking John Grundy Department of Computer Science University of Auckland Auckland New Zealand Department of Electrical and Computer Engineering University of Auckland Auckland New Zealand

We describe the evolution of a statistical survey design visual language from a standalone design-time modelling language into an environment supporting design, coordination, execution and publication of complex statistical surveys. This involved, firstly, elaboration of the notation to support additional requirements, notably in the area of task modelling. Secondly, tool support has been extended to allow association of model components with survey artefacts, such as data sets, metadata, and statistical package analysis procedures, with the ability to then execute elements of the survey design model to implement the survey analysis. This permits rapid exploration of statistical questions, together with the ability to publish both analysis results and the techniques and processes used, the latter in the form of executable Web services and generated documentation. Thirdly, we have undertaken a usability evaluation with a target end user sample that demonstrates strong satisfaction with the tool

关键词： data analysis Documentation Software packages Design engineering Packaging Web services Usability Programming visualization Probability

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Exploratory multilevel hot spot analysis: Australian taxation office case study

Conferences in Research and Practice in Information Technolo...

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conferences in Research and Practice in Information Technology Series 2007年 70卷 77-84页

作者： Denny Williams, Graham J. Christen, Peter Department of Computer Science The Australian National University Canberra 0200 Australia Faculty of Computer Science University of Indonesia Indonesia The Australian Taxation Office Australia

ISBN: (纸本)9781920682514

Population based real-life datasets often contain smaller clusters of unusual sub-populations. While these clusters, called 'hot spots', are small and sparse, they are usually of special interest to an analyst. In this paper we introduce a visual drill-down Self-Organizing Map (SOM)-based approach to explore such hot spots characteristics in real-life datasets. Iterative clustering algorithms (such as k-means) and SOM are not designed to show these small and sparse clusters in detail. The feasibility of our approach is demonstrated using a large real life dataset from the Australian Taxation Office. © 2007, Australian Computer Society, Inc.

关键词： data visualization

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Preliminary Usability Evaluation of PolyMeCo: A visualization Based Tool for Mesh analysis and Comparison

Preliminary Usability Evaluation of PolyMeCo: A Visualizatio...

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International conference on Geometric Modeling and Imaging, GMAI

作者： Beatriz Sousa Santos Samuel Silva Leonor Teixeira Carlos Ferreira Paulo Dias Joaquim Madeira IEETA/DETI University of Aveiro Portugal DEGEI University of Aveiro CIO University of Lisbon DEGEI University of Aveiro Portugal

An overall description of the methods used and the results obtained in the on-going evaluation of PolyMeCo a mesh analysis and comparison tool - is presented. We are trying to evaluate some aspects of both the user interface and the visualization techniques implemented. Heuristic evaluation, observation and querying techniques were used and produced encouraging preliminary results, which provided new ideas, as well as information, that will inform the development of a more usable version of PolyMeCo, including new functionality.

关键词： Usability data visualization Pipelines Performance evaluation Solid modeling User interfaces Testing Application software Computer architecture Collaboration

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Simulation of decays and secondary ion losses in a Betabeam decay ring

Simulation of decays and secondary ion losses in a Betabeam ...

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IEEE conference on Particle Accelerator

作者： F.W. Jones E. Wildner TRIUMF Vancouver Canada CERN Geneva Switzerland

Radioactive ions injected into the decay ring of a Betabeam neutrino facility will constitute a continuous source of decay products distributed around the ring. Secondary ions from beta decays will differ in charge state from the primary ions and will follow widely off- momentum orbits. In the racetrack configuration of the ring, they will be mismatched in the long straights and may acquire large amplitudes, but the great majority of losses will be in the arcs. We describe here a comprehensive model of ion decay, secondary ion tracking, and loss detection, which has been implemented in the tracking and simulation code Accsim. Methods have been developed to accurately follow ion trajectories at large momentum deviations as well as to detect their impact coordinates on vacuum chamber walls and possibly inside magnetic elements. Using secondary-ion data from Accsim and postprocessing with Mathematica, we have implemented a follow-on simulation in FLUKA with a 3D geometry of decay ring components and physics models for ion interactions in matter, allowing radiological studies and in particular the visualization and analysis of heat deposition in the dipole magnets which is a critical design factor for the ring. In our simulations we have also implemented absorber elements which are intended to localize the majority of losses outside of the dipoles. These studies provide estimates of ring performance (in terms of loss concentration and management), the effectiveness of absorbers, and the implications for successful superconducting dipole operation.

关键词： Superconducting magnets Solid modeling Neutrino sources Orbits Analytical models Geometry Physics Mathematical model data visualization Magnetic analysis

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User Study of Ontology Generation Tool

User Study of Ontology Generation Tool

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International conference on Information Technology Interfaces

作者： Ivana Ilijasic Misic Bozidar Kovacic Tamara Mohoric Dunja Mladenic Blaz Fortuna Marko Grobelnik Faculty of Arts and Sciences Rijeka Croatia Jozef Stefan Institute Ljubljana Slovenia

ISBN: (纸本)9537138097

We present design and results of a user study undertaken in order to evaluate ontology generation process. We have applied our study to an example tool for semi-automatic ontology generation OntoGen that covers different stages of the underlying process. In-depth analysis of the user experience delivered valuable insights into the requirements for the further system development.

关键词： Ontologies Art Feedback Cognition Psychology Testing Usability Software tools Technological innovation data visualization

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Graphical Tools for FID

Graphical Tools for FID

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conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： C. Z. Janikow L. Finney Department of Mathematics and Computer Science University of Missouri Saint Louis USA

FID3.2 is a public software tool for supervised classification, implementing the popular and efficient recursive partitioning technique of decision trees, while utilizing fuzzy representation and approximate reasoning for dealing with noise and language uncertainty. It is loosely based on the ID3 decision tree algorithm. It handles nominal, continuous, and linguistic attributes and classes. It can also operate with noisy and unknown features, both in training and in testing data. For continuous attributes that are not pre-partitioned, the system generates fuzzy partitioning using either a top-down or a bottom-up method. Finally, FID uses a number of inferences from two classes: set-based and exemplar-based. Recently, we have implemented a GUI interface to work with FID3.2. The interface provides graphical visualization of data, partitions, and the tree, and it also allows limited graphical manipulations. This paper describes the GUI interface.

关键词： Decision trees Graphical user interfaces Software tools Classification tree analysis Fuzzy reasoning Uncertainty Partitioning algorithms Testing Fuzzy systems data visualization

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Understanding data Science Instruction in Multiple STEM Disciplines

Understanding Data Science Instruction in Multiple STEM Disc...

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2021 ASEE Virtual Annual conference, ASEE 2021

作者： Snyder, Caitlin Asamen, Dawit M. Naseri, Mohammad Yunus Aryal, Niroj Biswas, Gautam Dubey, Abhishek Henrick, Erin Hotchkiss, Erin R. Jha, Manoj K. Jiang, Steven X. Kern, Emily C. Lohani, Vinod K. Marston, Landon Todd Vanags, Christopher P. Xia, Kang Vanderbilt University United States North Carolina Agricultural and Technical State University United States Virginia Polytechnic Institute and State University United States

As technology advances, data driven work is becoming increasingly important across all disciplines. data science is an emerging field that encompasses a large array of topics including data collection, data preprocessing, data visualization, and data analysis using statistical and machine learning methods. As undergraduates enter the workforce in the future, they will need to "benefit from a fundamental awareness of and competence in data science"[9]. This project has formed a research practice partnership that brings together STEM+C instructors and researchers from three universities and an education research and consulting group. We aim to use high frequency monitoring data collected from real-world systems to develop and implement an interdisciplinary approach to enable undergraduate students to develop an understanding of data science concepts through individual STEM disciplines that include engineering, computer science, environmental science, and biology. In this paper, we perform an initial exploratory analysis on how data science topics are introduced into the different courses, with the ultimate goal of understanding how instructional modules and accompanying assessments can be developed for multidisciplinary use. We analyze information collected from instructor interviews and surveys, student surveys, and assessments from five undergraduate courses (243 students) at the three universities to understand aspects of data science curricula that are common across disciplines. Using a qualitative approach, we find commonalities in data science instruction and assessment components across the disciplines. This includes topical content, data sources, pedagogical approaches, and assessment design. Preliminary analyses of instructor interviews also suggest factors that affect the content taught and the assessment material across the five courses. These factors include class size, students' year of study, students' reasons for taking class, and students' background e

关键词： Students

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Finding the Needle in a Haystack: Using Treemaps to Analyze Supplier Performance

Finding the Needle in a Haystack: Using Treemaps to Analyze ...

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Portland International conference on Management of Engineering and Technology (PICMET)

作者： Larry A. Mallak David M. Lyth Betsy M. Aller Juhani Engelberg Western Michigan University Kalamazoo MI USA Kohler Company Kohler WI USA

Supplier evaluation is critical to managing an organization's supply base. Key to that evaluation is portraying the information graphically. Organizations with large supply bases find traditional methods are overwhelmed with large datasets. A tier 1 automotive supplier's quality system tracked eight performance measures for its many divisions and plants within the divisions. Each plant had a multitude of suppliers. Even when pivot tables, and pivot charts were used, the effective representation of that information wasn't accomplished through spreadsheets. Treemapping is a data visualization technology that makes efficient use of space and allows very large hierarchies to be displayed in their entirety. It sorts data using a user-defined hierarchy and displays data to meet the user's needs and decision making style. This paper provides a case study on the use of treemaps as a supplier performance rating system. The system was presented to users of the Excel-based system and their perceptions and evaluations of the treemap system were analyzed. They found the treemap system provided them with better information, made their job easier, and allowed for a full representation of supplier performance. Users were also appreciative of the ability to "drill down" on details of specific suppliers.

关键词： Needles Performance analysis Tree graphs Displays Automotive engineering Space technology Decision making Application software data visualization Production

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Visual Pattern Recognition Method Based on Optimized Parallel Coordinates

Visual Pattern Recognition Method Based on Optimized Paralle...

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IEEE International conference on Integration Technology (ICIT)

作者： Yonghong Xu Wenxue Hong Xin Li Jialin Song Department of Biomedical Engineerin University of Yanshan Qinhuangdao Hebei China

A visual pattern recognition method based on optimized parallel coordinates is proposed in this paper. We first introduce the traditional theory of parallel coordinates and indicate that parallel coordinates has a potential for classification tasks due to its projective transformation interpretation. Nevertheless, some optimization is needed. The main aim of optimization is to hide the valueless information and reveal the most valuable information for classification. Three interaction operations are proposed to do this work. We demonstrate by several examples how to solve some pattern recognition problems graphically, we also point out that some start-of-the-art classification techniques such as Fisher linear discriminant analysis, decision trees and support vector machines are related to and can be visualized by this method. By cooperation and union of this method with some machine learning and human-machine interaction techniques into a system, the black boxes of some pattern recognition techniques are expected to be opened up, the original data, the process of classification and final results will be more transparency and understandable.

关键词： Pattern recognition Optimization methods Classification tree analysis Linear discriminant analysis Decision trees Support vector machines Support vector machine classification data visualization Machine learning Man machine systems

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TIBOR: A Resource-bounded Information Foraging Agent for Visual Analytics 07

TIBOR: A Resource-bounded Information Foraging Agent for Vis...

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IEEE/WIC/ACM International conference on Intelligent Agent Technology (IAT)

作者： Dingxiang Liu Anita Raja Jayasri Vaidyanath Department of Software and Information Systems University of North Carolina Charlotte Charlotte NC USA

ISBN: (纸本)9780769530277

Visual analytics is the science of applying reasoning and analysis techniques to large, complex real-world data for problem solving using visualizations. Real world knowledge gathering and investigative tasks are very complex because the problem-solving context is constantly evolving, and the data may be incomplete, unreliable and/or conflicting. We describe a mixed-initiative reasoning agent that will assist investigative analysts to choose from and reason about enormous databases of text, imagery, video and Webcast. This agent leverages sequential decision making and an AI blackboard system to support hypothesis tracking and validation in a highly uncertain environment. Resource-bounded control mechanisms enable the agent to reason about the uncertainty. We have also designed a user interface that will enable analysts to gather and sift large amounts of evidence and to collaborate with and, where necessary, to control the agent.

关键词： Visual analytics Problem-solving data visualization Image analysis Image databases Visual databases Decision making Artificial intelligence Uncertainty User interfaces

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