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Watson B.,North Carolina State University | Setlur V.,Tableau Software
MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct | Year: 2015

Data visualization has become an inherent part of our daily lives - whether it is viewing the latest weather on a map, or the current company stock price. As people shift to mobiles as their primary source of information, we are faced with exploring new visuals and interfaces that are optimized for small screens and constrained input modalities. Source


Talbot J.,Tableau Software | De Vito Z.,Stanford University | Hanrahan P.,Stanford University
Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) | Year: 2014

and statistical computing. In these languages, vectors have both dynamic type and dynamic length, making static generation of efficient machine code difficult. In this paper, we describe a tracebased just-in-time compilation strategy that performs partial length specialization of dynamically typed vector code. This selective specialization is designed to avoid excessive compilation overhead while still enabling the generation of efficient machine code through length-based optimizations such as vector fusion, vector copy elimination, and the use of hardware SIMD units. We have implemented our approach in a virtual machine for a subset of R, a vector-based statistical computing language. In a variety of workloads, containing both scalar and vector code, we show near autovectorized. Copyright © 2014 ACM 978-1-4503-2937-8/14/06. 15.00. Source


Setlur V.,Tableau Software | Mackinlay J.D.,Seattle Software
Conference on Human Factors in Computing Systems - Proceedings | Year: 2014

Authors use icon encodings to indicate the semantics of categorical information in visualizations. The default icon libraries found in visualization tools often do not match the semantics of the data. Users often manually search for or create icons that are more semantically meaningful. This process can hinder the flow of visual analysis, especially when the amount of data is large, leading to a suboptimal user experience. We propose a technique for automatically generating semantically relevant icon encodings for categorical dimensions of data points. The algorithm employs natural language processing in order to find relevant imagery from the Internet. We evaluate our approach on Mechanical Turk by generating large libraries of icons using Tableau Public workbooks that represent real analytical effort by people out in the world. Our results show that the automatic algorithm does nearly as well as the manually created icons, and particularly has higher user satisfaction for larger cardinalities of data. Source


Lin S.,Stanford University | Fortuna J.,Stanford University | Kulkarni C.,Stanford University | Stone M.,Tableau Software | Heer J.,Stanford University
Computer Graphics Forum | Year: 2013

We introduce an algorithm for automatic selection of semantically-resonant colors to represent data (e.g., using blue for data about "oceans", or pink for "love"). Given a set of categorical values and a target color palette, our algorithm matches each data value with a unique color. Values are mapped to colors by collecting representative images, analyzing image color distributions to determine value-color affinity scores, and choosing an optimal assignment. Our affinity score balances the probability of a color with how well it discriminates among data values. A controlled study shows that expert-chosen semantically-resonant colors improve speed on chart reading tasks compared to a standard palette, and that our algorithm selects colors that lead to similar gains. A second study verifies that our algorithm effectively selects colors across a variety of data categories. © 2013 The Author(s) Computer Graphics Forum © 2013 The Eurographics Association and Blackwell Publishing Ltd. Source


Dasgupta A.,New York University | Kosara R.,Tableau Software | Gosink L.,Pacific Northwest National Laboratory
Computer Graphics Forum | Year: 2015

Observing interactions among chemical species and microorganisms in the earth's sub-surface is a common task in the field of geology. Bioremediation experiments constitute one such class of interactions which focus on getting rid of pollutants through processes such as carbon sequestration. The main goal of scientists' observations is to analyze the dynamics of the chemical reactions and understand how they collectively affect the carbon content of the soil. In our work, we extract the high-level goals of geologists and propose a visual analytics solution which helps scientists in deriving insights about multivariate, temporal behavior of these chemical species. Specifically, our key contributions are the following: i) characterization of the domain-specific goals and their translation to exploratory data analysis tasks, ii) developing an analytical abstraction in the form of perceptually motivated screen-space metrics for bridging the gap between the tasks and the visualization, and iii) realization of the tasks and metrics in the form of VIMTEX, which is a set of coordinated multiple views for letting scientists observe multivariate, temporal relationships in the data. We provide several examples and case studies along with expert feedback for demonstrating the efficacy of our solution. © 2015 The Author(s) Computer Graphics Forum © 2015 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. Source

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