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Web-based Visualization and Querying of Food and Beverage Endorsements by Celebrities

Published: 28 July 2019 Publication History

Abstract

This paper explores the development of a web-based dashboard for the exploration of the nature of food and beverage endorsements by celebrities in the United States. While traditional methods of analysis require the translation of user requests to queries in one of the many languages that analysts use, the end user with no formal training in these languages is usually unable to get meaningful information without assistance. In this work, we propose the creation of a web-based dashboard that allows the user to use widgets to submit queries that can be executed on the dataset. To meet the design requirements of visualizing this data, two visual encodings are matched to the dataset under consideration, which illustrate the hierarchical relationships that exist between the various entities, within the confines of limited visual space. Finally, the dashboard concept is extended to a dataset larger than machine RAM using out-of-core processing that allows users to work on such data without resorting to distributed resources.

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Published In

cover image ACM Other conferences
PEARC '19: Practice and Experience in Advanced Research Computing 2019: Rise of the Machines (learning)
July 2019
775 pages
ISBN:9781450372275
DOI:10.1145/3332186
  • General Chair:
  • Tom Furlani
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 July 2019

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Author Tags

  1. Bokeh
  2. D3
  3. Dashboard
  4. Python
  5. Visualization

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PEARC '19

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Overall Acceptance Rate 133 of 202 submissions, 66%

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