VDS'21: Visualization in Data Science

VDS'21: Visualization in Data Science

Abstract

Data science is the practice of deriving insight from data, enabled by modeling, computational methods, interactive visual analysis, and domain-driven problem solving. Data science draws from methodology developed in such fields as applied mathematics, statistics, machine learning, data mining, data management, visualization, and HCI. It drives discoveries in business, economy, biology, medicine, environmental science, the physical sciences, the humanities and social sciences, and beyond. Machine learning and data mining and visualization are integral parts of data science, and essential to enable sophisticated analysis of data. Nevertheless, both research areas are currently still rather separated and investigated by different communities rather independently. The goal of this workshop is to bring researchers from both communities together in order to discuss common interests, to talk about practical issues in application-related projects, and to identify open research problems. This summary gives a brief overview of the ACM KDD Workshop on Visualization in Data Science (VDS at ACM KDD and IEEE VIS), which will take place virtually on Aug 14-18, 2021 (Held in conjunction with KDD'21). The workshop website is available at: http://www.visualdatascience.org/2021/

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Authors
  • Plant, Claudia
  • Ottley, Alvitta
  • Gou, Liang
  • Möller, Torsten
  • Perer, Adam
  • Lex, Alexander
  • Shao, Junming
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Other)
Event Title
KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14-18, 2021
Divisions
Data Mining and Machine Learning
Visualization and Data Analysis
Event Location
Singapore, Virtual
Event Type
Workshop
Event Dates
14.-18.08.2021
Series Name
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
ISSN/ISBN
978-1-4503-8332-5
Publisher
ACM
Page Range
pp. 4149-4150
Date
14 August 2021
Official URL
https://doi.org/10.1145/3447548.3469466
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