@misc{627,
  keywords = {information visualization, Set},
  author = {Martin Wortschack},
  title = {A scalable visualization of set-typed data},
  abstract = {<p>In information visualization set-typed data refers to datasets that represent element-set memberships, such as which countries (sets) produce a certain product (element), or which genres (sets) a movie (element) belongs to. Set-typed data appears in various forms and can serve as a data models in various data analysis scenarios. One of the main challenges in the context of set-typed data visualization is scalability. Traditionally, Euler and Venn diagrams count as two of the most popular set visualizations that depict the concepts from set theory. However, despite the widespread usage of these diagrams across several scientific fields they lack the ability of visualizing more than three sets without becoming too complex. This limits their applicability to data analysis scenarios that involve hundreds of sets such as the world countries. Besides Euler and Venn diagrams a variety of visualization techniques for set-typed data has been developed over the past. Typically, existing techniques scale well with either an increasing number of elements or an increasing number of sets. The goal of this thesis is to develop a set visualization technique which offers high scalability in both the number of sets and elements. The proposed technique, called Scets, employs different aggregations of set-type data, and uses a matrix layout to visualize the aggregated information. Furthermore, it allows users to explore the aggregated information interactively. The implemented prototype uses modern web technologies which make the visualization both able to handle a large amount of data using server-side backend, and accessible to a wide range of users using web-based frontend. Two different use cases demonstrate how the proposed visualization technique helps to investigate real-world data and enables users in an intuitive way to  reveal several patterns which could not be easily detected by other visualization techniques.</p>},
  year = {2016},
  journal = {Institute of Visual Computing and Human-Centered Technology},
  pages = {89},
  publisher = {TU Wien},
  address = {Vienna},
  doi = {10.34726/hss.2016.27526},
}