SkyFlow: A visual analysis of high-dimensional skylines in time-series

Wooil Kim, Changbeom Shim, Yon Dohn Chung

Research output: Contribution to journalArticlepeer-review

Abstract

Abstract: Decision makers often find themselves in situations where they need to consider time-varying values for multi-criteria decision-making. Skyline queries are one of the most widely used methods of approaching multi-criteria decision-making problems because they reduce the size of search space by excluding inferior data. However, skylines in time-series data fluctuate with changes in attributes. Moreover, the number of skyline points increases as the number of dimensions increases, and the skyline query itself does not provide any ranking method. Thus, users are required to direct a considerable amount of effort into analyzing and finding the best selection. To address these issues, we propose SkyFlow, a visual analytical system for comparing time-varying data to facilitate the decision-making process. We apply two datasets in our system and describe scenarios to demonstrate the effectiveness of SkyFlow. In addition, we conduct a qualitative study to highlight the efficiency of our system in assisting users to compare candidates and make decisions involving time-series data. Graphic abstract: [Figure not available: see fulltext.]

Original languageEnglish
Pages (from-to)1033-1050
Number of pages18
JournalJournal of Visualization
Volume24
Issue number5
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Skyline in time-series data
  • Time-varying multi-criteria decision-making
  • Visual analytics

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Electrical and Electronic Engineering

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