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

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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.

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Data Availability Statement

The dataset is fully available at www.kaggle.com/drgilermo/nba-players-stats. The source code of SkyFlow is available at https://github.com/wooilkim/SkyFlow.

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Acknowledgements

The authors appreciate the valuable comments of the anonymous reviewers. This work was partly supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019H1D8A2105513), by the MSIT (Ministry of Science and ICT), Korea, under the ICT Creative Consilience program (IITP-2020-0-01819) supervised by the IITP (Institute for Information and Communications Technology Planning and Evaluation), and under the framework of international cooperation program managed by the National Research Foundation of Korea (No. NRF-2020K2A9A1A01095894).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Korea University, Seoul, 02841, South Korea

    Wooil Kim & Yon Dohn Chung

  2. Department of Electrical and Computer Engineering, Curtin University, Perth, WA, 6102, Australia

    Changbeom Shim

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  1. Wooil Kim
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Correspondence to Yon Dohn Chung.

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Kim, W., Shim, C. & Chung, Y.D. SkyFlow: A visual analysis of high-dimensional skylines in time-series. J Vis 24, 1033–1050 (2021). https://doi.org/10.1007/s12650-021-00758-y

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