Skip to main content
SpringerLink
Log in

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

  • Regular Paper
  • Published:
Journal of Visualization Aims and scope Submit manuscript

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

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

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.

References

  • Bagajewicz M, Cabrera E (2003) Pareto optimal solutions visualization techniques for multiobjective design and upgrade of instrumentation networks. Ind Eng Chem Res 42(21):5195–5203

    Article  Google Scholar 

  • Borzsony S, Kossmann D, Stocker K (2001) The skyline operator. In: Proceedings 17th international conference on data engineering. IEEE, pp 421–430

  • Bostock M (2019) D3.js. https://d3js.org. Accessed 2020

  • Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley, London

    MATH  Google Scholar 

  • Chen S, Amid D, Shir OM, Limonad L, Boaz D, Anaby-Tavor A, Schreck T (2013) Self-organizing maps for multi-objective pareto frontiers. In: 2013 IEEE Pacific Visualization Symposium (PacificVis). IEEE, pp 153–160

  • Choi H, Jung H, Lee KY, Chung YD (2013) Skyline queries on keyword-matched data. Inf Sci 232:449–463

    Article  Google Scholar 

  • Choi W, Liu L, Yu B (2012) Multi-criteria decision making with skyline computation. In: 2012 IEEE 13th international conference on information reuse and integration (IRI). IEEE, pp 316–323

  • Chou JK, Wang Y, Ma KL (2016) Privacy preserving event sequence data visualization using a sankey diagram-like representation. In: SIGGRAPH ASIA 2016 symposium on visualization. ACM, p 1

  • Cox TF, Cox MA (2000) Multidimensional scaling. Chapman and hall/CRC, London

  • Cuba N (2015) Research note: Sankey diagrams for visualizing land cover dynamics. Landsc Urban Plan 139:163–167

    Article  Google Scholar 

  • Django Software Foundation (2005) Django. https://www.djangoproject.com. Accessed 2020

  • Edsall RM (2003) The parallel coordinate plot in action: design and use for geographic visualization. Comput Stat Data Anal 43(4):605–619

    Article  MathSciNet  Google Scholar 

  • Ellis G, Dix A (2006) Enabling automatic clutter reduction in parallel coordinate plots. IEEE Trans Vis Comput Graph 12(5):717–724

    Article  Google Scholar 

  • Emrich T, Franzke M, Mamoulis N, Renz M, Züfle A (2014) Geo-social skyline queries. In: International conference on database systems for advanced applications. Springer, pp 77–91

  • Gajjar S, Palazoglu A (2016) A data-driven multidimensional visualization technique for process fault detection and diagnosis. Chemom Intell Lab Syst 154:122–136

    Article  Google Scholar 

  • Gautam BP, Batajoo A, Asami H (2017) Cost effective accommodation planning in a trip by using accommodation advisor query (aa-query) in STPF. In: 2017 international conference on networking and network applications (NaNA). IEEE, pp 330–336

  • Godinho PIA, Meiguins BS, Meiguins ASG, do Carmo RMC, de Brito Garcia M, Almeida LH, Lourenco R (2007) Prisma-a multidimensional information visualization tool using multiple coordinated views. In: 2007 11th international conference information visualization (IV’07). IEEE, pp 23–32

  • Gogolou A, Tsandilas T, Palpanas T, Bezerianos A (2019) Comparing similarity perception in time series visualizations. IEEE Trans Vis Comput Graph 25(1):523–533

    Article  Google Scholar 

  • Goldstein O (2018) NBA players stats since 1950. https://www.kaggle.com/drgilermo/nba-players-stats. Accessed 2020

  • Gotz D, Stavropoulos H (2014) Decisionflow: visual analytics for high-dimensional temporal event sequence data. IEEE Trans Vis Comput Graph 20(12):1783–1792

    Article  Google Scholar 

  • Gratzl S, Lex A, Gehlenborg N, Pfister H, Streit M (2013) Lineup: Visual analysis of multi-attribute rankings. IEEE Trans Vis Comput Graph 19(12):2277–2286

    Article  Google Scholar 

  • Gürbüz T, Albayrak YE (2014) An engineering approach to human resources performance evaluation: hybrid MCDM application with interactions. Appl Soft Comput 21:365–375

    Article  Google Scholar 

  • Hsueh YL, Zimmermann R, Ku WS, Jin Y (2011) Skyengine: Efficient skyline search engine for continuous skyline computations. In: 2011 IEEE 27th international conference on data engineering. IEEE, pp 1316–1319

  • Huang YK, Chang CH, Lee C (2012) Continuous distance-based skyline queries in road networks. Inf Syst 37(7):611–633

    Article  Google Scholar 

  • Huang Z, Lu H, Ooi BC, Tung AK (2006) Continuous skyline queries for moving objects. IEEE Trans Knowl Data Eng 18(12):1645–1658

    Article  Google Scholar 

  • Inselberg A, Dimsdale B (1987) Parallel coordinates for visualizing multi-dimensional geometry. In: Computer graphics. Springer, pp 25–44

  • Inselberg A, Dimsdale B (1990) Parallel coordinates: a tool for visualizing multi-dimensional geometry. In: Proceedings of the 1st conference on Visualization’90. IEEE Computer Society Press, pp 361–378

  • Javed W, McDonnel B, Elmqvist N (2010) Graphical perception of multiple time series. IEEE Trans Vis Comput Graph 16(6):927–934

    Article  Google Scholar 

  • Jiang B, Pei J (2009) Online interval skyline queries on time series. In: 2009 IEEE 25th international conference on data engineering. IEEE, pp 1036–1047

  • Jolliffe I (2011) Principal component analysis. Springer, Berlin

    MATH  Google Scholar 

  • Kou G, Peng Y, Wang G (2014) Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Inf Sci 275:1–12

    Article  Google Scholar 

  • Levy JK (2005) Multiple criteria decision making and decision support systems for flood risk management. Stoch Environ Res Risk Assess 19(6):438–447

    Article  Google Scholar 

  • Li Q, Moon B, Lopez I (2004) Skyline index for time series data. IEEE Trans Knowl Data Eng 16(6):669–684

    Article  Google Scholar 

  • Liu D, Weng D, Li Y, Bao J, Zheng Y, Qu H, Wu Y (2017) Smartadp: visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Trans Vis Comput Graph 23(1):1–10

    Article  Google Scholar 

  • Liu Z, Heer J (2014) The effects of interactive latency on exploratory visual analysis. IEEE Trans Vis Comput Graph 20(12):2122–2131

    Article  Google Scholar 

  • Ma E, Liu Y, Li J, Chen S (2016) Anticipating Chinese tourists arrivals in Australia: a time series analysis. Tour Manag Perspect 17:50–58

    Article  Google Scholar 

  • Maaten LVD, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605

    MATH  Google Scholar 

  • Mathieu Blondel LB Matthieu Brucher (2007) Scikit-learn. https://scikit-learn.org/stable/. Accessed 2020

  • Munzner T (2009) A nested model for visualization design and validation. IEEE Trans Vis Comput Graph 15(6):921–928

    Article  Google Scholar 

  • Numbeo (2009) Numbeo Cost of Living. https://www.numbeo.com/cost-of-living/. Accessed 2020

  • Orcutt GH, Watts HW, Edwards JB (1968) Data aggregation and information loss. Am Econ Rev 58(4):773–787

    Google Scholar 

  • Pajer S, Streit M, Torsney-Weir T, Spechtenhauser F, Möller T, Piringer H (2017) Weightlifter: visual weight space exploration for multi-criteria decision making. IEEE Trans Vis Comput Graph 23(1):611–620

    Article  Google Scholar 

  • Partl C, Gratzl S, Streit M, Wassermann AM, Pfister H, Schmalstieg D, Lex A (2016) Pathfinder: visual analysis of paths in graphs. Comput Graph Forum 35:71–80

    Article  Google Scholar 

  • Reynoso-Meza G, Blasco X, Sanchis J, Herrero JM (2013) Comparison of design concepts in multi-criteria decision-making using level diagrams. Inf Sci 221:124–141

    Article  MathSciNet  Google Scholar 

  • Rezaei J (2016) Best-worst multi-criteria decision-making method: some properties and a linear model. Omega 64:126–130

    Article  Google Scholar 

  • Riehmann P, Hanfler M, Froehlich B (2005) Interactive Sankey diagrams. In: IEEE symposium on information visualization, 2005. INFOVIS 2005. IEEE, pp 233–240

  • Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323–2326

    Article  Google Scholar 

  • Schmidt M (2008) The Sankey diagram in energy and material flow management: Part I: History. J Ind Ecol 12(1):82–94

    Article  Google Scholar 

  • Tay FE, Cao L (2001) Application of support vector machines in financial time series forecasting. Omega 29(4):309–317

    Article  Google Scholar 

  • Tian Y, Lee KC, Lee WC (2009) Finding skyline paths in road networks. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, pp 444–447

  • Tsay RS (2005) Analysis of financial time series, vol 543. Wiley, London

    Book  Google Scholar 

  • Vlachou A, Vazirgiannis M (2010) Ranking the sky: discovering the importance of skyline points through subspace dominance relationships. Data Knowl Eng 69(9):943–964

    Article  Google Scholar 

  • Wang H, Wang CK, Xu YJ, Ning YC (2013) Dominant skyline query processing over multiple time series. J Comput Sci Technol 28(4):625–635

    Article  MathSciNet  Google Scholar 

  • Zhao X, Wu Y, Cui W, Du X, Chen Y, Wang Y, Lee DL, Qu H (2018) Skylens: visual analysis of skyline on multi-dimensional data. IEEE Trans Vis Comput Graph 24(1):246–255

    Article  Google Scholar 

Download references

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).

Author information

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

Authors
  1. Wooil Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Changbeom Shim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yon Dohn Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yon Dohn Chung.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12650-021-00758-y

Keywords

Search

Navigation