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research-article

Comparing Similarity Perception in Time Series Visualizations

Authors:

Theophanis Tsandilas,

Themis Palpanas,

Anastasia BezerianosAuthors Info & Claims

IEEE Transactions on Visualization and Computer Graphics, Volume 25, Issue 1

Pages 523 - 533

https://doi.org/10.1109/TVCG.2018.2865077

Published: 01 January 2019 Publication History

Abstract

A common challenge faced by many domain experts working with time series data is how to identify and compare similar patterns. This operation is fundamental in high-level tasks, such as detecting recurring phenomena or creating clusters of similar temporal sequences. While automatic measures exist to compute time series similarity, human intervention is often required to visually inspect these automatically generated results. The visualization literature has examined similarity perception and its relation to automatic similarity measures for line charts, but has not yet considered if alternative visual representations, such as horizon graphs and colorfields, alter this perception. Motivated by how neuroscientists evaluate epileptiform patterns, we conducted two experiments that study how these three visualization techniques affect similarity perception in EEG signals. We seek to understand if the time series results returned from automatic similarity measures are perceived in a similar manner, irrespective of the visualization technique; and if what people perceive as similar with each visualization aligns with different automatic measures and their similarity constraints. Our findings indicate that horizon graphs align with similarity measures that allow local variations in temporal position or speed (i.e., dynamic time warping) more than the two other techniques. On the other hand, horizon graphs do not align with measures that are insensitive to amplitude and y-offset scaling (i.e., measures based on z-normalization), but the inverse seems to be the case for line charts and colorfields. Overall, our work indicates that the choice of visualization affects what temporal patterns we consider as similar, i.e., the notion of similarity in time series is not visualization independent.

References

[1]

W. Aigner, S. Miksch, H. Schumann, and C. Tominski. Visualization of Time-Oriented Data. Springer Publishing Company, Incorporated, 1st ed., 2011.

[2]

D. Albers, M. Correll, and M. Gleicher. Task-driven evaluation of aggregation in time series visualization. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI'14, pp. 551–560. ACM, New York, NY, USA, 2014. https://doi.org/10.1145/2556288.2557200.

[3]

D. Albers, C. Dewey, and M. Gleicher. Sequence surveyor: Leveraging overview for scalable genomic alignment visualization. IEEE Transactions on Visualization and Computer Graphics, 17 (12) pp. 2392–2401, Dec. 2011. https://doi.org/10.1109/TVCG.2011.232.

Digital Library

[4]

J. Aßfalg, H.-P. Kriegel, P. Kröger, P. Kunath, A. Pryakhin, and M. Renz. Similarity search on time series based on threshold queries. In Proceedings of the 10th International Conference on Advances in Database Technology, EDBT'06, pp. 276–294. Springer-Verlag, Berlin, Heidelberg, 2006. https://doi.org/10.1007/11687238_19.

[5]

T. Baguley. Serious Stats: A guide to advanced statistics for the behavioral sciences. Palgrave Macmillan, 2012.

[6]

G.E. Batista, E.J. Keogh, O.M. Tataw, and V.M. Souza. Cid: An efficient complexity-invariant distance for time series. Data Min. Knowl. Discov., 28 (3) pp. 634–669, May 2014. https://doi.org/10.1007/s10618-013-0312-3.

Digital Library

[7]

D.J. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, AAAIWS'94, pp. 359–370. AAAI Press, 1994.

[8]

E. Bertini, P. Hertzog, and D. Lalanne. Spiralview: Towards security policies assessment through visual correlation of network resources with evolution of alarms. In Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology, VAST '07, pp. 139–146. IEEE Computer Society, Washington, DC, USA, 2007. https://doi.org/10.1109/VAST.2007.4389007.

[9]

R.L. Brennan and D.J. Prediger. Coefficient kappa: Some uses, misuses, and alternatives. Educational and psychological measurement, 41 (3) pp. 687–699, 1981.

[10]

P. Buono and A.L. Simeone. Interactive shape specification for pattern search in time series. In Proceedings of the Working Conference on Advanced Visual Interfaces, AVI '08, pp. 480–481. ACM, New York, NY, USA, 2008. https://doi.org/10.1145/1385569.1385666.

[11]

L. Byron and M. Wattenberg. Stacked graphs - geometry & aesthetics. IEEE Transactions on Visualization and Computer Graphics, 14 (6) pp. 1245–1252, Nov. 2008. https://doi.org/10.1109/TVCG.2008.166.

Digital Library

[12]

A. Canty and B.D. Ripley. boot: Bootstrap R (S-Plus) Functions, 2017. R package version 1.3-20.

[13]

Y. Chen, E. Keogh, B. Hu, N. Begum, A. Bagnall, A. Mueen, and G. Batista. The ucr time series classification archive, July 2015. [Online]. Available: www.cs.ucr.edu/-eamonn/time_series_data/.

[14]

Y. Chen, M. Nascimento, B.C. Ooi, and A.K. Tung. Spade: On shape-based pattern detection in streaming time series. In Proceedings of the IEEE 23rd International Conference on Data Engineering, ICDE'07, pp. 786–795. IEEE, 2007. https://doi.org/10.1109/ICDE.2007.367924.

[15]

M. Correll, D. Albers, S. Franconeri, and M. Gleicher. Comparing averages in time series data. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI'12, pp. 1095–1104. ACM, New York, NY, USA, 2012. https://doi.org/10.1145/2207676.2208556.

[16]

M. Correll and M. Gleicher. The semantics of sketch: Flexibility in visual query systems for time series data. In 2016 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 131–140, Oct 2016. https://doi.org/10.1109/VAST.2016.7883519.

[17]

M. de Curtis, J.G.R. Jefferys, and M. Avoli. Interictal epileptiform discharges in partial epilepsy: Complex neurobiological mechanisms based on experimental and clinical evidence. Jasper's Basic Mechanisms of the Epilepsies [Internet]. 4th edition, pp. 303–325, 2012.

[18]

H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, and E. Keogh. Querying and mining of time series data: Experimental comparison of representations and distance measures. Proc. VLDB Endow., 1 (2) pp. 1542–1552, Aug. 2008. https://doi.org/10.14778/1454159.1454226.

Digital Library

[19]

P. Dragicevic. “Fair statistical communication in hci”. In Modern Statistical Methods for HCI, pp. 291–330. Springer, 2016. https://doi.org/10.1007/978-3-319-26633-6_13.

[20]

B. Efron. Better bootstrap confidence intervals. Journal of the American Statistical Association, 82 (397) pp. 171–185, 1987. https://doi.org/10.1080/01621459.1987.10478410.

[21]

P. Eichmann and E. Zgraggen. Evaluating subjective accuracy in time series pattern-matching using human-annotated rankings. In Proceedings of the 20th International Conference on Intelligent User Interfaces, IUI '15, pp. 28–37. ACM, New York, NY, USA, 2015. https://doi.org/10.1145/2678025.2701379.

[22]

C. Faloutsos, M. Ranganathan, and Y. Manolopoulos. Fast subsequence matching in time-series databases. In Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data, SIGMOD '94, pp. 419–429. ACM, New York, NY, USA, 1994. https://doi.org/10.1145/191839.191925.

[23]

T.-C. Fu. A review on time series data mining. Eng. Appl. Artif. Intell., 24 (1) pp. 164–181, Feb. 2011. https://doi.org/10.1016/j.engappai.2010.09.007.

Digital Library

[24]

J. Fuchs, F. Fischer, F. Mansmann, E. Bertini, and P. Isenberg. Evaluation of alternative glyph designs for time series data in a small multiple setting. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI'13, pp. 3237–3246. ACM, New York, NY, USA, 2013. https://doi.org/10.1145/2470654.2466443.

[25]

A. Gogolou, T. Tsandilas, T. Palpanas, and A. Bezerianos. Comparing time series similarity perception under different color interpolations. [Online]. Available: https://hal.inria.fr/hal-01844994. Research Report, Inria, 06/2018.

[26]

D.Q. Goldin and P.C. Kanellakis. On similarity queries for time-series data: Constraint specification and implementation. In Proceedings of the First International Conference on Principles and Practice of Constraint Programming, CP '95, pp. 137–153. Springer-Verlag, London, UK, UK, 1995.

[27]

M. Gregory and B. Shneiderman. Shape identification in temporal data sets. Master's thesis, Master's thesis, University of Maryland, 2009.

[28]

K.L. Gwet. Handbook of Inter-Rater Reliability, 4th Edition: The Definitive Guide to Measuring The Extent of Agreement Among Raters. Advanced Analytics, LLC, 2014.

[29]

J. Heer, N. Kong, and M. Agrawala. Sizing the horizon: The effects of chart size and layering on the graphical perception of time series visualizations. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '09, pp. 1303–1312. ACM, New York, NY, USA, 2009. https://doi.org/10.1145/1518701.1518897.

[30]

H. Hochheiser and B. Shneiderman. Dynamic query tools for time series data sets: Timebox widgets for interactive exploration. Information Visualization, 3 (1) pp. 1–18, Mar. 2004. https://doi.org/10.1145/993176.993177.

[31]

C. Holz and S. Feiner. Relaxed selection techniques for querying time-series graphs. In Proceedings of the 22Nd Annual ACM Symposium on User Interface Software and Technology, UIST'09, pp. 213–222. ACM, New York, NY, USA, 2009. https://doi.org/10.1145/1622176.1622217.

[32]

K. Indiradevi, E. Elias, P. Sathidevi, S.D. Nayak, and K. Radhakrishnan. A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram. Computers in Biology and Medicine, 38 (7) 805–816, 2008. https://doi.org/10.1016/j.compbiomed.2008.04.010.

Digital Library

[33]

A. Jabbari, R. Blanch, and S. Dupuy-Chessa. Composite visual mapping for time series visualization. In 2018 IEEE Pacific Visualization Symposium (PacificVis), pp. 116–124, April 2018. https://doi.org/10.1109/PacificVis.2018.00023.

[34]

W. Javed, B. McDonnel, and N. Elmqvist. Graphical perception of multiple time series. IEEE Transactions on Visualization and Computer Graphics, 16 (6) pp. 927–934, Nov. 2010. https://doi.org/10.1109/TVCG.2010.162.

Digital Library

[35]

J. Jing, J. Dauwels, T. Rakthanmanon, E. Keogh, S. Cash, and M. West-over. Rapid annotation of interictal epileptiform discharges via template matching under dynamic time warping. Journal of Neuroscience Methods, 274: pp. 179–190, 2016. https://doi.org/10.1016/j.jneumeth.2016.02.025.

[36]

S. Juan Orta D. C. Kh, Q. Az, C. Dj, and C. Aj. Prognostic implications of periodic epileptiform discharges. Archives of Neurology, 66 (8) pp. 985–991, 2009. https://doi.org/10.1001/archneurol.2009.137.

[37]

R. Kincaid and H. Lam. Line graph explorer: Scalable display of line graphs using focus+context. In Proceedings of the Working Conference on Advanced Visual Interfaces, AVI '06, pp. 404–411. ACM, New York, NY, USA, 2006. https://doi.org/10.1145/1133265.1133348.

Digital Library

[38]

H. Levkowitz and G. Herman. The design and evaluation of color scales for image data. Computer Graphics and Applications), 12 (1) pp. 82–89, 1992.

[39]

E. Limpert, W.A. Stahel, and M. Abbt. Log-normal distributions across the sciences: Keys and clues. 51: p. 341-, 05 2001. https://doi.org/10.1641/0006-3568(2001)051[0341:LNDATS]2.0.CO;2.

[40]

E.K.S. Louis and L.C. Frey. Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants. American Epilepsy Society, 2016.

[41]

M. Mannino and A. Abouzied. Expressive time series querying with hand-drawn scale-free sketches. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI '18, pp. 388:1–388:13. ACM, New York, NY, USA, 2018. https://doi.org/10.1145/3173574.3173962.

[42]

P. McLachlan, T. Munzner, E. Koutsofios, and S. North. Liverac: Interactive visual exploration of system management time-series data. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '08, pp. 1483–1492. ACM, New York, NY, USA, 2008. https://doi.org/10.1145/1357054.1357286.

[43]

W. Müller and H. Schumann. Visualization for modeling and simulation: Visualization methods for time-dependent data - an overview. In Proceedings of the 35th Conference on Winter Simulation: Driving Innovation, WSC '03, pp. 737–745. Winter Simulation Conference, 2003.

[44]

P.K. Muthumanickam, K. Vrotsou, M. Cooper, and J. Johansson. Shape grammarextraction for efficient query-by-sketch pattern matching in long time series. In 2016 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 121–130, Oct 2016. https://doi.org/10.1109/VAST.2016.7883518.

[45]

D. Nadalutti and L. Chittaro. Visual analysis of users performance data in fitness activities. Computers & Graphics, 31 (3) pp. 429–439, 2007. https://doi.org/10.1016/j.cag.2007.01.032.

Digital Library

[46]

T. Palpanas. Data series management: The road to big sequence analytics. SIGMOD Record, 44 (2) pp. 47–52, 2015. https://doi.org/10.1145/2814710.2814719.

Digital Library

[47]

C. Perin, F. Vernier, and J.-D. Fekete. Interactive horizon graphs: Improving the compact visualization of multiple time series. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI'13, pp. 3217–3226. ACM, New York, NY, USA, 2013. https://doi.org/10.1145/2470654.2466441.

[48]

T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover, Q. Zhu, J. Zakaria, and E. Keogh. Searching and mining trillions of time series subsequences under dynamic time warping. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '12, pp. 262–270. ACM, New York, NY, USA, 2012. https://doi.org/10.1145/2339530.2339576.

[49]

C.A. Ratanamahatana and E. Keogh. Everything you know about dynamic time warping is wrong. In Third Workshop on Mining Temporal and Sequential Data. Citeseer, 2004.

[50]

H. Reijner. The development of the horizon graph. available online at http://www.stonesc.com/Vis08_Workshop/DVD/Reijner_submission.pdf, 2008.

[51]

H.G. Rey, C. Pedreira, and R. Quian Quiroga. Past, present and future of spike sorting techniques. Brain Research Bulletin, 119 (Pt B): pp. 106–117, Oct 2015. https://doi.org/10.1016/j.brainresbull.2015.04.007.

[52]

K. Ryall, N. Lesh, T. Lanning, D. Leigh, H. Miyashita, and S. Makino. Querylines: Approximate query for visual browsing. In CHI '05 Extended Abstracts on Human Factors in Computing Systems, CHI EA '05, pp. 1765–1768. ACM, New York, NY, USA, 2005. https://doi.org/10.1145/1056808.1057017.

[53]

T. Saito, H.N. Miyamura, M. Yamamoto, H. Saito, Y. Hoshiya, and T. Kaseda. Two-tone pseudo coloring: Compact visualization for one-dimensional data. In Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization, INFOVIS'05, p. 23-. IEEE Computer Society, Washington, DC, USA, 2005. https://doi.org/10.1109/INFOVIS.2005.35.

[54]

T. Siddiqui, A. Kim, J. Lee, K. Karahalios, and A. Parameswaran. Effortless data exploration with zenvisage: An expressive and interactive visual analytics system. Proc. VLDB Endow., 10 (4) pp. 457–468, Nov. 2016. https://doi.org/10.14778/3025111.3025126.

Digital Library

[55]

R.L. Spitzer and J.L. Fleiss. A re-analysis of the reliability of psychiatric diagnosis. The British Journal of Psychiatry, 125 (587) pp. 341–347, 1974. https://doi.org/10.1192/bjp.125.4.341.

[56]

K.J. Staley and F.E. Dudek. Interictal spikes and epileptogenesis. Epilepsy Currentsv 6. 6, pp. 199–202, 2006. https://doi.org/10.1111/j.1535-7511.2006.00145.x.

[57]

K.J. Staley, A. White, and F.E. Dudek. Interictal spikes: Harbingers or causes of epilepsy? Neuroscience letters 497. 3, pp. 247–250, 2011. https://doi.org/10.1016/j.neulet.2011.03.070.

[58]

M. Stone. In color perception, size matters. IEEE Computer Graphics and Applications, 32 (2) pp. 8–13, March 2012. https://doi.org/10.1109/MCG.2012.37.

Digital Library

[59]

B. Swihart, B. Caffo, B. James, M. Strand, B. Schwartz, and N. Punjabi. Lasagna plots: a saucy alternative to spaghetti plots. Epidemiology (Cambridge, Mass.), 21 (5) pp. 621–625, 2010. https://doi.org/10.1097/EDE.0b013e3181e5b06a.

[60]

I. Snchez Fernndez, T. Loddenkemper, A.S. Galanopoulou, and S.L. Mosh. Should epileptiform discharges be treated? Epilepsia, 56 (10) pp. 1492–1504, 2015. https://doi.org/10.1111/epi.13108.

[61]

J. Talbot, J. Gerth, and P. Hanrahan. An empirical model of slope ratio comparisons. IEEE Transactions on Visualization and Computer Graphics, 18 (12) pp. 2613–2620, Dec 2012. https://doi.org/10.1109/TVCG.2012.196.

Digital Library

[62]

T. Tsandilas. Fallacies of agreement: A critical review of consensus assessment methods for gesture elicitation. ACM Transactions on Computer-Human Interaction (TOCHI), 25 (3) pp. 18:1–18:49, June 2018. https://doi.org/10.1145/3182168.

[63]

E.R. Tufte. The Visual Display of Quantitative Information. Cheshire, CT, USA: Graphics Press, 1986.

Digital Library

[64]

J.J. Van Wijk and E.R. Van Selow. Cluster and calendar based visualization of time series data. In Proceedings of the 1999 IEEE Symposium on Information Visualization, INFOVIS '99, pp. 4-. IEEE Computer Society, Washington, DC, USA, 1999. https://doi.org/10.1109/INFVIS.1999.801851.

[65]

M. Wattenberg. Sketching a graph to query a time-series database. In CHI '01 Extended Abstracts on Human Factors in Computing Systems, CHI EA '01, pp. 381–382. ACM, New York, NY, USA, 2001. https://doi.org/10.1145/634067.634292.

Digital Library

[66]

M. Weber, M. Alexa, and W. Müller. Visualizing time-series on spirals. In Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS'01), INFOVIS '01, pp. 7-. IEEE Computer Society, Washington, DC, USA, 2001. https://doi.org/10.1109/INFVIS.2001.963273.

[67]

J. Zhao, F. Chevalier, and R. Balakrishnan. Kronominer: Using multi-foci navigation for the visual exploration of time-series data. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '11, pp. 1737–1746. ACM, New York, NY, USA, 2011. https://doi.org/10.1145/1978942.1979195.

[68]

J. Zhao, F. Chevalier, E. Pietriga, and R. Balakrishnan. Exploratory analysis of time-series with chronolenses. IEEE Transactions on Visualization and Computer Graphics, 17 (12) pp. 2422–2431, Dec. 2011. https://doi.org/10.1109/TVCG.2011.195.

Digital Library

[69]

K. Zoumpatianos, S. Idreos, and T. Palpanas. RINSE: interactive data series exploration with ADS+. PVLDB, 8 (12) pp. 1912–1915, 2015. https://doi.org/10.14778/2824032.2824099.

Digital Library

[70]

K. Zoumpatianos, S. Idreos, and T. Palpanas. ADS: the adaptive data series index. VLDB J., 25 (6) pp. 843–866, 2016. https://doi.org/10.1007/s00778-016-0442-5.

Digital Library

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Comparing Similarity Perception in Time Series Visualizations

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cover image IEEE Transactions on Visualization and Computer Graphics

IEEE Transactions on Visualization and Computer Graphics Volume 25, Issue 1

Jan. 2019

1266 pages

ISSN:1077-2626

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1077-2626 © 2018 IEEE.

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IEEE Educational Activities Department

United States

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Published: 01 January 2019

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Shi YChen BChen YJin ZXu KJiao XGao TCao N(2024)Supporting Guided Exploratory Visual Analysis on Time Series Data with Reinforcement LearningIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332720030:1(1172-1182)Online publication date: 1-Jan-2024
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