Abstract
Today digital recording technology empowers us to understand real-world behaviors with high quality and high definition multimodal time series data. Making the presentation of these time series fit for analysis purpose, at the right scale and resolution, has become a leading data visualization challenge. In this paper, we present TimeXplore, a novel visual analysis tool to aid the exploration of time series at scale. TimeXplore allows one to query and navigate large volumes of time series and their aggregates in near real time, with a simple yet powerful interface. The visualization synchronized across modalities can provide still further capability for us to develop and verify our hypothesis in multimodal data analysis.
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References
Berry, L., Munzner, T.: Binx: dynamic exploration of time series datasets across aggregation levels. In: IEEE Symposium on Information Visualization, pp. p2–p2. IEEE (2004)
Buono, P., Aris, A., Plaisant, C., Khella, A., Shneiderman, B.: Interactive pattern search in time series. Vis. Data Anal. 2005(5669), 175–186 (2005)
Casilari, E., Santoyo-Ramón, J.A., Cano-García, J.M.: UMAFall: a multisensor dataset for the research on automatic fall detection. Procedia Comput. Sci. 110, 32–39 (2017)
Cuenca, E., Sallaberry, A., Wang, F.Y., Poncelet, P.: Multistream: a multiresolution streamgraph approach to explore hierarchical time series. IEEE Trans. Visual Comput. Graph. 24(12), 3160–3173 (2018)
Dachselt, R., Frisch, M., Weiland, M.: FacetZoom: a continuous multi-scale widget for navigating hierarchical metadata. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1353–1356 (2008)
Faiola, A., Newlon, C.: Advancing critical care in the ICU: a human-centered biomedical data visualization systems. In: Robertson, M.M. (ed.) EHAWC 2011. LNCS, vol. 6779, pp. 119–128. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21716-6_13
Gschwandtner, T., Aigner, W., Kaiser, K., Miksch, S., Seyfang, A.: CareCruiser: exploring and visualizing plans, events, and effects interactively. In: 2011 IEEE Pacific Visualization Symposium, pp. 43–50. IEEE (2011)
Havre, S., Hetzler, E., Whitney, P., Nowell, L.: Themeriver: visualizing thematic changes in large document collections. IEEE Trans. Visual Comput. Graph. 8(1), 9–20 (2002). https://doi.org/10.1109/2945.981848
Hochheiser, H., Shneiderman, B.: Interactive exploration of time series data. In: Jantke, K.P., Shinohara, A. (eds.) DS 2001. LNCS (LNAI), vol. 2226, pp. 441–446. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45650-3_38
Hochheiser, H., Shneiderman, B.: Dynamic query tools for time series data sets: timebox widgets for interactive exploration. Inf. Vis. 3(1), 1–18 (2004)
Hunter, J.D.: Matplotlib: a 2d graphics environment. Comput. Sci. Eng. 9(03), 90–95 (2007)
Huynh, Q.T., Nguyen, U.D., Irazabal, L.B., Ghassemian, N., Tran, B.Q.: Optimization of an accelerometer and gyroscope-based fall detection algorithm. J. Sens. 2015 (2015)
Imrich, P., Mueller, K., Imre, D., Zelenyuk, A., Zhu, W.: Interactive poster: 3d themeriver. In: IEEE Information Visualization Symposium, vol. 3. IEEE Computer Society Press Los Alamitos (2003)
Kumar, P., Kumar, P., Zaidi, N., Rathore, V.S.: Analysis and comparative exploration of elastic search, MongoDB and Hadoop big data processing. In: Pant, M., Ray, K., Sharma, T.K., Rawat, S., Bandyopadhyay, A. (eds.) Soft Computing: Theories and Applications. AISC, vol. 584, pp. 605–615. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5699-4_57
Moritz, D., Fisher, D.: Visualizing a million time series with the density line chart. arXiv preprint arXiv:1808.06019 (2018)
Rind, A., et al.: Visual exploration of time-oriented patient data for chronic diseases: design study and evaluation. In: Holzinger, A., Simonic, K.-M. (eds.) USAB 2011. LNCS, vol. 7058, pp. 301–320. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25364-5_22
Robertson, G., Fernandez, R., Fisher, D., Lee, B., Stasko, J.: Effectiveness of animation in trend visualization. IEEE Trans. Visual Comput. Graph. 14(6), 1325–1332 (2008)
Santoyo-Ramón, J.A., Casilari, E., Cano-García, J.M.: Analysis of a smartphone-based architecture with multiple mobility sensors for fall detection with supervised learning. Sensors 18(4), 1155 (2018)
Shah, N., Willick, D., Mago, V.: A framework for social media data analytics using Elasticsearch and Kibana. Wireless Netw., 1–9 (2018)
Song, D., Xia, N., Cheng, W., Chen, H., Tao, D.: Deep r-th root of rank supervised joint binary embedding for multivariate time series retrieval. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2229–2238 (2018)
Steed, C.A., Halsey, W., Dehoff, R., Yoder, S.L., Paquit, V., Powers, S.: Falcon: visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing. Comput. Graph. 63, 50–64 (2017)
Weng, Y., Liu, L.: A collective anomaly detection approach for multidimensional streams in mobile service security. IEEE Access 7, 49157–49168 (2019)
Zhao, J., Chevalier, F., Pietriga, E., Balakrishnan, R.: Exploratory analysis of time-series with chronolenses. IEEE Trans. Visual Comput. Graph. 17(12), 2422–2431 (2011)
Zhao, Y., Wang, Y., Zhang, J., Fu, C.W., Xu, M., Moritz, D.: KD-Box: Line-segment-based KD-tree for interactive exploration of large-scale time-series data. IEEE Trans. Visual Comput. Graph. 28(1), 890–900 (2021)
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This work was supported in part by National Science Foundation grant IIS-1651581 and DUE-1726532.
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Huang, J., Ni, A., Zhang, J., Zhu, H., Zhang, H. (2023). Visualizing Multimodal Time Series at Scale. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2023. Lecture Notes in Computer Science, vol 14361. Springer, Cham. https://doi.org/10.1007/978-3-031-47969-4_6
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