Abstract
This chapter is concerned with computational methods to support the analysis of time-oriented data. A general overview of temporal data analysis is provided and specific application examples will be used for demonstration.
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References
Ali, M., A. Alqahtani, M.W. Jones, and X. Xie. 2019. Clustering and classification for time series data in visual analytics: A survey. IEEE Access 7: 181314–181338. https://doi.org/10.1109/ACCESS.2019.2958551.
Andrienko, N., G. Andrienko, S. Miksch, H. Schumann, and S. Wrobel. 2021. A Theoretical model for pattern discovery in visual analytics. Visual Informatics 5 (1): 23–42. https://doi.org/10.1016/j.visinf.2020.12.002.
Antunes, C.M., and A.L. Oliveira. 2001. Temporal data mining: An overview. In Workshop on Temporal Data Mining at the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).https://www.researchgate.net/publication/284602094.
Bade, R., S. Schlechtweg, S. Miksch. 2004. Connecting time-oriented data and information to a coherent interactive visualization. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), 105–112. ACM Press. https://doi.org/10.1145/985692.985706.
Bernard, J., C. Bors, M. Bögl, C. Eichner, T. Gschwandtner, S. Miksch, H. Schumann, and J. Kohlhammer. 2018. Combining the automated segmentation and visual analysis of multivariate time series. In Proceedings of the EuroVis Workshop on Visual Analytics (EuroVA), 49–53. Eurographics Association. https://doi.org/10.2312/eurova.20181112.
Bors, C., J. Bernard, M. Bögl, T. Gschwandtner, J. Kohlhammer, and S. Miksch. 2019. Quantifying Uncertainty in Multivariate Time Series Pre-processing. Eurographics Association. https://doi.org/10.2312/eurova.20191121.
Brockwell, P.J., and R.A. Davis. 1991. Time Series: Theory and Methods (2009), 2nd ed. Springer. https://doi.org/10.1007/978-1-4419-0320-4.
Clancey, W. J. 1985. Heuristic classification. Artificial Intelligence 27 (3): 289–350. https://doi.org/10.1016/0004-3702(85)90016-5.
Cohen, P., B. Heeringa, and N. Adams. 2002. Unsupervised segmentation of categorical time series into episodes. In Proceedings of the International Conference on Data Mining (ICDM), 99–106. IEEE Computer Society. https://doi.org/10.1109/ICDM.2002.1183891.
Combi. C., E. Keravnou-Papailiou, and Y. Shahar. 2010. Temporal Information Systems in Medicine. Springer. https://doi.org/10.1007/978-1-4419-6543-1.
Eichner, C., H. Schumann, and C. Tominski. 2020. Making parameter dependencies of time-series segmentation visually understandable. Computer Graphics Forum 39 (1): 607–622. https://doi.org/10.1111/cgf.13894.
Fayyad, U., G.G. Grinstein, and A. Wierse, eds. 2001. Information Visualization in Data Mining and Knowledge Discovery. Morgan Kaufmann.
Fayyad, U., G. Piatetsky-Shapiro, and P. Smyth. 1996. From data mining to knowledge discovery in databases. AI Magazine 17 (3): 37–54. https://doi.org/10.1609/aimag.v17i3.1230.
Foley, J.D. 2000. Getting there: The ten top problems left. IEEE Computer Graphics and Applications 20 (1): 66–68. https://doi.org/10.1109/38.814569.
Gan, G., C. Ma, J. Wu. 2007. Data Clustering: Theory, Algorithms, and Applications. Society for Industrial and Applied Mathematics, ASA-SIAM Series on Statistics and Applied Probability. https://doi.org/10.1137/1.9780898718348.
Gschwandtner, T., M. Bögl, P. Federico, and S. Miksch. 2016. Visual encodings of temporal uncertainty: A comparative user study. IEEE Transactions on Visualization and Computer Graphics 22 (1): 539–548. https://doi.org/10.1109/TVCG.2015.2467752.
Han, J., M. Kamber, and J. Pei. 2012. Data Mining: Concepts and Techniques, 3rd ed. Morgan Kaufmann. https://doi.org/10.1016/C2009-0-61819-5.
Jackson, J. E. 2003. A User’s Guide to Principal Components. Wiley.
Jain, A.K., M.N. Murty, and P.J. Flynn. 1999. Data clustering: A review. ACM Computing Surveys 31 (3): 264–323. https://doi.org/10.1145/331499.331504.
Jeong, D.H., C. Ziemkiewicz, B. Fisher, W. Ribarsky, and R. Chang. 2009. iPCA: An interactive system for PCA-based visual analytics. Computer Graphics Forum 28 (3): 767–774. https://doi.org/10.1111/j.1467-8659.2009.01475.x.
Jolliffe, I.T. 2002. Principal Component Analysis, 2nd ed. Springer. https://doi.org/10.1007/b98835.
Keim, D. A., F. Mansmann, J. Schneidewind, and H. Ziegler. 2006a. Challenges in visual data analysis In Proceedings of the International Conference Information Visualisation (IV), 9–16. IEEE Computer Society. https://doi.org/10.1109/IV.2006.31.
Laxman, S., and P.S. Sastry. 2006. A survey of temporal data mining. sādhanā 31: 173–198. https://doi.org/10.1007/bf02719780.
Lin, J., E. Keogh, S. Lonardi, and P. Patel. 2002. Finding motifs in time series. In Proceedings of the SIGKDD Workshop on Temporal Data Mining, 53–68. https://cs.gmu.edu/~jessica/Lin_motif.pdf.
Lin, J., E.J. Keogh, L. Wei, and S. Lonardi. 2007. Experiencing SAX: A novel symbolic representation of time series. Data Mining and Knowledge Discovery 15 (2): 107–144. https://doi.org/10.1007/s10618-007-0064-z.
Miksch, S., W. Horn, C. Popow, and F. Paky. 1996. Utilizing temporal data abstraction for data validation and therapy planning for artificially ventilated newborn infants. Artificial Intelligence in Medicine 8 (6): 543–576. https://doi.org/10.1016/s0933-3657(96)00355-7.
Miksch, S., A. Seyfang,W. Horn, and C. Popow. 1999. Abstracting steady qualitative descriptions over time from noisy, high-frequency data. In Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making (AIMDM), 281–290. Springer. https://doi.org/10.1007/3-540-48720-4_31.
Mitsa, T. 2010. Temporal Data Mining. Chapman & Hall/CRC. https://doi.org/10.1201/9781420089776.
Nocke, T., H. Schumann, and U. Böhm. 2004. Methods for the visualization of clustered climate data. Computational Statistics 19 (1): 75–94. https://doi.org/10.1007/bf02915277.
Stacey, M., and C. McGregor. 2007. Temporal abstraction in intelligent clinical data analysis: A survey. Artificial Intelligence in Medicine 39 (1): 1–24. https://doi.org/10.1016/j.artmed.2006.08.002.
Thomas, J. J., and K. A. Cook. 2005. Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE Computer Society.
Van Wijk, J. J. and E. R. van Selow. 1999. Cluster and calendar based visualization of time series data In Proceedings of the IEEE Symposium Information Visualization (InfoVis), 4–9. IEEE Computer Society. https://doi.org/10.1109/INFVIS.1999.801851.
Ware, C. 2008. Visual Thinking for Design. Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-370896-0.X0001-7.
Warren Liao, T. 2005. Clustering of time series data - a survey. Pattern Recognition 38 (11): 1857–1874. https://doi.org/10.1016/j.patcog.2005.01.025.
Wegner, P. 1997. Why interaction is more powerful than algorithms. Communications of the ACM 40 (5): 80–91. https://doi.org/10.1145/253769.253801.
Xing, Z., J. Pei, and E. Keogh. 2010. A brief survey on sequence classification. SIGKDD Explorations Newsletter 12 (1): 40–48. https://doi.org/10.1145/1882471.1882478.
Xu, R., and D.C. II Wunsch. 2009. Clustering. Wiley. https://doi.org/10.1002/9780470382776.
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Aigner, W., Miksch, S., Schumann, H., Tominski, C. (2023). Computational Analysis Support. In: Visualization of Time-Oriented Data. Human–Computer Interaction Series. Springer, London. https://doi.org/10.1007/978-1-4471-7527-8_6
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DOI: https://doi.org/10.1007/978-1-4471-7527-8_6
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