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ST Sequence Miner: visualization and mining of spatio-temporal event sequences


As a promising field of research, event sequence analysis seems to assist in facilitating clear reasoning behind human decisions by mining reality behind the sequential actions. Mining frequent patterns from event sequences has proved to be promising in extracting actionable insights, which plays an important role in many application domains. Much of the related work challenges the problem solely from the temporal perspective omitting the information that could be gained from the spatial part. This could be in part due to the fact that analysis of event sequences with references to both time and space is attributed as a challenging task due to the additional variance in the data introduced by the spatial aspect. We propose a visual analytics approach that incorporates spatio-temporal pattern extraction leveraging an extended sequential pattern mining algorithm and a pattern discovery guidance mechanism operating on geographic query and selection capabilities. As an implementation of our approach, we introduce a visual analytics tool, namely ST Sequence Miner, enabling event pattern exploration in time-location space. We evaluate our approach over a credit card transaction dataset by adopting case study methodology. Our study unveils that patterns mined from event sequences can better explain possible relationships with proper visualization of time-location data.

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  1. 1.

    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)

  2. 2.

    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, pp. 3–14 (1995)

  3. 3.

    Andrienko, G., Andrienko, N., Chen, W., Maciejewski, R., Zhao, Y.: Visual analytics of mobility and transportation: state of the art and further research directions. IEEE Trans. Intell. Transp. Syst. 18(8), 2232–2249 (2017)

    Google Scholar 

  4. 4.

    Andrienko, G., Andrienko, N., Demsar, U., Dransch, D., Dykes, J., Fabrikant, S.I., Jern, M., Kraak, M.J., Schumann, H., Tominski, C.: Space, time and visual analytics. Int. J. Geogr. Inf. Sci. 24(10), 1577–1600 (2010)

    Google Scholar 

  5. 5.

    Andrienko, G., Malerba, D., May, M., Teisseire, M.: Mining spatio-temporal data. J. Intell. Inf. Syst. 27(3), 187–190 (2006)

    Google Scholar 

  6. 6.

    Andrienko, N., Andrienko, G.: Interactive visual tools to explore spatio-temporal variation. In: Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 417–420. Association for Computing Machinery, New York (2004)

  7. 7.

    Andrienko, N., Andrienko, G.: Visual analytics of movement: an overview of methods, tools and procedures. Inf. Vis. 12(1), 3–24 (2013)

    MathSciNet  Google Scholar 

  8. 8.

    Bertin, J.: Semiology of Graphics. University of Wisconsin Press, Madison (1983)

    Google Scholar 

  9. 9.

    Bonilla, E.V., Chai, K.M.A., Williams, C.K.I.: Multi-task Gaussian process prediction. In: Proceedings of the 20th International Conference on Neural Information Processing Systems, NIPS’07, pp. 153–160. Curran Associates Inc., Red Hook (2007)

  10. 10.

    Carter, E., Burd, R., Monroe, M., Plaisant, C., Shneiderman, B.: Using eventflow to analyze task performance during trauma resuscitation. In: Proceedings of the Workshop on Interactive Systems in Healthcare (2013)

  11. 11.

    Chen, Y., Xu, P., Ren, L.: Sequence synopsis: optimize visual summary of temporal event data. IEEE Trans. Vis. Comput. Graph. 24(1), 45–55 (2018)

    Google Scholar 

  12. 12.

    Cressie, N., Shi, T., Kang, E.L.: Fixed rank filtering for spatio-temporal data. J. Comput. Graph. Stat. 19(3), 724–745 (2010)

    MathSciNet  Google Scholar 

  13. 13.

    Di Clemente, R., Luengo-Oroz, M., Travizano, M., Xu, S., Vaitla, B., González, M.C.: Sequences of purchases in credit card data reveal lifestyles in urban populations. Nat Communi 9(1), 3330 (2018).

    Article  Google Scholar 

  14. 14.

    Du, F., Shneiderman, B., Plaisant, C., Malik, S., Perer, A.: Coping with volume and variety in temporal event sequences: strategies for sharpening analytic focus. IEEE Trans. Vis. Comput. Graph. 23(6), 1636–1649 (2017)

    Google Scholar 

  15. 15.

    Elmqvist, N., Fekete, J.D.: Hierarchical aggregation for information visualization: overview, techniques, and design guidelines. IEEE Trans. Vis. Comput. Graph. 16(3), 439–454 (2010)

    Google Scholar 

  16. 16.

    Fournier-Viger, P., Lin, J.C.W., Kiran, R.U., Koh, Y.S., Thomas, R.: A survey of sequential pattern mining. Data Sci. Pattern Recogn. 1(1), 54–77 (2017)

    Google Scholar 

  17. 17.

    Guo, H., Gomez, S.R., Ziemkiewicz, C., Laidlaw, D.H.: A case study using visualization interaction logs and insight metrics to understand how analysts arrive at insights. IEEE Trans. Vis. Comput. Graph. 22(1), 51–60 (2015)

    Google Scholar 

  18. 18.

    Guo, H., Wang, Z., Yu, B., Zhao, H., Yuan, X.: Tripvista: triple perspective visual trajectory analytics and its application on microscopic traffic data at a road intersection. In: 2011 IEEE Pacific Visualization Symposium, pp. 163–170. IEEE (2011)

  19. 19.

    Hicks, D.: Contextual inquiries: a discourse-oriented study of classroom learning. In: Discourse, Learning and Schooling, pp. 104–141. Cambridge University Press, New York, NY (1996)

  20. 20.

    Liu, D., Xu, P., Ren, L.: Tpflow: progressive partition and multidimensional pattern extraction for large-scale spatio-temporal data analysis. IEEE Trans. Vis. Comput. Graph. 25(1), 1–11 (2019)

    Google Scholar 

  21. 21.

    Liu, S., Cui, W., Wu, Y., Liu, M.: A survey on information visualization: recent advances and challenges. Vis. Comput. 30(12), 1373–1393 (2014)

    Google Scholar 

  22. 22.

    Liu, Z., Wang, Y., Dontcheva, M., Hoffman, M., Walker, S., Wilson, A.: Patterns and sequences: interactive exploration of clickstreams to understand common visitor paths. IEEE Trans. Vis. Comput. Graph. 23(1), 321–330 (2017)

    Google Scholar 

  23. 23.

    Meyer, T.E., Monroe, M., Plaisant, C., Lan, R., Wongsuphasawat, K., Coster, T.S., Gold, S., Millstein, J., Shneiderman, B.: Visualizing patterns of drug prescriptions with eventflow: a pilot study of asthma medications in the military health system. Technical report, DTIC Document (2013)

  24. 24.

    Mørup, M.: Applications of tensor (multiway array) factorizations and decompositions in data mining. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 1(1), 24–40 (2011)

    Google Scholar 

  25. 25.

    Nguyen, P.H., Turkay, C., Andrienko, G., Andrienko, N., Thonnard, O., Zouaoui, J.: Understanding user behaviour through action sequences: from the usual to the unusual. IEEE Trans. Vis. Comput. Graph. 25, 2838–2852 (2018)

    Google Scholar 

  26. 26.

    North, C.: Toward measuring visualization insight. IEEE Comput. Graph. Appl. 26(3), 6–9 (2006)

    Google Scholar 

  27. 27.

    Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans. Knowl. Data Eng. 16(11), 1424–1440 (2004)

    Google Scholar 

  28. 28.

    Pennacchioli, D., Coscia, M., Rinzivillo, S., Giannotti, F., Pedreschi, D.: The retail market as a complex system. EPJ Data Sci. 3(1), 33 (2014)

    Google Scholar 

  29. 29.

    Perer, A., Wang, F.: Frequence: interactive mining and visualization of temporal frequent event sequences. In: Proceedings of the 19th International Conference on Intelligent User Interfaces, pp. 153–162. Association for Computing Machinery, New York (2014)

  30. 30.

    Scheepens, R., Willems, N., Van de Wetering, H., Andrienko, G., Andrienko, N., Van Wijk, J.J.: Composite density maps for multivariate trajectories. IEEE Trans. Vis. Comput. Graph. 12, 2518–2527 (2011)

    Google Scholar 

  31. 31.

    Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of IEEE Symposium on Visual Languages, 1996, pp. 336–343. IEEE (1996)

  32. 32.

    Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Apers, P., Bouzeghoub, M., Gardarin, G. (eds.) Advances in Database Technology—EDBT ’96, pp. 1–17. Springer, Berlin (1996)

    Google Scholar 

  33. 33.

    Tominski, C., Schumann, H., Andrienko, G., Andrienko, N.: Stacking-based visualization of trajectory attribute data. IEEE Trans. Vis. Comput. Graph. 18(12), 2565–2574 (2012)

    Google Scholar 

  34. 34.

    Tsoukatos, I., Gunopulos, D.: Efficient mining of spatiotemporal patterns. In: Advances in Spatial and Temporal Databases, pp. 425–442. Springer, Berlin, Heidelberg (2001)

  35. 35.

    Vrotsou, K., Nordman, A.: Exploratory visual sequence mining based on pattern-growth. IEEE Trans. Vis. Comput. Graph. 25, 2597–2610 (2018)

    Google Scholar 

  36. 36.

    Wang, J., Hsu, W., Lee, M.L., Wang, J.: Flowminer: finding flow patterns in spatio-temporal databases. In: 16th IEEE International Conference on Tools with Artificial Intelligence, pp. 14–21 (2004)

  37. 37.

    Ward, M.O.: A taxonomy of glyph placement strategies for multidimensional data visualization. Inf. Vis. 1(3–4), 194–210 (2002)

    Google Scholar 

  38. 38.

    Yi, J.S., ah Kang, Y., Stasko, J.: Toward a deeper understanding of the role of interaction in information visualization. IEEE Trans. Vis. Comput. Graph. 13(6), 1224–1231 (2007)

    Google Scholar 

  39. 39.

    Zaki, M.J.: Spade: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1–2), 31–60 (2001)

    MATH  Google Scholar 

  40. 40.

    Zheng, Y., Capra, L., Wolfson, O., Yang, H.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol: (TIST) 5(3), 38 (2014)

    Google Scholar 

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Correspondence to Baran Koseoglu.

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Koseoglu, B., Kaya, E., Balcisoy, S. et al. ST Sequence Miner: visualization and mining of spatio-temporal event sequences. Vis Comput 36, 2369–2381 (2020).

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  • Sequence mining
  • Event sequences
  • Spatio-temporal data
  • Information visualization
  • Visual analytics