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2018 | OriginalPaper | Chapter

6. Spatiotemporal Event Sequence (STES) Mining

Authors : Berkay Aydin, Rafal A. Angryk

Published in: Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories

Publisher: Springer International Publishing

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Abstract

Spatiotemporal event sequences are the ordered series of event types. These event types represent the types of evolving region trajectory based instances that follow each other. The goal of spatiotemporal event sequence mining is finding frequently occurring sequences of event types from the follow relationships among all event instances. The key aspect of spatiotemporal event sequences is the spatiotemporal follow relationship appearing among the event instances. The relationship is characterized by temporal sequence relationship with spatial proximity constraints. In this chapter, we will touch upon the key concepts of spatiotemporal event sequence models, describe the spatiotemporal follow relationship thoroughly, and then present the state-of-the-art algorithms for discovering the event sequences.

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previous chapter Spatiotemporal Co-occurrence Pattern (STCOP) Mining

Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB’94, Proceedings of 20th International Conference on Very Large Data Bases, September 12–15, 1994, Santiago de Chile, Chile, pp. 487–499 (1994)

Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983). https://doi.org/10.1145/182.358434 CrossRef

Aydin, B., Angryk, R.: Discovering spatiotemporal event sequences. In: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems, pp. 46–55. ACM (2016)

10.

Aydin, B., Angryk, R.: Spatiotemporal event sequence mining from evolving regions. In: 23rd International Conference on Pattern Recognition (ICPR), Cancún, México, December 4–8, 2016, pp. 4167–4172 (2016)

13.

Aydin, B., Angryk, R.A.: A graph-based approach to spatiotemporal event sequence mining. In: IEEE International Conference on Data Mining Workshops, ICDM Workshops 2016, December 12–15, 2016, Barcelona, Spain, pp. 1090–1097 (2016). https://doi.org/10.1109/ICDMW.2016.0157. URL http://dx.doi.org/10.1109/ICDMW.2016.0157

16.

Aydin, B., Kucuk, A., Angryk, R.A., Martens, P.C.: Measuring the significance of spatiotemporal co-occurrences. ACM Trans. Spatial Algorithms and Systems 3(3), 9:1–9:35 (2017). https://doi.org/10.1145/3139351. URL http://doi.acm.org/10.1145/3139351.

17.

Aydin, B., Kucuk, A., Boubrahimi, S.F., Angryk, R.A.: Top-(R%, K) spatiotemporal event sequence mining. In: 2017 IEEE International Conference on Data Mining Workshops, ICDM Workshops 2017, New Orleans, LA, USA, November 18–21, 2017, pp. 250–257 (2017). https://doi.org/10.1109/ICDMW.2017.39. URL https://doi.org/10.1109/ICDMW.2017.39

28.

Celik, M., Shekhar, S., Rogers, J.P., Shine, J.A., Kang, J.M.: Mining at most top-k% mixed-drove spatio-temporal co-occurrence patterns: A summary of results. In: Proceedings of the 23rd International Conference on Data Engineering Workshops, ICDE 2007, 15–20 April 2007, Istanbul, Turkey, pp. 565–574 (2007). https://doi.org/10.1109/ICDEW.2007.4401042. URL http://dx.doi.org/10.1109/ICDEW.2007.4401042

31.

Chuang, K., Huang, J., Chen, M.: Mining top-k frequent patterns in the presence of the memory constraint. VLDB J. 17(5), 1321–1344 (2008). https://doi.org/10.1007/s00778-007-0078-6. URL http://dx.doi.org/10.1007/s00778-007-0078-6

79.

Mörchen, F.: Algorithms for time series knowledge mining. In: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, August 20–23, 2006, pp. 668–673 (2006). https://doi.org/10.1145/1150402.1150485

92.

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

95.

Pillai, K.G., Angryk, R.A., Banda, J.M., Kempton, D., Aydin, B., Martens, P.C.: Mining at most top-k&percnt; spatiotemporal co-occurrence patterns in datasets with extended spatial representations. ACM Trans. Spatial Algorithms and Systems 2(3), 10:1–10:27 (2016). https://doi.org/10.1145/2936775. URL http://doi.acm.org/10.1145/2936775

121.

Tseng, V.S., Wu, C., Fournier-Viger, P., Yu, P.S.: Efficient algorithms for mining top-k high utility itemsets. IEEE Trans. Knowl. Data Eng. 28(1), 54–67 (2016). https://doi.org/10.1109/TKDE.2015.2458860. URL http://dx.doi.org/10.1109/TKDE.2015.2458860

122.

Tzvetkov, P., Yan, X., Han, J.: TSP: mining top-k closed sequential patterns. Knowl. Inf. Syst. 7(4), 438–457 (2005). https://doi.org/10.1007/s10115-004-0175-4. URL http://dx.doi.org/10.1007/s10115-004-0175-4

129.

Webb, G.I.: Filtered-top-k association discovery. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery 1(3), 183–192 (2011). https://doi.org/10.1002/widm.28. URL http://dx.doi.org/10.1002/widm.28

Title: Spatiotemporal Event Sequence (STES) Mining
Authors: Berkay Aydin
Rafal A. Angryk
Publisher: Springer International Publishing
Book: Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories
Print ISBN: 978-3-319-99872-5

Electronic ISBN: 978-3-319-99873-2

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-99873-2_6

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