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English Information

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Authors
# Name
1 Heraldo Borges(hborges@eic.cefet-rj.br)
2 Antonio Castro(antonio.castro@eic.cefet-rj.br)
3 Rafaelli Coutinho(rafaelli.coutinho@cefet-rj.br)
4 Fabio Porto (fporto@lncc.br)
5 Esther Pacitti( Esther.Pacitti@inria.fr)
6 Eduardo Ogasawara(eogasawara@ieee.org)

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Reference
# Reference
1 Bischoff, F. and Rodrigues, P. (2020). tsmp: An R Package for Time Series with Matrix Profile. R Journal, 12(1):76–86.
2 Borges, H., Dutra, M., Bazaz, A., Coutinho, R., Perosi, F., Porto, F., Masseglia, F., Pacitti, E., and Ogasawara, E. (2020). Spatial-time motifs discovery. Intelligent Data Analysis, 24(5):1121–1140.
3 dgbes (2018). Netherlands Offshore F3 Block - Complete. Technical report, https://opendtect.org/osr/Main/NetherlandsOffshoreF3BlockComplete4GB.
4 Eichmann, P., Tatbul, N., Solleza, F., and Zdonik, S. (2019). Visual exploration of time series anomalies with metro-viz. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 1901–1904.
5 Lin, J., Keogh, E., Lonardi, S., Lankford, J. P., and Nystrom, D. M. (2004). VizTree: a tool for visually mining and monitoring massive time series databases. In Proceedings of the Thirtieth international conference on Very large data bases - Volume 30, VLDB ’04, pages 1269–1272, Toronto, Canada. VLDB Endowment.
6 Linardi, M., Zhu, Y., Palpanas, T., and Keogh, E. (2020). Matrix profile goes MAD: variable-length motif and discord discovery in data series. Data Mining and Knowledge Discovery, 34(4):1022–1071.
7 Oates, T., Boedihardjo, A., Lin, J., Chen, C., Frankenstein, S., and Gandhi, S. (2013). Motif discovery in spatial trajectories using grammar inference. In International Conference on Information and Knowledge Management, Proceedings, pages 1465–1468.
8 Ramanujam, E. and Padmavathi, S. (2022). Comprehensive review on time series motif discovery using evolutionary techniques. International Journal of Advanced Intelligence Paradigms, 23(1-2):155–170.
9 Senin, P., Lin, J., Wang, X., Oates, T., Gandhi, S., Boedihardjo, A., Chen, C., Frankenstein, S., and Lerner, M. (2014). GrammarViz 2.0: A tool for grammar-based pattern discovery in time series. Lecture Notes in Computer Science, 8726 LNAI(PART 3):468–472.
10 Shekhar, S., Feiner, S., and Aref, W. (2016). Spatial computing. Communications of the ACM, 59(1):72–81.
11 Torkamani, S. and Lohweg, V. (2017). Survey on time series motif discovery. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(2).
12 Yeh, C.-C., Zhu, Y., Ulanova, L., Begum, N., Ding, Y., Dau, H., Zimmerman, Z., Silva, D., Mueen, A., and Keogh, E. (2018). Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile. Data Mining and Knowledge Discovery, 32(1):83–123.