Paper Information | SBC BOOKS

Fill in your paper information

Language (*)

Title (*)

Keywords (*)

Abstract (*)

A myriad of applications from different domains collects time series data for further analysis. In many of them, such as seismic datasets, the observed data is also associated to a space dimension, which corresponds, in fact, to spatial-time series. The analysis of these datasets is difficult due to both the continuous nature of the observed data and the relationship between spatial and time dimensions. Meanwhile, sequential patterns mining techniques have been successfully used in large volume of transactional databases to obtain insights from data. In this work, we start exploring the discovery of frequent sequential patterns in seismic datasets. For that, we discretize continuous values into symbols and adapt well known sequential algorithm to mine spatial-time dataset. To better understand the quality of the identified patterns, we visualize them over the original seismic traces images. Our preliminary results indicate that the study of sequence mining in seismic datasets is promising.

Pages (*)

File Link

English Information

Title

Keywords

Abstract

(*) To change the order drag the item to the new position.

Authors

#	Name
1	Riccardo Campisano(riccardo.campisano@linea.gov.br)
2	Fabio Porto (fporto@lncc.br)
3	Esther Pacitti(esther.pacitti@lirmm.fr)
4	Florent Masseglia(Florent.Masseglia@inria.fr)
5	Eduardo Ogasawara( eogasawara@ieee.org)

(*) To change the order drag the item to the new position.

Reference

#	Reference
1	Agrawal, R. and Srikant, R. (1995). Mining sequential patterns. In Proceedings of the Eleventh International Conference on Data Engineering, ICDE ’95, pages 3–14, Washington, DC, USA. IEEE Computer Society.
2	Alatrista-Salas, H., Azé, J., Bringay, S., Cernesson, F., Selmaoui-Folcher, N., and Teisseire, M. (2015). A knowledge discovery process for spatiotemporal data: Application to river water quality monitoring. Ecological Informatics, 26:127–139.
3	Frank, A. U. (2003). Ontology for spatio-temporal databases. In Spatio-Temporal Databases, pages 9–77. Springer.
4	Fu, T.-c. (2011). A review on time series data mining. Eng. Appl. Artif. Intell., 24(1):164–181.
5	Han, J., Cheng, H., Xin, D., and Yan, X. (2007). Frequent pattern mining: Current status and future directions. Data Min. Knowl. Discov., 15(1):55–86.
6	Leong, K. and Chan, S. (2012). Stem: A novel approach for spatiotemporal sequence mining. Asian Journal of Information Technology, 11(3):94–99.
7	Lin, J., Keogh, E., Lonardi, S., and Chiu, B. (2003). A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD ’03, pages 2–11, New York, NY, USA. ACM.
8	Mabroukeh, N. R. and Ezeife, C. I. (2010). A taxonomy of sequential pattern mining algorithms. ACM Comput. Surv., 43(1):3:1–3:41.
9	Zhou, H.-W. (2014). Practical seismic data analysis. Cambridge University Press.

Paper Registration

Fill in your paper information

English Information

Authors

Reference