SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

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

Authors
# Name
1 Ana Beatriz Cruz(anacruz@acm.org)
2 Joao Ferreira(joao.parana@acm.org)
3 Diego Carvalho(d.carvalho@ieee.org)
4 Eduardo Mendes(eduardo.mendes@fgv.br)
5 Esther Pacitti(Esther.Pacitti@inria.fr)
6 Rafaelli Coutinho(rafaelli.coutinho@cefet-rj.br)
7 Fabio Porto (fporto@lncc.br)
8 Eduardo Ogasawara( eogasawara@ieee.org)

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

Reference
# Reference
1 Aggarwal, C. C. (2016). Outlier Analysis. Springer, New York, NY, 2nd edition.
2 Bierlaire, M., Chen, J., and Newman, J. (2013). A probabilistic map matching method for smartphone GPS data. Transportation Research Part C: Emerging Technologies, 26:78–98.
3 Chen, W., Guo, F., and Wang, F.-Y. (2015). A survey of traffic data visualization. Intelligent Transportation Systems, IEEE Transactions on, 16(6):2970–2984.
4 Cressie, N. and Wikle, C. K. (2015). Statistics for spatio-temporal data. John Wiley & Sons.
5 Cruz, A. B., Ferreira, J., Monteiro, B., Coutinho, R., Porto, F., and Ogasawara, E. (2017). Deteccção de anomalias no transporte rodoviário urbano. In Proceedings of the 32nd Brazilian Symposium on Databases (SBBD), pages 240–245.
6 Ferreira, N., Poco, J., Vo, H. T., Freire, J., and Silva, C. T. (2013). Visual exploration of big spatio-temporal urban data: A study of new york city taxi trips. Visualization and Computer Graphics, IEEE Transactions on, 19(12):2149–2158.
7 Giannotti, F., Nanni, M., Pinelli, F., and Pedreschi, D. (2007). Trajectory pattern mining. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 330–339.
8 Han, J., Kamber, M., and Pei, J. (2011). Data Mining: Concepts and Techniques. Morgan Kaufmann, Haryana, India; Burlington, MA, 3 edition.
9 Lakhina, A., Crovella, M., and Diot, C. (2004). Diagnosing network-wide traffic anomalies. In ACM SIGCOMM Computer Communication Review, pages 219–230. ACM.
10 Liu, B., Hsu, W., Chen, S., and Ma, Y. (2000). Analyzing the subjective interestingness of association rules. IEEE Intelligent Systems and their Applications, 15(5):47–55.
11 Mcgarry, K. (2005). A survey of interestingness measures for knowledge discovery. The Knowledge Engineering Review, 20(1):39–61.
12 Tao, Y., Kollios, G., Considine, J., Li, F., and Papadias, D. (2004). Spatio-temporal aggregation using sketches. In Proceedings - International Conference on Data Engineering, volume 20, pages 214–225.
13 United Nations (2014). World urbanization prospects. https://www.unilibrary.org/content/publication/527e5125-en.