Um Framework de Extração e Etiquetamento de Informações de Trânsito

  • Leonardo Teteo UNIRIO
  • Pedro Moura UNIRIO
  • Elton F. de S. Soares UNIRIO
  • Carlos Alberto V. Campos UNIRIO

Abstract


With the great development of computational technologies, it has been possible to use social networks to collect and analyze information of individuals, communities and with respect to cities in real time. The information overload, however, is a challenge even in the context of cities, where many events occur in parallel. In this context, this paper describes the development of a framework that seeks to ease the extraction, treatment and identification of events and their locations in tweets written in the Portuguese language using the Conditional Random Fields technique to address the Named Entity Recognition task. The obtained results demonstrate the potential of the tool in identifying important traffic events in a given location of interest.

References

Anantharam, P., Barnaghi, P., Thirunarayan, K., and Sheth, A. (2015). Extracting city traffic events from social streams. ACMTrans. Intell. Syst. Technol., 6(4):43:1–43:27.

Bajaj, G., Agarwal, R., Bouloukakis, G., Singh, P., Georgantas, N., and Issarny, V. (2016). Towards building real-time, convenient route recommendation system for public tran- sit. In 2016 IEEE International Smart Cities Conference (ISC2), pages 1–5.

Dobbelaere, P. and Esmaili, K. S. (2017). Kafka versus rabbitmq: A comparative study of two industry reference publish/subscribe implementations: Industry paper. In 11th ACM DEBS, pages 227–238.

Eugster, P. T., Felber, P. A., Guerraoui, R., and Kermarrec, A.-M. (2003). The many faces of publish/subscribe. ACM Computing Surveys (CSUR), 35(2):114–131.

FarajiDavar, N., Kolozali, S., and Barnaghi, P. M. (2017). A deep multi-view learning framework for city event extraction from twitter data streams. CoRR, abs/1705.09975.

Finkel, J. R., Grenager, T., and Manning, C. (2005). Incorporating non-local information into information extraction systems by gibbs sampling. In 43rd annual meeting on association for computational linguistics, pages 363–370.

Neirotti, P., De Marco, A., Cagliano, A. C., Mangano, G., and Scorrano, F. (2014). Cur- rent trends in smart city initiatives: Some stylised facts. Cities, 38:25–36.

P Hancke, G. and Silva, B. (2012). The role of advanced sensing in smart cities. Sensors (Basel, Switzerland), 13:393–425.

Puiu, D., Barnaghi, P., T¨onjes, R., Kumper, D., Ali, M. I., Mileo, A., Parreira, J., Fischer, M., Kolozali, S., Farajidavar, N., Gao, F., ggena, T., Pham, T.-L., Nechifor, C.-S., Pus- chmann, D., and Fernandes, J. (2016). Citypulse: Large scale data analytics framework for smart cities. IEEE ACCESS, 4:1086–1108.

Twitter (2019). Twitter streaming api connection guidelines. https://developer. twitter.com/en/docs/tutorials/consuming-streaming-data. html. Accessed: 2019-03-24.

Zanella, A., Bui, N., Castellani, A., Vangelista, L., and Zorzi, M. (2012). Internet of things for smart cities. Internet ofThings Journal, IEEE, 1.
Published
2019-07-08
TETEO, Leonardo; MOURA, Pedro ; SOARES, Elton F. de S.; CAMPOS, Carlos Alberto V.. Um Framework de Extração e Etiquetamento de Informações de Trânsito. In: WORKSHOP ON PERFORMANCE OF COMPUTER AND COMMUNICATION SYSTEMS (WPERFORMANCE), 2019. , 2019, Belém. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . ISSN 2595-6167. DOI: https://doi.org/10.5753/wperformance.2019.6472.