Estratégia Distribuída para Análise de Assuntos Abordados no Twitter Via Evolução de Clusters
Resumo
Recentes técnicas têm aplicado algoritmos de evolução de clusters para analisar transições de assuntos em redes sociais e apresentam-se eficazes no monitoramento destes. No entanto, a elevada taxa de produção de dados nas redes sociais cria a necessidade de processamento de uma quantidade de dados cada vez maior. Este trabalho propõe uma estratégia mais escalável para análise da evolução de assuntos em redes sociais, por meio do emprego de uma solução distribuída na etapa de clustering dos dados. Os experimentos foram realizados utilizando dados obtidos do Twitter e demonstram que a solução proposta é promissora, apresentando ganhos consideráveis de desempenho.
Palavras-chave:
Evolução de Assuntos, Redes Sociais, Clustering de Dados
Referências
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He, Yaobin, et al. (2011) "Mr-dbscan: an efficient parallel density-based clustering algorithm using mapreduce." Parallel and Distributed Systems (ICPADS), 2011 IEEE 17th International Conference on. IEEE.
Kim, M. S., & Han, J. (2009). A particle-and-density based evolutionary clustering method for dynamic networks. Proceedings of the VLDB Endowment, 2(1), 622-633.
Lee, P., Lakshmanan, L. V., & Milios, E. E. (2014). Incremental cluster evolution tracking from highly dynamic network data. In Data Engineering (ICDE), 2014 IEEE 30th International Conference on (pp. 3-14). IEEE.
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Rodrigues, Priscila R.F. et al. (2016)“Dinâmica de Temas Abordados no Twitter Via Evolução de Clusters.” Proceedings of the 31thSBBD, p. 151-157.
Silva, Ticiana L. Coelho, José AF de Macêdo, and Marco A. Casanova. (2014) "Discovering frequent mobility patterns on moving object data." Proceedings of the Third ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems.ACM.
Tajeuna, Etienne Gael, Mohamed Bouguessa, and ShengruiWang. (2015) "Tracking the evolution of community structures in time-evolving social networks." DSAA, 2015. 36678 2015.IEEE International Conference on.IEEE,.
White, T. (2012). Hadoop: The definitive guide. "O'Reilly Media, Inc.".
Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.
Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd(Vol. 96, No. 34, pp. 226-231).
Gan, J., & Tao, Y. (2015). DBSCAN revisited: mis-claim, un-fixability, and approximation. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (pp. 519-530). ACM.
He, Yaobin, et al. (2011) "Mr-dbscan: an efficient parallel density-based clustering algorithm using mapreduce." Parallel and Distributed Systems (ICPADS), 2011 IEEE 17th International Conference on. IEEE.
Kim, M. S., & Han, J. (2009). A particle-and-density based evolutionary clustering method for dynamic networks. Proceedings of the VLDB Endowment, 2(1), 622-633.
Lee, P., Lakshmanan, L. V., & Milios, E. E. (2014). Incremental cluster evolution tracking from highly dynamic network data. In Data Engineering (ICDE), 2014 IEEE 30th International Conference on (pp. 3-14). IEEE.
Neto, Antonio Cavalcante Araujo, et al. (2015) "G2P: A partitioning approach for processing dbscan with mapreduce." International Symposium on Web and Wireless Geographical Information Systems. Springer, Cham.
Rodrigues, Priscila R.F. et al. (2016)“Dinâmica de Temas Abordados no Twitter Via Evolução de Clusters.” Proceedings of the 31thSBBD, p. 151-157.
Silva, Ticiana L. Coelho, José AF de Macêdo, and Marco A. Casanova. (2014) "Discovering frequent mobility patterns on moving object data." Proceedings of the Third ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems.ACM.
Tajeuna, Etienne Gael, Mohamed Bouguessa, and ShengruiWang. (2015) "Tracking the evolution of community structures in time-evolving social networks." DSAA, 2015. 36678 2015.IEEE International Conference on.IEEE,.
White, T. (2012). Hadoop: The definitive guide. "O'Reilly Media, Inc.".
Publicado
02/10/2017
Como Citar
RODRIGUES, Priscila Rocha Ferreira; DA SILVA, Ticiana L. Coelho; M. FILHO, José Maria da Silva; MACÊDO, José Antônio F. de.
Estratégia Distribuída para Análise de Assuntos Abordados no Twitter Via Evolução de Clusters. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 32. , 2017, Uberlândia/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 258-263.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2017.175059.
