Um conjunto de bases de dados para suportar agentes inteligentes em domínios de roteamento da Internet Infrastructure
Resumo
Este artigo apresenta um conjunto de três bases de dados que compõem o Internet Infrastructure Data Base (IIDB). O IIDB é um conjunto formado pelas bases iidb.rfc, iidb.person e iidb.acronym, peças-chave para apoiar o desenvolvimento do aprendizado de máquina desejado aos elementos inteligentes do projeto Arquitetura Autônoma Sobre Domı́nios Restritos (A2RD). Os dados contidos em iidb.rfc e iidb.person foram criados após o processamento do conteúdo disponı́vel na página web do RFC Index. Enquanto os dados contidos no iidb.acronym foram criados após o processamento do conteúdo dos arquivos disponı́veis no repositório Request for Comments (RFC) produzido e mantido pelo RFC Editor. Todos os dados no IIDB são formatados em JavaScript Object Notation (JSON), cujos respectivos modelos estão disponı́veis no mesmo site onde as bases de dados são depositadas, acessı́veis através de qualquer linguagem de programação.
Palavras-chave:
ietf, irtf, rfc, acrônimo, Internet Infrastructure, agents
Referências
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Braga, J., Silva, J. N., Endo, P. T., Ribas, J., and Omar, N. (2018). Blockchain to Improve Security, Knowledge and Collaboration Inter-Agent Communication over Restrict Do- mains of the Internet Infrastructure. In Proceeding of CSBC 2018 - VWorkshop pre IETF, pages 61–73, Natal, RN Brazil.
Colel, R., Callon, R., Gardner, E., and Rekhter, Y. (May 1994). Guidelines for OSI NSAP Allocation in the Internet . Technical report, RFC Editor. RFC1629.
Conroy, J. M. and O’leary, D. P. (2001). Text summarization via hidden Markov models. In Proceedings ofthe 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 406–407.
Fellbaum, C. (1998). WordNet: An Electronic Lexical Database. MIT Press, Cambridge, MA.
Freitag, D. and McCallum, A. (1999). Information extraction with hmms and shrink- age. In Proceedings of the AAAI-99 workshop on machine learning for information extraction, pages 31–36. Orlando, Florida.
Hares, S. and Katz, D. (December 1989). Administrative Domains and Routing Domains: A model for routing in the Internet. Technical report, RFC Editor. RFC113.
Isotani, S. and Bittencourt, I. I. (2015). Dados abertos conectados. Novatec Editora, S˜ao Paulo, SP, Brasil.
Jacobs, K., Itai, A., and Wintner, S. (2018). Acronyms: identification, expansion and disambiguation. Annals ofMathematics and Artificial Intelligence, pages 1–16.
Ji, X., Xu, G., Bailey, J., and Li, H. (2008). Mining, ranking, and using acronym patterns. In Asia-Pacific Web Conference, pages 371–382. Springer.
Khairova, N., Petrasova, S., Lewoniewski, W., Mamyrbayev, O., and Mukhsina, K. (2018). Automatic extraction of synonymous collocation pairs from a text corpus. In 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), pages 485–488. IEEE.
Miller, G. A. (1995). WordNet: A Lexical Database for English. Communications ofthe ACM, 38(11):39–41.
Moldovan, D. and Novischi, A. (2004). Word sense disambiguation of wordnet glosses. Computer Speech & Language, 18(3):301–317.
Musumeci, F., Rottondi, C., Nag, A., Macaluso, I., Zibar, D., Ruffini, M., and Torna- tore, M. (2018). A Survey on Application of Machine Learning Techniques in Optical Networks. IEEE Communications Surveys & Tutorials, pages 1–1.
Ong, L., Rytina, I., Garcia, M., Schwarzbauer, H., Coene, L., Lin, H., Juhasz, I., Holdrege, M., and Sharp, C. (October 1999). Framework Architecture for Signaling Transport. Technical report, RFC Editor. RFC2719.
Osiek, B. A., Xex´eo, G., and de Carvalho, L. A. V. (2010). A language-independent acronym extraction from biomedical texts with hidden markov models. IEEE Trans- actions on Biomedical Engineering, 57(11):2677–2688.
Pakhomov, S. (2002). Semi-supervised maximum entropy based approach to acronym and abbreviation normalization in medical texts. In Proceedings ofthe 40th Annual Meet- ing on Association for Computational Linguistics, ACL ’02, pages 160–167, Strouds- burg, PA, USA. Association for Computational Linguistics.
Paulino, C. D. M., Turkman, M. A. A., and Murteira, B. (2018). Estat´ıstica Bayesiana. Fundac¸ ˜ao Calouste Gulbenkian, second edition.
Perkins, J. (2014). Python 3 text processing with NLTK 3 cookbook. Packt Publishing Ltd.
Poole, D. L. and Mackworth, A. K. (2010). Artificial Intelligence: foundations ofcompu- tational agents. Cambridge University Press.
Pustejovsky, J., Castano, J., Cochran, B., Kotecki, M., and Morrell, M. (2001). Auto- matic extraction of acronym-meaning pairs from medline databases. Studies in health technology and informatics, 84(1):371–375.
Rabiner, L. R. (1989). A tutorial on hidden markov models and selected applications in speech recognition. Proceedings ofthe IEEE, 77(2):257–286.
S´anchez, D. and Isern, D. (2011). Automatic extraction of acronym definitions from theWeb. Applied Intelligence, 34(2):311–327.
Shpiner, A., Tse, R., Schelp, C., and Mizrahi, T. (December 2016). Multipath Time Synchronization. Technical report, RFC Editor. RFC8039.
Stewart, R., Tuexen, M., and Proshin, M. (February 2019). Stream Control Transmission Protocol: Errata and Issues in RFC 4960. Technical report, RFC Editor. RFC8540.
Zahariev, M. (2004). A(Acronyms). PhD thesis, Simon Fraser University.
Bird, S., Klein, E., and Loper, E. (2009). Natural language processing with Python. ” O’Reilly Media, Inc.”.
Braga, J., Silva, J. N., Endo, P. T., and Omar, N. (2019). Autonomous Architec- ture Over Restricted Domains (A2RD). DOI 10.17605/OSF.IO/TKA9U. Available at https://osf.io/tka9u/. Acessed: 19 Mar 2019.
Braga, J., Silva, J. N., Endo, P. T., Ribas, J., and Omar, N. (2018). Blockchain to Improve Security, Knowledge and Collaboration Inter-Agent Communication over Restrict Do- mains of the Internet Infrastructure. In Proceeding of CSBC 2018 - VWorkshop pre IETF, pages 61–73, Natal, RN Brazil.
Colel, R., Callon, R., Gardner, E., and Rekhter, Y. (May 1994). Guidelines for OSI NSAP Allocation in the Internet . Technical report, RFC Editor. RFC1629.
Conroy, J. M. and O’leary, D. P. (2001). Text summarization via hidden Markov models. In Proceedings ofthe 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 406–407.
Fellbaum, C. (1998). WordNet: An Electronic Lexical Database. MIT Press, Cambridge, MA.
Freitag, D. and McCallum, A. (1999). Information extraction with hmms and shrink- age. In Proceedings of the AAAI-99 workshop on machine learning for information extraction, pages 31–36. Orlando, Florida.
Hares, S. and Katz, D. (December 1989). Administrative Domains and Routing Domains: A model for routing in the Internet. Technical report, RFC Editor. RFC113.
Isotani, S. and Bittencourt, I. I. (2015). Dados abertos conectados. Novatec Editora, S˜ao Paulo, SP, Brasil.
Jacobs, K., Itai, A., and Wintner, S. (2018). Acronyms: identification, expansion and disambiguation. Annals ofMathematics and Artificial Intelligence, pages 1–16.
Ji, X., Xu, G., Bailey, J., and Li, H. (2008). Mining, ranking, and using acronym patterns. In Asia-Pacific Web Conference, pages 371–382. Springer.
Khairova, N., Petrasova, S., Lewoniewski, W., Mamyrbayev, O., and Mukhsina, K. (2018). Automatic extraction of synonymous collocation pairs from a text corpus. In 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), pages 485–488. IEEE.
Miller, G. A. (1995). WordNet: A Lexical Database for English. Communications ofthe ACM, 38(11):39–41.
Moldovan, D. and Novischi, A. (2004). Word sense disambiguation of wordnet glosses. Computer Speech & Language, 18(3):301–317.
Musumeci, F., Rottondi, C., Nag, A., Macaluso, I., Zibar, D., Ruffini, M., and Torna- tore, M. (2018). A Survey on Application of Machine Learning Techniques in Optical Networks. IEEE Communications Surveys & Tutorials, pages 1–1.
Ong, L., Rytina, I., Garcia, M., Schwarzbauer, H., Coene, L., Lin, H., Juhasz, I., Holdrege, M., and Sharp, C. (October 1999). Framework Architecture for Signaling Transport. Technical report, RFC Editor. RFC2719.
Osiek, B. A., Xex´eo, G., and de Carvalho, L. A. V. (2010). A language-independent acronym extraction from biomedical texts with hidden markov models. IEEE Trans- actions on Biomedical Engineering, 57(11):2677–2688.
Pakhomov, S. (2002). Semi-supervised maximum entropy based approach to acronym and abbreviation normalization in medical texts. In Proceedings ofthe 40th Annual Meet- ing on Association for Computational Linguistics, ACL ’02, pages 160–167, Strouds- burg, PA, USA. Association for Computational Linguistics.
Paulino, C. D. M., Turkman, M. A. A., and Murteira, B. (2018). Estat´ıstica Bayesiana. Fundac¸ ˜ao Calouste Gulbenkian, second edition.
Perkins, J. (2014). Python 3 text processing with NLTK 3 cookbook. Packt Publishing Ltd.
Poole, D. L. and Mackworth, A. K. (2010). Artificial Intelligence: foundations ofcompu- tational agents. Cambridge University Press.
Pustejovsky, J., Castano, J., Cochran, B., Kotecki, M., and Morrell, M. (2001). Auto- matic extraction of acronym-meaning pairs from medline databases. Studies in health technology and informatics, 84(1):371–375.
Rabiner, L. R. (1989). A tutorial on hidden markov models and selected applications in speech recognition. Proceedings ofthe IEEE, 77(2):257–286.
S´anchez, D. and Isern, D. (2011). Automatic extraction of acronym definitions from theWeb. Applied Intelligence, 34(2):311–327.
Shpiner, A., Tse, R., Schelp, C., and Mizrahi, T. (December 2016). Multipath Time Synchronization. Technical report, RFC Editor. RFC8039.
Stewart, R., Tuexen, M., and Proshin, M. (February 2019). Stream Control Transmission Protocol: Errata and Issues in RFC 4960. Technical report, RFC Editor. RFC8540.
Zahariev, M. (2004). A(Acronyms). PhD thesis, Simon Fraser University.
Publicado
11/07/2019
Como Citar
BRAGA, Julião; SILVA, Joao ; OMAR, Nizam .
Um conjunto de bases de dados para suportar agentes inteligentes em domínios de roteamento da Internet Infrastructure. In: WORKSHOP PRÉ-IETF (WPIETF), 6. , 2019, Belém.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
ISSN 2595-6388.
DOI: https://doi.org/10.5753/wpietf.2019.6579.
