Gerência de Incerteza em Bancos de Dados de Proveniência de Workflows de Bioinformática
Resumo
Bancos de dados de proveniência de experimentos científicos desempenham um papel fundamental na ciência. Os modelos utilizados para representar esses dados assumem que existe uma certeza nos relacionamentos de proveniência. Entretanto, diversos experimentos não são determinísticos e seus resultados estão associados a incertezas. Realizar a análise dos dados de proveniência com tais incertezas não é trivial. Nesse artigo é proposta uma abordagem para gerência de incertezas em dados de proveniência baseada em um componente extrator que armazena os dados de proveniência e a incerteza associada em um banco de dados probabilístico. Experimentos mostraram um overhead aceitável da abordagem de cerca de 3% no tempo total de execução do workflow e 16% no tempo de processamento da consulta.
Palavras-chave:
Bancos de dados de proveniência, Gerência de incertezas
Referências
Ahola, V., Aittokallio, T., Vihinen, M. and Uusipaikka, E. (2008). Model-based prediction of sequence alignment quality. Bioinformatics (Oxford, England), v. 24, n. 19, p. 2165–2171.
Boulos, J., Dalvi, N., Mandhani, B., et al. (2005). MYSTIQ: A System for Finding More Answers by Using Probabilities. In Int. Conf. Management of Data (SIGMOD), pp. 891-893.
Chapman, A., Blaustein, B. and Elsaesser, C. (2010). Provenance-based Belief. In Workshop on the Theory and Practice of Provenance (TaPP). p. 11.
Costa, F., Silva, V., De Oliveira, D., et al. (2013). Capturing and Querying Workflow Runtime Provenance with PROV: A Practical Approach. In EDBT/ICDT Workshops, pp. 282-289.
De Oliveira, D., Silva, V. and Mattoso, M. (2015). How Much Domain Data Should Be in Provenance Databases? In Workshop on Theory and Practice of Provenance (TaPP).
Freire, J., Koop, D., Santos, E. and Silva, C. T. (2008). Provenance for Computational Tasks: A Survey. Computing in Science Engineering, v. 10, n. 3, p. 11–21.
Gonçalves, J. C. de A. R., Oliveira, D. De, Ocaña, K. A. C. S., Ogasawara, E. and Mattoso, M. (2012). Using Domain-Specific Data to Enhance Scientific Workflow Steering Queries. In International Provenance and Annotation Workshop (IPAW), pp. 152–167.
Huang, J., Antova, L., Koch, C. and Olteanu, D. (2009). MayBMS: A Probabilistic Database Management System. In Int. Conf. Management of Data (SIGMOD), pp. 1071-1071.
Idika, N., Varia, M. and Phan, H. (2013). The Probabilistic Provenance Graph. In IEEE Security and Privacy Workshops (SPW), pp 34-41.
Mattoso, M., Werner, C., Travassos, G. H., et al. (2010). Towards supporting the life cycle of large scale scientific experiments. Int. Journal of Business Process Integration and Management, v. 5, n. 1, p. 79.
Moreau, L., Clifford, B., Freire, J., et al. (2011). The Open Provenance Model core specification (v1.1). Future Generation Computer Systems, v. 27, n. 6, p. 743–756.
Moreau, L. and Missier, P. (2013). The PROV Data Model and Abstract Syntax Notation. W3C Recommendation.
Ocaña, K. A. C. S., Oliveira, D. De, Ogasawara, E., et al. (2011). SciPhy: A Cloud-Based Workflow for Phylogenetic Analysis of Drug Targets in Protozoan Genomes. In Advances in Bioinformatics and Computational Biology, pp. 66-70.
Ogasawara, E., Dias, J., Oliveira, D., et al. (2011). An Algebraic Approach for Data-Centric Scientific Workflows. Proc. of the Int. Conf. on Very Large Data Bases (PVLDB), v. 4, n. 12, p. 1328–1339.
Re, C. and Suciu, D. (2007). Management of Data with Uncertainties. In Conference on Information and Knowledge Management (CIKM), pp. 3-8.
Simmhan, Y. L., Plale, B. and Gannon, D. (2008). Query capabilities of the Karma provenance framework. Concurrency and Computation: Practice and Experience, v. 20, n. 5, p. 441–451.
Boulos, J., Dalvi, N., Mandhani, B., et al. (2005). MYSTIQ: A System for Finding More Answers by Using Probabilities. In Int. Conf. Management of Data (SIGMOD), pp. 891-893.
Chapman, A., Blaustein, B. and Elsaesser, C. (2010). Provenance-based Belief. In Workshop on the Theory and Practice of Provenance (TaPP). p. 11.
Costa, F., Silva, V., De Oliveira, D., et al. (2013). Capturing and Querying Workflow Runtime Provenance with PROV: A Practical Approach. In EDBT/ICDT Workshops, pp. 282-289.
De Oliveira, D., Silva, V. and Mattoso, M. (2015). How Much Domain Data Should Be in Provenance Databases? In Workshop on Theory and Practice of Provenance (TaPP).
Freire, J., Koop, D., Santos, E. and Silva, C. T. (2008). Provenance for Computational Tasks: A Survey. Computing in Science Engineering, v. 10, n. 3, p. 11–21.
Gonçalves, J. C. de A. R., Oliveira, D. De, Ocaña, K. A. C. S., Ogasawara, E. and Mattoso, M. (2012). Using Domain-Specific Data to Enhance Scientific Workflow Steering Queries. In International Provenance and Annotation Workshop (IPAW), pp. 152–167.
Huang, J., Antova, L., Koch, C. and Olteanu, D. (2009). MayBMS: A Probabilistic Database Management System. In Int. Conf. Management of Data (SIGMOD), pp. 1071-1071.
Idika, N., Varia, M. and Phan, H. (2013). The Probabilistic Provenance Graph. In IEEE Security and Privacy Workshops (SPW), pp 34-41.
Mattoso, M., Werner, C., Travassos, G. H., et al. (2010). Towards supporting the life cycle of large scale scientific experiments. Int. Journal of Business Process Integration and Management, v. 5, n. 1, p. 79.
Moreau, L., Clifford, B., Freire, J., et al. (2011). The Open Provenance Model core specification (v1.1). Future Generation Computer Systems, v. 27, n. 6, p. 743–756.
Moreau, L. and Missier, P. (2013). The PROV Data Model and Abstract Syntax Notation. W3C Recommendation.
Ocaña, K. A. C. S., Oliveira, D. De, Ogasawara, E., et al. (2011). SciPhy: A Cloud-Based Workflow for Phylogenetic Analysis of Drug Targets in Protozoan Genomes. In Advances in Bioinformatics and Computational Biology, pp. 66-70.
Ogasawara, E., Dias, J., Oliveira, D., et al. (2011). An Algebraic Approach for Data-Centric Scientific Workflows. Proc. of the Int. Conf. on Very Large Data Bases (PVLDB), v. 4, n. 12, p. 1328–1339.
Re, C. and Suciu, D. (2007). Management of Data with Uncertainties. In Conference on Information and Knowledge Management (CIKM), pp. 3-8.
Simmhan, Y. L., Plale, B. and Gannon, D. (2008). Query capabilities of the Karma provenance framework. Concurrency and Computation: Practice and Experience, v. 20, n. 5, p. 441–451.
Publicado
04/10/2016
Como Citar
TALLARIDA, Gustavo; OCAÑA, Kary; PAES, Aline; BRAGANHOLO, Vanessa; DE OLIVEIRA, Daniel.
Gerência de Incerteza em Bancos de Dados de Proveniência de Workflows de Bioinformática. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 31. , 2016, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 181-186.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2016.24325.
