FReeP: towards parameter recommendation in scientific workflows using preference learning
Resumo
Scientific workflows are a de facto standard for modeling scientific experiments. However, several workflows have too many parameters to be manually configured. Poor choices of parameter values may lead to unsuccessful executions of the workflow. In this paper, we present FReeP, a parameter recommendation algorithm that suggests a value to a parameter that agrees with the user preferences. FReeP is based on the Preference Learning technique. A preliminary experimental evaluation performed over the SciPhy workflow showed the feasibility of FReeP to recommend parameter values for scientific workflows.
Palavras-chave:
Scientific Workflows, Preference Learning, Parameter Recommendation
Referências
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Cheng, Z., Zhou, Z., and Wang, X. (2015). Scientific workflow clustering and recommendation. In 11th International Conf. on Semantics, Knowledge and Grids (SKG), pages 272–274.
Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1):21–27.
Das, J., Mukherjee, P., Majumder, S., and Gupta, P. (2014). Clustering-based recommender system using principles of voting theory. In IC3I, pages 230–235. IEEE.
Fishburn, P. C. (1974). Simple voting systems and majority rule. Systems Research and Behavioral Science, 19(3):166–176.
Freire, J., Koop, D., Santos, E., and Silva, C. T. (2008). Provenance for Computational Tasks: A Survey. Computing in Science & Engineering, pages 20–30.
Fürnkranz, J. and Hüllermeier, E. (2011). Preference learning. In Encyclopedia of Machine Learning, pages 789–795. Springer.
Halioui, A., Valtchev, P., and Diallo, A. B. (2016). Towards an ontology-based recommender system for relevant bioinformatics workflows. bioRxiv, page 082776.
Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 22(1):5–53.
Hüllermeier, E., Fürnkranz, J., Cheng, W., and Brinker, K. (2008). Label ranking by learning pairwise preferences. Artificial Intelligence, 172(16-17):1897–1916.
Mukherjee, R., Sajja, N., and Sen, S. (2003). A movie recommendation system–an application of voting theory in user modeling. User Modeling and User-Adapted Interaction, 13(1-2):5–33.
Ocaña, K. A., de Oliveira, D., Ogasawara, E., Dávila, A. M., Lima, A. A., and Mattoso, M. (2011). Sciphy: a cloud-based workflow for phylogenetic analysis of drug targets in protozoan genomes. In BSB11, pages 66–70. Springer.
Pessiot, J., Truong, T., Usunier, N., Amini, M., and Gallinari, P. (2007). Learning to rank for collaborative filtering. In ICEIS 2007 - Proc. of the 9th International Conf. on Enterprise Information Systems, pages 145–151.
Refaeilzadeh, P., Tang, L., and Liu, H. (2016). Cross-validation. Encyclopedia of database systems, pages 1–7.
Soomro, K., Munir, K., and McClatchey, R. (2015). Incorporating semantics in pattern-based scientific workflow recommender systems: Improving the accuracy of recommendations. In SAI’2015, pages 565–571. IEEE.
Zhao, Y., Raicu, I., and Foster, I. (2008). Scientific workflow systems for 21st century, new bottle or new wine? In IEEE Services, pages 467–471. IEEE.
Cheng, Z., Zhou, Z., and Wang, X. (2015). Scientific workflow clustering and recommendation. In 11th International Conf. on Semantics, Knowledge and Grids (SKG), pages 272–274.
Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1):21–27.
Das, J., Mukherjee, P., Majumder, S., and Gupta, P. (2014). Clustering-based recommender system using principles of voting theory. In IC3I, pages 230–235. IEEE.
Fishburn, P. C. (1974). Simple voting systems and majority rule. Systems Research and Behavioral Science, 19(3):166–176.
Freire, J., Koop, D., Santos, E., and Silva, C. T. (2008). Provenance for Computational Tasks: A Survey. Computing in Science & Engineering, pages 20–30.
Fürnkranz, J. and Hüllermeier, E. (2011). Preference learning. In Encyclopedia of Machine Learning, pages 789–795. Springer.
Halioui, A., Valtchev, P., and Diallo, A. B. (2016). Towards an ontology-based recommender system for relevant bioinformatics workflows. bioRxiv, page 082776.
Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 22(1):5–53.
Hüllermeier, E., Fürnkranz, J., Cheng, W., and Brinker, K. (2008). Label ranking by learning pairwise preferences. Artificial Intelligence, 172(16-17):1897–1916.
Mukherjee, R., Sajja, N., and Sen, S. (2003). A movie recommendation system–an application of voting theory in user modeling. User Modeling and User-Adapted Interaction, 13(1-2):5–33.
Ocaña, K. A., de Oliveira, D., Ogasawara, E., Dávila, A. M., Lima, A. A., and Mattoso, M. (2011). Sciphy: a cloud-based workflow for phylogenetic analysis of drug targets in protozoan genomes. In BSB11, pages 66–70. Springer.
Pessiot, J., Truong, T., Usunier, N., Amini, M., and Gallinari, P. (2007). Learning to rank for collaborative filtering. In ICEIS 2007 - Proc. of the 9th International Conf. on Enterprise Information Systems, pages 145–151.
Refaeilzadeh, P., Tang, L., and Liu, H. (2016). Cross-validation. Encyclopedia of database systems, pages 1–7.
Soomro, K., Munir, K., and McClatchey, R. (2015). Incorporating semantics in pattern-based scientific workflow recommender systems: Improving the accuracy of recommendations. In SAI’2015, pages 565–571. IEEE.
Zhao, Y., Raicu, I., and Foster, I. (2008). Scientific workflow systems for 21st century, new bottle or new wine? In IEEE Services, pages 467–471. IEEE.
Publicado
25/08/2018
Como Citar
SILVA JR., Daniel; PAES, Aline; PACITTI, Esther; DE OLIVEIRA, Daniel.
FReeP: towards parameter recommendation in scientific workflows using preference learning. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 33. , 2018, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 211-216.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2018.22232.
