On graph reduction for QoS prediction of very large web service compositions (2012)
- Authors:
- USP affiliated authors: LEJBMAN, ALFREDO GOLDMAN VEL - IME ; NGOKO, YANIK MARTIAL - IME
- Unidade: IME
- DOI: 10.1109/SCC.2012.21
- Subjects: WORLD WIDE WEB; COMPUTABILIDADE E COMPLEXIDADE; TEORIA DOS GRAFOS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Piscataway
- Date published: 2012
- Source:
- Título: Proceedings
- Conference titles: International Conference on Services Computing - SCC
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
GOLDMAN, Alfredo e NGOKO, Yanik. On graph reduction for QoS prediction of very large web service compositions. 2012, Anais.. Piscataway: IEEE, 2012. Disponível em: https://doi.org/10.1109/SCC.2012.21. Acesso em: 19 fev. 2026. -
APA
Goldman, A., & Ngoko, Y. (2012). On graph reduction for QoS prediction of very large web service compositions. In Proceedings. Piscataway: IEEE. doi:10.1109/SCC.2012.21 -
NLM
Goldman A, Ngoko Y. On graph reduction for QoS prediction of very large web service compositions [Internet]. Proceedings. 2012 ;[citado 2026 fev. 19 ] Available from: https://doi.org/10.1109/SCC.2012.21 -
Vancouver
Goldman A, Ngoko Y. On graph reduction for QoS prediction of very large web service compositions [Internet]. Proceedings. 2012 ;[citado 2026 fev. 19 ] Available from: https://doi.org/10.1109/SCC.2012.21 - Malleable resource sharing algorithms for cooperative resolution of problems
- A comparative study on task dependent scheduling algorithms for grid computing
- An analytical approach for predicting QoS of web services choreographies
- The influence of organizational factors on inter-team knowledge sharing effectiveness in agile environments
- Improving the performance of actor model runtime environments on multicore and manycore platforms
- Towards automatic actor pinning on multi-core architectures
- A simple BSP-based model to predict execution time in GPU applications
- A comparison of GPU execution time prediction using machine learning and analytical modeling
- Message from the program committee co-chairs. [Apresentação]
- Useful statistical methods for human factors research in software engineering: a discussion on validation with quantitative data
Informações sobre o DOI: 10.1109/SCC.2012.21 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
