INTERACTIVE E-SCIENCE CYBERINFRASTRUCTURE FOR WORKFLOW MANAGEMENT COUPLED WITH BIGDATA TECHNOLOGY

Yu J., Buyya R. A taxonomy of workflow management systems for grid computing //Journal of Grid Computing. – 2005. – Т. 3. – №. 3-4. – С. 171-200.;

Foster I. et al. Cloud computing and grid computing 360 -degree compared //Grid Computing Environments Workshop, 2008. GCE'08. – Ieee, 2008. – С. 1-10.

Tansley S. et al. (ed.). The fourth paradigm: data -intensive scientific discovery. – Redmond, WA : Microsoft Research, 2009. – Т. 1.

Assunção M. D. et al. Big Data computing and clouds: Trends and future directions //Journal of Parallel and Distributed Computing. – 2014.

Manjunatha A. et al. Getting Code Near the Data: A Study of Generating Customized Data Intensive Scientific Workflows with DSL. – 2010.

Baranowski M., Belloum A., Bubak M. MapReduce Operations with WS -VLAM WMS //Procedia Computer Science. – 2013. – Т. 18. – С. 2599-2602.

Gil Y. et al. Time -bound analytic tasks on large datasets through dynamic configuration of workflows //Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science. – ACM, 2013. – С. 88-97.

Gil Y. et al. Examining the challenges of scientific workflows //Ieee computer. – 2007. – Т. 40. – №. 12. – С. 26-34.

Foster I., Kesselman C. Scaling system -level science: Scientific exp loration and IT implications //Computer. – 2006. – №. 11. – С. 31-39.

Boukhanovsky A. V., Kovalchuk S. V., Maryin S. V. Intelligent software platform for complex system computer simulation: conception, architecture and implementation //Izvestiya VUZov. Priborostroenie. – 2009. – Т. 10. – С. 5-24.

Knyazkov K. V. et al. CLAVIRE: e -Science infrastructure for data -driven computing //Journal of Computational Science. – 2012. – Т. 3. – №. 6. – С. 504- 510.

Kovalchuk S. V. et al. Knowledge -based Expressive Technolo gies within Cloud Computing Environments //PAoIS. – Springer Berlin Heidelberg, 2014. – С. 1-11.

Knyazkov K. V. et al. Interactive workflow -based infrastructure for urgent computing //Procedia Computer Science. – 2013. – Т. 18. – С. 2223-2232.

Kovalchuk S. V. et al. Virtual Simulation Objects concept as a framework for system-level simulation //arXiv preprint arXiv:1211.7080. – 2012.

Kovalchuk S. V. et al. A Technology for BigData Analysis Task Description Using Domain-specific Languages //Procedia Computer Science. – 2014. – Т. 29. – С. 488-498. International Multidisciplinary Scientific GeoConfenferences SGEM 2015 www.sgem.org

Yu J., Buyya R. A taxonomy of workflow management systems for grid computing //Journal of Grid Computing. – 2005. – Т. 3. – №. 3-4. – С. 171-200.;

Foster I. et al. Cloud computing and grid computing 360 -degree compared //Grid Computing Environments Workshop, 2008. GCE'08. – Ieee, 2008. – С. 1-10.

Tansley S. et al. (ed.). The fourth paradigm: data -intensive scientific discovery. – Redmond, WA : Microsoft Research, 2009. – Т. 1.

Assunção M. D. et al. Big Data computing and clouds: Trends and future directions //Journal of Parallel and Distributed Computing. – 2014.

Manjunatha A. et al. Getting Code Near the Data: A Study of Generating Customized Data Intensive Scientific Workflows with DSL. – 2010.

Baranowski M., Belloum A., Bubak M. MapReduce Operations with WS -VLAM WMS //Procedia Computer Science. – 2013. – Т. 18. – С. 2599-2602.

Gil Y. et al. Time -bound analytic tasks on large datasets through dynamic configuration of workflows //Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science. – ACM, 2013. – С. 88-97.

Gil Y. et al. Examining the challenges of scientific workflows //Ieee computer. – 2007. – Т. 40. – №. 12. – С. 26-34.

Foster I., Kesselman C. Scaling system -level science: Scientific exp loration and IT implications //Computer. – 2006. – №. 11. – С. 31-39.

Boukhanovsky A. V., Kovalchuk S. V., Maryin S. V. Intelligent software platform for complex system computer simulation: conception, architecture and implementation //Izvestiya VUZov. Priborostroenie. – 2009. – Т. 10. – С. 5-24.

Knyazkov K. V. et al. CLAVIRE: e -Science infrastructure for data -driven computing //Journal of Computational Science. – 2012. – Т. 3. – №. 6. – С. 504- 510.

Kovalchuk S. V. et al. Knowledge -based Expressive Technolo gies within Cloud Computing Environments //PAoIS. – Springer Berlin Heidelberg, 2014. – С. 1-11.

Knyazkov K. V. et al. Interactive workflow -based infrastructure for urgent computing //Procedia Computer Science. – 2013. – Т. 18. – С. 2223-2232.

Kovalchuk S. V. et al. Virtual Simulation Objects concept as a framework for system-level simulation //arXiv preprint arXiv:1211.7080. – 2012.

Kovalchuk S. V. et al. A Technology for BigData Analysis Task Description Using Domain-specific Languages //Procedia Computer Science. – 2014. – Т. 29. – С. 488-498. International Multidisciplinary Scientific GeoConfenferences SGEM 2015 www.sgem.org