| Machine learning on big data: Opportunities and challenges L Zhou, S Pan, J Wang, AV Vasilakos Neurocomputing 237, 350-361, 2017 | 324 | 2017 |
| Kepler+ Hadoop: a general architecture facilitating data-intensive applications in scientific workflow systems J Wang, D Crawl, I Altintas Proceedings of the 4th Workshop on Workflows in Support of Large-Scale …, 2009 | 167 | 2009 |
| Pattern based property specification and verification for service composition J Yu, TP Manh, J Han, Y Jin, Y Han, J Wang International Conference on Web Information Systems Engineering, 156-168, 2006 | 133 | 2006 |
| Smart manufacturing J Davis, T Edgar, R Graybill, P Korambath, B Schott, D Swink, J Wang, ... Annual review of chemical and biomolecular engineering 6, 141-160, 2015 | 118 | 2015 |
| Big data provenance: Challenges, state of the art and opportunities J Wang, D Crawl, S Purawat, M Nguyen, I Altintas 2015 IEEE International Conference on Big Data (Big Data), 2509-2516, 2015 | 69 | 2015 |
| Workflow as a service in the cloud: architecture and scheduling algorithms J Wang, P Korambath, I Altintas, J Davis, D Crawl Procedia computer science 29, 546-556, 2014 | 63 | 2014 |
| CAFISE: An approach to enabling adaptive configuration of service grid applications YB Han, ZF Zhao, G Li, DS Xing, QZ Lv, JW Wang, JH Xiong, H Liu Journal of Computer Science and Technology 18 (4), 484, 2003 | 55 | 2003 |
| Provenance for mapreduce-based data-intensive workflows D Crawl, J Wang, I Altintas Proceedings of the 6th Workshop on Workflows in Support of Large-scale …, 2011 | 52 | 2011 |
| Cloud computing in e-Science: research challenges and opportunities X Yang, D Wallom, S Waddington, J Wang, A Shaon, B Matthews, ... The Journal of Supercomputing 70 (1), 408-464, 2014 | 46 | 2014 |
| Deploying kepler workflows as services on a cloud infrastructure for smart manufacturing P Korambath, J Wang, A Kumar, L Hochstein, B Schott, R Graybill, ... Procedia Computer Science 29, 2254-2259, 2014 | 43 | 2014 |
| Challenges and approaches for distributed workflow-driven analysis of large-scale biological data I Altintas, J Wang, D Crawl, W Li Proceedings of the 2012 Joint EDBT/ICDT Workshops, 73-78, 2012 | 37 | 2012 |
| Big data applications using workflows for data parallel computing J Wang, D Crawl, I Altintas, W Li Computing in Science & Engineering 16 (4), 11-21, 2014 | 35 | 2014 |
| Early cloud experiences with the kepler scientific workflow system J Wang, I Altintas Procedia Computer Science 9, 1630-1634, 2012 | 33 | 2012 |
| A scalable data science workflow approach for big data bayesian network learning J Wang, Y Tang, M Nguyen, I Altintas 2014 IEEE/ACM International Symposium on Big Data Computing, 16-25, 2014 | 32 | 2014 |
| A smart manufacturing use case: Furnace temperature balancing in steam methane reforming process via kepler workflows P Korambath, J Wang, A Kumar, J Davis, R Graybill, B Schott, M Baldea Procedia Computer Science 80, 680-689, 2016 | 27 | 2016 |
| A service modeling approach with business-level reusability and extensibility J Wang, J Yu, Y Han IEEE International Workshop on Service-Oriented System Engineering (SOSE'05 …, 2005 | 26 | 2005 |
| A service virtualization mechanism for business user programming ZF Zhao, YB Han, J Yu, JW Wang Journal of Computer Research and Development 41 (12), 2224-2230, 2004 | 26 | 2004 |
| A framework for distributed data-parallel execution in the Kepler scientific workflow system J Wang, D Crawl, I Altintas Procedia Computer Science 9, 1620-1629, 2012 | 24 | 2012 |
| Comparison of distributed data-parallelization patterns for big data analysis: A bioinformatics case study J Wang, D Crawl, I Altintas, K Tzoumas, V Markl Proceedings of the Fourth International Workshop on Data Intensive Computing …, 2013 | 23 | 2013 |
| A hybrid learning framework for imbalanced stream classification W Zhang, J Wang 2017 IEEE International Congress on Big Data (BigData Congress), 480-487, 2017 | 21 | 2017 |
Ilkay AltintasSDSC/UCSDVerified email at sdsc.edu
