| HORIZONTALLY SCALABLE ML PIPELINES WITH A FEATURE STORE AA Ormenisan, M Ismail, K Hammar, R Andersson, E Gebremeskel, ... | 19* | 2019 |
| Implicit provenance for machine learning artifacts AA Ormenisan, M Ismail, S Haridi, J Dowling Proceedings of MLSys 20, 2020 | 15 | 2020 |
| Time travel and provenance for machine learning pipelines AA Ormenisan, M Meister, F Buso, R Andersson, S Haridi, J Dowling 2020 USENIX Conference on Operational Machine Learning (OpML 20), 2020 | 10 | 2020 |
| Towards distribution transparency for supervised ML with oblivious training functions M Meister, S Sheikholeslami, R Andersson, AA Ormenisan, J Dowling Proc. Workshop MLOps Syst, 1-3, 2020 | 6 | 2020 |
| Fast and Flexible Networking for Message-Oriented Middleware L Kroll, AA Ormenişan, J Dowling 2017 IEEE 37th International Conference on Distributed Computing Systems …, 2017 | 3 | 2017 |
| KompicsTesting-Unit Testing Event Streams IW Ubah, L Kroll, AA Ormenisan, S Haridi arXiv preprint arXiv:1705.04669, 2017 | 1 | 2017 |
| The Hopsworks Feature Store for Machine Learning J de la Rúa Martínez, F Buso, A Kouzoupis, AA Ormenisan, S Niazi, ... Companion of the 2024 International Conference on Management of Data, 135-147, 2024 | | 2024 |
| Dela—Sharing Large Datasets between Hadoop Clusters AA Ormenişan, J Downling 2017 IEEE 37th International Conference on Distributed Computing Systems …, 2017 | | 2017 |
| Conflict free p2p replicated datatypes J Dowling, A Ormenisan | | 2017 |
| Providing a Data Model to the CATS key-value store AA Ormenisan | | 2013 |
| Reliable news over a gradient topology A Ormenisan, J Dowling | | |
