| The stratosphere platform for big data analytics A Alexandrov, R Bergmann, S Ewen, JC Freytag, F Hueske, A Heise, ... The VLDB Journal 23 (6), 939-964, 2014 | 554 | 2014 |
| Automatically Tracking Metadata and Provenance of Machine Learning Experiments S Schelter, JH Boese, J Kirschnick, T Klein, S Seufert NIPS Workshop ML Systems, 2017 | 85 | 2017 |
| Automating large-scale data quality verification S Schelter, D Lange, P Schmidt, M Celikel, F Biessmann, A Grafberger Proceedings of the VLDB Endowment 11 (12), 1781-1794, 2018 | 84 | 2018 |
| Scalable similarity-based neighborhood methods with mapreduce S Schelter, C Boden, V Markl Proceedings of the sixth ACM conference on Recommender systems, 163-170, 2012 | 74 | 2012 |
| Probabilistic demand forecasting at scale JH Böse, V Flunkert, J Gasthaus, T Januschowski, D Lange, D Salinas, ... Proceedings of the VLDB Endowment 10 (12), 1694-1705, 2017 | 67 | 2017 |
| "All roads lead to Rome": Optimistic recovery for distributed iterative data processing S Schelter, S Ewen, K Tzoumas, V Markl Proceedings of the 22nd ACM international conference on Information …, 2013 | 66 | 2013 |
| On challenges in machine learning model management S Schelter, F Biessmann, T Januschowski, D Salinas, S Seufert, ... | 62 | 2018 |
| Collaborative filtering with apache mahout S Schelter, S Owen Proc. of ACM RecSys Challenge, 2012 | 61 | 2012 |
| Distributed matrix factorization with mapreduce using a series of broadcast-joins S Schelter, C Boden, M Schenck, A Alexandrov, V Markl Proceedings of the 7th ACM Conference on Recommender Systems, 281-284, 2013 | 57 | 2013 |
| Iterative parallel data processing with stratosphere: an inside look S Ewen, S Schelter, K Tzoumas, D Warneke, V Markl Proceedings of the 2013 ACM SIGMOD International Conference on Management of …, 2013 | 39 | 2013 |
| Samsara: Declarative machine learning on distributed dataflow systems S Schelter, A Palumbo, S Quinn, S Marthi, A Musselman NIPS Workshop MLSystems, 2016 | 36 | 2016 |
| Deep Learning for Missing Value Imputation in Tables with Non-Numerical Data F Biessmann, D Salinas, S Schelter, P Schmidt, D Lange Proceedings of the 27th ACM International Conference on Information and …, 2018 | 35 | 2018 |
| On the Ubiquity of Web Tracking: Insights from a Billion-Page Web Crawl S Schelter, J Kunegis Journal of Web Science 4 (4), 53-66, 2018 | 34 | 2018 |
| Elastic machine learning algorithms in amazon sagemaker E Liberty, Z Karnin, B Xiang, L Rouesnel, B Coskun, R Nallapati, ... Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 30 | 2020 |
| Factorbird-a parameter server approach to distributed matrix factorization S Schelter, V Satuluri, R Zadeh arXiv preprint arXiv:1411.0602, 2014 | 30 | 2014 |
| Optimistic recovery for iterative dataflows in action S Dudoladov, C Xu, S Schelter, A Katsifodimos, S Ewen, K Tzoumas, ... Proceedings of the 2015 ACM SIGMOD International Conference on Management of …, 2015 | 26 | 2015 |
| Fairprep: Promoting data to a first-class citizen in studies on fairness-enhancing interventions S Schelter, Y He, J Khilnani, J Stoyanovich arXiv preprint arXiv:1911.12587, 2019 | 23 | 2019 |
| An intermediate representation for optimizing machine learning pipelines A Kunft, A Katsifodimos, S Schelter, S Breß, T Rabl, V Markl Proceedings of the VLDB Endowment 12 (11), 1553-1567, 2019 | 23 | 2019 |
| Tracking the trackers: A large-scale analysis of embedded web trackers S Schelter, J Kunegis Tenth International AAAI Conference on Web and Social Media, 2016 | 23 | 2016 |
| DataWig: Missing Value Imputation for Tables. F Biessmann, T Rukat, P Schmidt, P Naidu, S Schelter, A Taptunov, ... J. Mach. Learn. Res. 20, 175:1-175:6, 2019 | 19 | 2019 |
Volker MarklTechnische Universität BerlinVerified email at tu-berlin.de
