| 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 | 485 | 2014 |
| 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 | 67 | 2012 |
| "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 | 60 | 2013 |
| 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 | 55 | 2013 |
| Automatically Tracking Metadata and Provenance of Machine Learning Experiments S Schelter, JH Boese, J Kirschnick, T Klein, S Seufert NIPS Workshop ML Systems, 2017 | 50 | 2017 |
| Collaborative filtering with apache mahout S Schelter, S Owen Proc. of ACM RecSys Challenge, 2012 | 50 | 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 | 44 | 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 | 42 | 2018 |
| 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 | 34 | 2013 |
| Samsara: Declarative machine learning on distributed dataflow systems S Schelter, A Palumbo, S Quinn, S Marthi, A Musselman NIPS Workshop MLSystems, 2016 | 31 | 2016 |
| 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 | 26 | 2018 |
| Factorbird-a parameter server approach to distributed matrix factorization S Schelter, V Satuluri, R Zadeh arXiv preprint arXiv:1411.0602, 2014 | 26 | 2014 |
| On Challenges in Machine Learning Model Management. S Schelter, F Biessmann, T Januschowski, D Salinas, S Seufert, ... IEEE Data Eng. Bull. 41 (4), 5-15, 2018 | 24 | 2018 |
| 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 | 24 | 2015 |
| 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 | 21 | 2018 |
| 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 | 19 | 2016 |
| Blockjoin: Efficient matrix partitioning through joins A Kunft, A Katsifodimos, S Schelter, T Rabl, V Markl Proceedings of the VLDB Endowment 10 (13), 2061-2072, 2017 | 17 | 2017 |
| Efficient sample generation for scalable meta learning S Schelter, J Soto, V Markl, D Burdick, B Reinwald, A Evfimievski 2015 IEEE 31st International Conference on Data Engineering, 1191-1202, 2015 | 17 | 2015 |
| 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 | 11 | 2019 |
| Structural patterns in the rise of Germany’s new right on Facebook S Schelter, F Biessmann, M Zobel, N Teneva 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW …, 2016 | 10 | 2016 |
Volker MarklTechnische Universität BerlinVerified email at tu-berlin.de
