| Cardinality estimation with local deep learning models L Woltmann, C Hartmann, M Thiele, D Habich, W Lehner Proceedings of the second international workshop on exploiting artificial …, 2019 | 95 | 2019 |
| Simplicity Done Right for Join Ordering A Hertzschuch, C Hartmann, D Habich, W Lehner CIDR, 2021 | 25 | 2021 |
| Forecasting the data cube: A model configuration advisor for multi-dimensional data sets U Fischer, C Schildt, C Hartmann, W Lehner 2013 IEEE 29th International Conference on Data Engineering (ICDE), 853-864, 2013 | 23 | 2013 |
| Exploiting big data in time series forecasting: A cross-sectional approach C Hartmann, M Hahmann, W Lehner, F Rosenthal 2015 IEEE international conference on data science and advanced analytics …, 2015 | 22 | 2015 |
| CSAR: the cross-sectional autoregression model for short and long-range forecasting C Hartmann, F Ressel, M Hahmann, D Habich, W Lehner International Journal of Data Science and Analytics 8, 165-181, 2019 | 11 | 2019 |
| PostCENN: PostgreSQL with Machine Learning Models for Cardinality Estimation L Woltmann, D Olwig, C Hartmann, D Habich, W Lehner | 10 | 2021 |
| Machine learning-based cardinality estimation in dbms on pre-aggregated data L Woltmann, C Hartmann, D Habich, W Lehner arXiv preprint arXiv:2005.09367, 2020 | 7 | 2020 |
| CSAR: The cross-sectional autoregression model C Hartmann, M Hahmann, D Habich, W Lehner 2017 IEEE international conference on data science and advanced analytics …, 2017 | 7 | 2017 |
| Web-based benchmarks for forecasting systems: The ecast platform R Ulbricht, C Hartmann, M Hahmann, H Donker, W Lehner Proceedings of the 2016 International Conference on Management of Data, 2169 …, 2016 | 7 | 2016 |
| Turbo-charging SPJ query plans with learned physical join operator selections A Hertzschuch, C Hartmann, D Habich, W Lehner Proceedings of the VLDB Endowment 15 (11), 2706-2718, 2022 | 6 | 2022 |
| Aggregate-based training phase for ML-based cardinality estimation L Woltmann, C Hartmann, D Habich, W Lehner Datenbank-Spektrum 22 (1), 45-57, 2022 | 5 | 2022 |
| Best of both worlds: combining traditional and machine learning models for cardinality estimation L Woltmann, C Hartmann, D Habich, W Lehner Proceedings of the Third International Workshop on Exploiting Artificial …, 2020 | 5 | 2020 |
| Particulate Matter Matters—The Data Science Challenge@ BTW 2019 HJ Meyer, H Grunert, T Waizenegger, L Woltmann, C Hartmann, ... Datenbank-Spektrum, 1-18, 2019 | 5 | 2019 |
| Challenges for context-driven time series forecasting R Ulbricht, H Donker, C Hartmann, M Hahmann, W Lehner Journal of Data and Information Quality (JDIQ) 7 (1-2), 1-4, 2016 | 5 | 2016 |
| Season-and Trend-aware Symbolic Approximation for Accurate and Efficient Time Series Matching L Kegel, C Hartmann, M Thiele, W Lehner Datenbank-Spektrum 21, 225-236, 2021 | 3 | 2021 |
| Large-Scale Time Series Analytics M Hahmann, C Hartmann, L Kegel, W Lehner Datenbank-Spektrum, 1-13, 2019 | 3* | 2019 |
| Sensor-based jump detection and classification with Machine Learning in trampoline gymnastics L Woltmann, C Hartmann, W Lehner, P Rausch, K Ferger German Journal of Exercise and Sport Research 53 (2), 187-195, 2023 | 2 | 2023 |
| Ingredient-based Forecast of Sold Dish Portions in Campus Canteen Kitchens L Woltmann, J Drechsel, C Hartmann, W Lehner 2022 IEEE 38th International Conference on Data Engineering Workshops (ICDEW …, 2022 | 2 | 2022 |
| Assessing the Impact of Driving Bans with Data Analysis L Woltmann, C Hartmann, W Lehner | 2 | 2019 |
| Forecasting Large-scale Time Series Data C Hartmann Technische Universität Dresden, 2018 | 2 | 2018 |
Wolfgang LehnerTechnische Universität DresdenBestätigte E-Mail-Adresse bei tu-dresden.de
