| Learned cardinalities: Estimating correlated joins with deep learning A Kipf, T Kipf, B Radke, V Leis, P Boncz, A Kemper Conference on Innovative Data Systems Research (CIDR-19), 2019 | 229 | 2019 |
| How good are modern spatial analytics systems? V Pandey, A Kipf, T Neumann, A Kemper Proceedings of the VLDB Endowment 11 (11), 1661-1673, 2018 | 80 | 2018 |
| RadixSpline: a single-pass learned index A Kipf, R Marcus, A van Renen, M Stoian, A Kemper, T Kraska, ... Proceedings of the Third International Workshop on Exploiting Artificial …, 2020 | 53 | 2020 |
| Benchmarking learned indexes R Marcus, A Kipf, A van Renen, M Stoian, S Misra, A Kemper, T Neumann, ... arXiv preprint arXiv:2006.12804, 2020 | 38 | 2020 |
| A proposed integrated data collection, analysis and sharing platform for impact evaluation A Kipf, W Brunette, J Kellerstrass, M Podolsky, J Rosa, M Sundt, D Wilson, ... Development Engineering 1, 36-44, 2016 | 31 | 2016 |
| SOSD: A benchmark for learned indexes A Kipf, R Marcus, A van Renen, M Stoian, A Kemper, T Kraska, ... NeurIPS Workshop on Machine Learning for Systems, 2019 | 29 | 2019 |
| Estimating Cardinalities with Deep Sketches A Kipf, D Vorona, J Müller, T Kipf, B Radke, V Leis, P Boncz, T Neumann, ... Proceedings of the 2019 International Conference on Management of Data, 2019 | 28 | 2019 |
| High-performance geospatial analytics in hyperspace V Pandey, A Kipf, D Vorona, T Mühlbauer, T Neumann, A Kemper Proceedings of the 2016 International Conference on Management of Data, 2145 …, 2016 | 26 | 2016 |
| Make the most out of your SIMD investments: counter control flow divergence in compiled query pipelines H Lang, L Passing, A Kipf, P Boncz, T Neumann, A Kemper The VLDB Journal 29 (2), 757-774, 2020 | 22 | 2020 |
| Adaptive main-memory indexing for high-performance point-polygon joins A Kipf, H Lang, VN Pandey, RA Persa, C Anneser, ET Zacharatou, ... | 22* | 2020 |
| Analytics on Fast Data: Main-Memory Database Systems versus Modern Streaming Systems. A Kipf, V Pandey, J Böttcher, L Braun, T Neumann, A Kemper EDBT, 49-60, 2017 | 22 | 2017 |
| The Case for Learned Spatial Indexes V Pandey, A van Renen, A Kipf, I Sabek, J Ding, A Kemper 2nd International Workshop on Applied AI for Database Systems and Applications, 2020 | 19 | 2020 |
| Low-latency communication for fast DBMS using RDMA and shared memory P Fent, A van Renen, A Kipf, V Leis, T Neumann, A Kemper 2020 IEEE 36th International Conference on Data Engineering (ICDE), 1477-1488, 2020 | 14 | 2020 |
| Approximate geospatial joins with precision guarantees A Kipf, H Lang, V Pandey, RA Persa, P Boncz, T Neumann, A Kemper 2018 IEEE 34th International Conference on Data Engineering (ICDE), 1360-1363, 2018 | 13 | 2018 |
| DeepSPACE: Approximate Geospatial Query Processing with Deep Learning D Vorona, A Kipf, T Neumann, A Kemper 27th ACM SIGSPATIAL International Conference on Advances in Geographic …, 2019 | 12 | 2019 |
| Estimating Filtered Group-By Queries is Hard: Deep Learning to the Rescue A Kipf, M Freitag, D Vorona, P Boncz, T Neumann, A Kemper 1st International Workshop on Applied AI for Database Systems and Applications, 2019 | 10 | 2019 |
| Flow-Loss: learning cardinality estimates that matter P Negi, R Marcus, A Kipf, H Mao, N Tatbul, T Kraska, M Alizadeh arXiv preprint arXiv:2101.04964, 2021 | 9 | 2021 |
| Scalable analytics on fast data A Kipf, V Pandey, J Böttcher, L Braun, T Neumann, A Kemper ACM Transactions on Database Systems (TODS) 44 (1), 1-35, 2019 | 8 | 2019 |
| The Case for Distance-Bounded Spatial Approximations E Tzirita Zacharatou, A Kipf, I Sabek, V Pandey, H Doraiswamy, V Markl Conference on Innovative Data Systems Research (CIDR-21), 2021 | 7* | 2021 |
| High-performance main-memory database systems and modern virtualization: Friends or foes? T Mühlbauer, W Rödiger, A Kipf, A Kemper, T Neumann Proceedings of the Fourth Workshop on Data analytics in the Cloud, 1-4, 2015 | 6 | 2015 |
Thomas NeumannProfessor, Technische Universität MünchenVerified email at in.tum.de
