| Anomaly detection in time series: a comprehensive evaluation S Schmidl, P Wenig, T Papenbrock Proceedings of the VLDB Endowment 15 (9), 1779-1797, 2022 | 417 | 2022 |
| Functional dependency discovery: An experimental evaluation of seven algorithms T Papenbrock, J Ehrlich, J Marten, T Neubert, JP Rudolph, M Schönberg, ... Proceedings of the VLDB Endowment 8 (10), 1082-1093, 2015 | 275 | 2015 |
| A hybrid approach to functional dependency discovery T Papenbrock, F Naumann Proceedings of the 2016 International Conference on Management of Data, 821-833, 2016 | 190 | 2016 |
| Progressive duplicate detection T Papenbrock, A Heise, F Naumann IEEE Transactions on knowledge and data engineering 27 (5), 1316-1329, 2014 | 164 | 2014 |
| Data profiling with metanome T Papenbrock, T Bergmann, M Finke, J Zwiener, F Naumann Proceedings of the VLDB Endowment 8 (12), 1860-1863, 2015 | 149 | 2015 |
| Data profiling Z Abedjan, L Golab, F Naumann, T Papenbrock Morgan & Claypool Publishers, 2019 | 147 | 2019 |
| Divide & conquer-based inclusion dependency discovery T Papenbrock, S Kruse, JA Quiané-Ruiz, F Naumann Proceedings of the VLDB Endowment 8 (7), 774-785, 2015 | 85 | 2015 |
| Data-driven Schema Normalization. T Papenbrock, F Naumann EDBT 17, 342-353, 2017 | 64 | 2017 |
| Data dependencies for query optimization: a survey J Kossmann, T Papenbrock, F Naumann The VLDB Journal 31 (1), 1-22, 2022 | 61 | 2022 |
| DynFD: Functional Dependency Discovery in Dynamic Datasets. P Schirmer, T Papenbrock, S Kruse, F Naumann, D Hempfing, T Mayer, ... EDBT, 253-264, 2019 | 60 | 2019 |
| TimeEval: A benchmarking toolkit for time series anomaly detection algorithms P Wenig, S Schmidl, T Papenbrock Proceedings of the VLDB Endowment 15 (12), 3678-3681, 2022 | 46 | 2022 |
| Detecting inclusion dependencies on very many tables F Tschirschnitz, T Papenbrock, F Naumann ACM Transactions on Database Systems (TODS) 42 (3), 1-29, 2017 | 44 | 2017 |
| A hybrid approach for efficient unique column combination discovery T Papenbrock, F Naumann Gesellschaft für Informatik, Bonn, 2017 | 44 | 2017 |
| MDedup: Duplicate detection with matching dependencies loannis Koumarelas, T Papenbrock, F Naumann Proceedings of the VLDB Endowment 13 (5), 712-725, 2020 | 32 | 2020 |
| Fast approximate discovery of inclusion dependencies S Kruse, T Papenbrock, C Dullweber, M Finke, M Hegner, M Zabel, ... Datenbanksysteme für Business, Technologie und Web (BTW 2017), 207-226, 2017 | 32 | 2017 |
| Approximate discovery of functional dependencies for large datasets T Bleifuß, S Bülow, J Frohnhofen, J Risch, G Wiese, S Kruse, ... Proceedings of the 25th ACM International on Conference on Information and …, 2016 | 32 | 2016 |
| Efficient discovery of matching dependencies P Schirmer, T Papenbrock, I Koumarelas, F Naumann ACM Transactions on Database Systems (TODS) 45 (3), 1-33, 2020 | 30 | 2020 |
| Hitting set enumeration with partial information for unique column combination discovery J Birnick, T Bläsius, T Friedrich, F Naumann, T Papenbrock, M Schirneck Proceedings of the VLDB Endowment 13 (12), 2270-2283, 2020 | 28 | 2020 |
| Scaling out the discovery of inclusion dependencies S Kruse, T Papenbrock, F Naumann Gesellschaft für Informatik eV, 2015 | 25 | 2015 |
| RDFind: Scalable conditional inclusion dependency discovery in RDF datasets S Kruse, A Jentzsch, T Papenbrock, Z Kaoudi, JA Quiané-Ruiz, ... Proceedings of the 2016 International Conference on Management of Data, 953-967, 2016 | 22 | 2016 |
Felix NaumannHasso Plattner Institute, University of Potsdamhpi.de의 이메일 확인됨