| The case for learned index structures T Kraska, A Beutel, EH Chi, J Dean, N Polyzotis Proceedings of the 2018 international conference on management of data, 489-504, 2018 | 1200 | 2018 |
| CrowdDB: answering queries with crowdsourcing MJ Franklin, D Kossmann, T Kraska, S Ramesh, R Xin Proceedings of the 2011 ACM SIGMOD International Conference on Management of …, 2011 | 882 | 2011 |
| Crowder: Crowdsourcing entity resolution J Wang, T Kraska, MJ Franklin, J Feng arXiv preprint arXiv:1208.1927, 2012 | 754 | 2012 |
| MLbase: A Distributed Machine-learning System. T Kraska, A Talwalkar, JC Duchi, R Griffith, MJ Franklin, MI Jordan Cidr 1, 2-1, 2013 | 485 | 2013 |
| Neo: A learned query optimizer R Marcus, P Negi, H Mao, C Zhang, M Alizadeh, T Kraska, ... arXiv preprint arXiv:1904.03711, 2019 | 458 | 2019 |
| Building a database on S3 M Brantner, D Florescu, D Graf, D Kossmann, T Kraska Proceedings of the 2008 ACM SIGMOD international conference on Management of …, 2008 | 447 | 2008 |
| An evaluation of alternative architectures for transaction processing in the cloud D Kossmann, T Kraska, S Loesing Proceedings of the 2010 ACM SIGMOD International Conference on Management of …, 2010 | 402 | 2010 |
| Consistency rationing in the cloud: Pay only when it matters T Kraska, M Hentschel, G Alonso, D Kossmann Proceedings of the VLDB Endowment 2 (1), 253-264, 2009 | 375 | 2009 |
| MDCC: Multi-data center consistency T Kraska, G Pang, MJ Franklin, S Madden, A Fekete Proceedings of the 8th ACM European Conference on Computer Systems, 113-126, 2013 | 354 | 2013 |
| ALEX: an updatable adaptive learned index J Ding, UF Minhas, J Yu, C Wang, J Do, Y Li, H Zhang, B Chandramouli, ... Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 334 | 2020 |
| Superneurons: Dynamic GPU memory management for training deep neural networks L Wang, J Ye, Y Zhao, W Wu, A Li, SL Song, Z Xu, T Kraska Proceedings of the 23rd ACM SIGPLAN symposium on principles and practice of …, 2018 | 310 | 2018 |
| Sherlock: A deep learning approach to semantic data type detection M Hulsebos, K Hu, M Bakker, E Zgraggen, A Satyanarayan, T Kraska, ... Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 268 | 2019 |
| Leveraging transitive relations for crowdsourced joins J Wang, G Li, T Kraska, MJ Franklin, J Feng Proceedings of the 2013 ACM SIGMOD International Conference on Management of …, 2013 | 268 | 2013 |
| How is the weather tomorrow? Towards a benchmark for the cloud C Binnig, D Kossmann, T Kraska, S Loesing Proceedings of the Second International Workshop on Testing Database Systems …, 2009 | 267 | 2009 |
| Vizml: A machine learning approach to visualization recommendation K Hu, MA Bakker, S Li, T Kraska, C Hidalgo Proceedings of the 2019 CHI conference on human factors in computing systems …, 2019 | 265 | 2019 |
| MLI: An API for distributed machine learning ER Sparks, A Talwalkar, V Smith, J Kottalam, X Pan, J Gonzalez, ... 2013 IEEE 13th International Conference on Data Mining, 1187-1192, 2013 | 247 | 2013 |
| Fiting-tree: A data-aware index structure A Galakatos, M Markovitch, C Binnig, R Fonseca, T Kraska Proceedings of the 2019 international conference on management of data, 1189 …, 2019 | 246* | 2019 |
| Learning multi-dimensional indexes V Nathan, J Ding, M Alizadeh, T Kraska Proceedings of the 2020 ACM SIGMOD international conference on management of …, 2020 | 242 | 2020 |
| Bao: Making learned query optimization practical R Marcus, P Negi, H Mao, N Tatbul, M Alizadeh, T Kraska Proceedings of the 2021 International Conference on Management of Data, 1275 …, 2021 | 235 | 2021 |
| The end of slow networks: It's time for a redesign C Binnig, A Crotty, A Galakatos, T Kraska, E Zamanian arXiv preprint arXiv:1504.01048, 2015 | 224 | 2015 |
Michael FranklinUniversity of ChicagoПотвърден имейл адрес: uchicago.edu
