Mercury: Hybrid centralized and distributed scheduling in large shared clusters K Karanasos, S Rao, C Curino, C Douglas, K Chaliparambil, ... 2015 USENIX Annual Technical Conference (USENIX ATC 15), 485-497, 2015 | 242 | 2015 |
Wanalytics: Geo-distributed analytics for a data intensive world A Vulimiri, C Curino, PB Godfrey, T Jungblut, K Karanasos, J Padhye, ... Proceedings of the 2015 ACM SIGMOD international conference on management of …, 2015 | 183 | 2015 |
Efficient queue management for cluster scheduling J Rasley, K Karanasos, S Kandula, R Fonseca, M Vojnovic, S Rao Proceedings of the Eleventh European Conference on Computer Systems, 1-15, 2016 | 140 | 2016 |
View selection in semantic web databases F Goasdoué, K Karanasos, J Leblay, I Manolescu arXiv preprint arXiv:1110.6648, 2011 | 133 | 2011 |
Medea scheduling of long running applications in shared production clusters P Garefalakis, K Karanasos, P Pietzuch, A Suresh, S Rao Proceedings of the thirteenth EuroSys conference, 1-13, 2018 | 128 | 2018 |
Selecting subexpressions to materialize at datacenter scale A Jindal, K Karanasos, S Rao, H Patel Proceedings of the VLDB Endowment 11 (7), 800-812, 2018 | 117 | 2018 |
Hydra: a federated resource manager for data-center scale analytics C Curino, S Krishnan, K Karanasos, S Rao, GM Fumarola, B Huang, ... 16th USENIX Symposium on Networked Systems Design and Implementation (NSDI …, 2019 | 84 | 2019 |
Extending relational query processing with ML inference K Karanasos, M Interlandi, D Xin, F Psallidas, R Sen, K Park, I Popivanov, ... arXiv preprint arXiv:1911.00231, 2019 | 77 | 2019 |
Computation reuse in analytics job service at microsoft A Jindal, S Qiao, H Patel, Z Yin, J Di, M Bag, M Friedman, Y Lin, ... Proceedings of the 2018 International Conference on Management of Data, 191-203, 2018 | 72 | 2018 |
Data science through the looking glass: Analysis of millions of github notebooks and ml. net pipelines F Psallidas, Y Zhu, B Karlas, J Henkel, M Interlandi, S Krishnan, B Kroth, ... ACM SIGMOD Record 51 (2), 30-37, 2022 | 55 | 2022 |
A tensor compiler for unified machine learning prediction serving S Nakandala, K Saur, GI Yu, K Karanasos, C Curino, M Weimer, ... 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2020 | 54 | 2020 |
Fact checking and analyzing the web F Goasdoué, K Karanasos, Y Katsis, J Leblay, I Manolescu, S Zampetakis Proceedings of the 2013 ACM SIGMOD International Conference on Management of …, 2013 | 48 | 2013 |
Dynamically optimizing queries over large scale data platforms K Karanasos, A Balmin, M Kutsch, F Ozcan, V Ercegovac, C Xia, ... Proceedings of the 2014 ACM SIGMOD international conference on Management of …, 2014 | 44 | 2014 |
End-to-end optimization of machine learning prediction queries K Park, K Saur, D Banda, R Sen, M Interlandi, K Karanasos Proceedings of the 2022 International Conference on Management of Data, 587-601, 2022 | 38 | 2022 |
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML A Agrawal, R Chatterjee, C Curino, A Floratou, N Gowdal, M Interlandi, ... arXiv preprint arXiv:1909.00084, 2019 | 36 | 2019 |
Efficient XQuery rewriting using multiple views I Manolescu, K Karanasos, V Vassalos, S Zoupanos 2011 IEEE 27th International Conference on Data Engineering, 972-983, 2011 | 34 | 2011 |
Growing triples on trees: an XML-RDF hybrid model for annotated documents F Goasdoué, K Karanasos, Y Katsis, J Leblay, I Manolescu, S Zampetakis The VLDB Journal 22, 589-613, 2013 | 30 | 2013 |
Query processing on tensor computation runtimes D He, S Nakandala, D Banda, R Sen, K Saur, K Park, C Curino, ... arXiv preprint arXiv:2203.01877, 2022 | 29 | 2022 |
Neptune: Scheduling suspendable tasks for unified stream/batch applications P Garefalakis, K Karanasos, P Pietzuch Proceedings of the ACM symposium on cloud computing, 233-245, 2019 | 28 | 2019 |
Query and resource optimization: Bridging the gap L Viswanathan, A Jindal, K Karanasos 2018 IEEE 34th International Conference on Data Engineering (ICDE), 1384-1387, 2018 | 27 | 2018 |