{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:20:07Z","timestamp":1750220407996,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":33,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,6,20]],"date-time":"2021-06-20T00:00:00Z","timestamp":1624147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,6,20]]},"DOI":"10.1145\/3462462.3468878","type":"proceedings-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T06:53:01Z","timestamp":1623999181000},"page":"1-4","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards understanding end-to-end learning in the context of data"],"prefix":"10.1145","author":[{"given":"Wentao","family":"Wu","sequence":"first","affiliation":[{"name":"Microsoft Research"}]},{"given":"Ce","family":"Zhang","sequence":"additional","affiliation":[{"name":"ETH Zurich, Switzerland"}]}],"member":"320","published-online":{"date-parts":[[2021,6,20]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Leonel Aguilar David Dao Shaoduo Gan Nezihe Merve Gurel Nora Hollenstein Jiawei Jiang Bojan Karlas Thomas Lemmin Tian Li Yang Li Susie Rao Johannes Rausch Cedric Renggli Luka Rimanic Maurice Weber Shuai Zhang Zhikuan Zhao Kevin Schawinski Wentao Wu and Ce Zhang. 2021. Ease.ML: A Lifecycle Management System for MLDev and MLOps. http:\/\/cidrdb.org\/cidr2021\/papers\/cidr2021_paper26.pdf. Accessed: 2021-6-4.  Leonel Aguilar David Dao Shaoduo Gan Nezihe Merve Gurel Nora Hollenstein Jiawei Jiang Bojan Karlas Thomas Lemmin Tian Li Yang Li Susie Rao Johannes Rausch Cedric Renggli Luka Rimanic Maurice Weber Shuai Zhang Zhikuan Zhao Kevin Schawinski Wentao Wu and Ce Zhang. 2021. Ease.ML: A Lifecycle Management System for MLDev and MLOps. http:\/\/cidrdb.org\/cidr2021\/papers\/cidr2021_paper26.pdf. Accessed: 2021-6-4."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.14778\/3007263.3007279"},{"key":"e_1_3_2_2_3_1","unstructured":"Eric Breck et al. 2019. Data Validation for Machine Learning. In SysML.  Eric Breck et al. 2019. Data Validation for Machine Learning. In SysML."},{"volume-title":"Sequential Information Maximization: When is Greedy Near-optimal? 40","year":"2015","author":"Chen Yuxin","key":"e_1_3_2_2_4_1"},{"key":"e_1_3_2_2_5_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"1320","author":"Cohen Jeremy","year":"2019"},{"volume-title":"Data Shapley: Equitable Valuation of Data for Machine Learning. (April","year":"2019","author":"Ghorbani Amirata","key":"e_1_3_2_2_6_1"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Stefan Grafberger Julia Stoyanovich and Sebastian Schelter. 2021. Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines. In CIDR.  Stefan Grafberger Julia Stoyanovich and Sebastian Schelter. 2021. Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines. In CIDR.","DOI":"10.1007\/s00778-021-00726-w"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1265530.1265535"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.14778\/3342263.3342637"},{"key":"e_1_3_2_2_10_1","volume-title":"Proceedings of Machine Learning Research (Proceedings of Machine Learning Research","volume":"1176","author":"Jia Ruoxi","year":"2019"},{"volume-title":"Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification? CVPR","year":"2021","author":"Jia Ruoxi","key":"e_1_3_2_2_11_1"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/3430915.3442426"},{"volume-title":"Hubert H K Teo, and Percy Liang","year":"2019","author":"Koh Pang Wei","key":"e_1_3_2_2_13_1"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305381.3305576"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.14778\/3229863.3240493"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.14778\/2994509.2994514"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.14778\/2994509.2994514"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3187009.3177737"},{"volume-title":"TFX: A TensorFlow-Based Production-Scale Machine Learning Platform. In KDD","year":"2017","author":"Naresh Akshay","key":"e_1_3_2_2_19_1"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3319874"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.14778\/3407790.3407816"},{"volume-title":"Carlo Curino, and Others","year":"2019","author":"Psallidas Fotis","key":"e_1_3_2_2_22_1"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.14778\/3157794.3157797"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137628.3137631"},{"key":"e_1_3_2_2_25_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"4585","author":"Sharchilev Boris","year":"2018"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939502.2939516"},{"volume-title":"RAB: Provable Robustness Against Backdoor Attacks. arXiv preprint arXiv:2003.08904","year":"2020","author":"Weber Maurice","key":"e_1_3_2_2_27_1"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389743"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389696"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457566"},{"volume-title":"Doris Jung-Lin Lee, Niloufar Salehi, and Aditya Parameswaran.","year":"2021","author":"Xin Doris","key":"e_1_3_2_2_31_1"},{"key":"e_1_3_2_2_32_1","unstructured":"M. Zaharia et al. 2018. Accelerating the Machine Learning Lifecycle with MLflow. IEEE Data Eng. Bull. (2018).  M. Zaharia et al. 2018. Accelerating the Machine Learning Lifecycle with MLflow. IEEE Data Eng. Bull. (2018)."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3060586"}],"event":{"name":"SIGMOD\/PODS '21: International Conference on Management of Data","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"],"location":"Virtual Event China","acronym":"SIGMOD\/PODS '21"},"container-title":["Proceedings of the Fifth Workshop on Data Management for End-To-End Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3462462.3468878","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3462462.3468878","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:19:02Z","timestamp":1750191542000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3462462.3468878"}},"subtitle":["machine learning dancing over semirings &amp; Codd's table"],"short-title":[],"issued":{"date-parts":[[2021,6,20]]},"references-count":33,"alternative-id":["10.1145\/3462462.3468878","10.1145\/3462462"],"URL":"https:\/\/doi.org\/10.1145\/3462462.3468878","relation":{},"subject":[],"published":{"date-parts":[[2021,6,20]]},"assertion":[{"value":"2021-06-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}