{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:53:36Z","timestamp":1773482016937,"version":"3.50.1"},"reference-count":5,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>\n            Machine learning (ML) has gained a pivotal role in answering complex predictive analytic queries. Model building for large scale datasets is one of the time consuming parts of the data science pipeline. Often data scientists are willing to sacrifice some accuracy in order to speed up this process during the exploratory phase. In this paper, we propose to demonstrate ApproxML, a system that efficiently constructs approximate ML models for new queries from previously constructed ML models using the concepts of\n            <jats:italic>model materialization<\/jats:italic>\n            and\n            <jats:italic>reuse<\/jats:italic>\n            . ApproxML supports a variety of ML models such as generalized linear models for supervised learning, and K-means and Gaussian Mixture model for unsupervised learning.\n          <\/jats:p>","DOI":"10.14778\/3352063.3352096","type":"journal-article","created":{"date-parts":[[2019,9,18]],"date-time":"2019-09-18T18:36:11Z","timestamp":1568831771000},"page":"1906-1909","source":"Crossref","is-referenced-by-count":2,"title":["ApproxML"],"prefix":"10.14778","volume":"12","author":[{"given":"Sona","family":"Hasani","sequence":"first","affiliation":[{"name":"University of Texas at Arlington"}]},{"given":"Faezeh","family":"Ghaderi","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington"}]},{"given":"Shohedul","family":"Hasan","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago"}]},{"given":"Saravanan","family":"Thirumuruganathan","sequence":"additional","affiliation":[{"name":"QCRI, HBKU"}]},{"given":"Abolfazl","family":"Asudeh","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago"}]},{"given":"Nick","family":"Koudas","sequence":"additional","affiliation":[{"name":"University of Toronto"}]},{"given":"Gautam","family":"Das","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington"}]}],"member":"320","published-online":{"date-parts":[[2019,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.14778\/3236187.3269462"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2882952"},{"key":"e_1_2_1_3_1","volume-title":"et al. Scikit-learn: Machine learning in python. JMLR, 12(Oct):2825--2830","author":"Pedregosa F.","year":"2011","unstructured":"F. Pedregosa , G. Varoquaux , A. Gramfort , V. Michel , B. Thirion , O. Grisel , M. Blondel , P. Prettenhofer , R. Weiss , V. Dubourg , et al. Scikit-learn: Machine learning in python. JMLR, 12(Oct):2825--2830 , 2011 . F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al. Scikit-learn: Machine learning in python. JMLR, 12(Oct):2825--2830, 2011."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939502.2939516"},{"key":"e_1_2_1_5_1","volume-title":"Accelerating the machine learning lifecycle with mlflow","author":"Zaharia M.","year":"2018","unstructured":"M. Zaharia , A. Chen , A. Davidson , A. Ghodsi , S. A. Hong , A. Konwinski , S. Murching , T. Nykodym , P. Ogilvie , M. Parkhe , F. Xie , and C. Zumar . Accelerating the machine learning lifecycle with mlflow . In IEEE Data Engineering Bulletin , 41(4), 2018 . M. Zaharia, A. Chen, A. Davidson, A. Ghodsi, S. A. Hong, A. Konwinski, S. Murching, T. Nykodym, P. Ogilvie, M. Parkhe, F. Xie, and C. Zumar. Accelerating the machine learning lifecycle with mlflow. In IEEE Data Engineering Bulletin, 41(4), 2018."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3352063.3352096","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:32:52Z","timestamp":1672223572000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3352063.3352096"}},"subtitle":["efficient approximate ad-hoc ML models through materialization and reuse"],"short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":5,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["10.14778\/3352063.3352096"],"URL":"https:\/\/doi.org\/10.14778\/3352063.3352096","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2019,8]]}}}