{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:37:23Z","timestamp":1758271043025},"reference-count":13,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2018,8]]},"abstract":"<jats:p>\n            Data application developers and data scientists spend an inordinate amount of time\n            <jats:italic>iterating<\/jats:italic>\n            on machine learning (ML) workflows---by modifying the data pre-processing, model training, and postprocessing steps---via trial-and-error to achieve the desired model performance. Existing work on accelerating machine learning focuses on speeding up\n            <jats:italic>one-shot execution<\/jats:italic>\n            of workflows, failing to address the incremental and dynamic nature of typical ML development. We propose H\n            <jats:sc>elix<\/jats:sc>\n            , a declarative machine learning system that accelerates iterative development by optimizing workflow execution end-to-end and across iterations. H\n            <jats:sc>elix<\/jats:sc>\n            minimizes the runtime per iteration via program analysis and intelligent reuse of previous results, which are selectively materialized---trading off the cost of materialization for potential future benefits---to speed up future iterations. Additionally, H\n            <jats:sc>elix<\/jats:sc>\n            offers a graphical interface to visualize workflow DAGs and compare versions to facilitate iterative development. Through two ML applications, in classification and in structured prediction, attendees will experience the succinctness of H\n            <jats:sc>elix<\/jats:sc>\n            's programming interface and the speed and ease of iterative development using H\n            <jats:sc>elix<\/jats:sc>\n            . In our evaluations, H\n            <jats:sc>elix<\/jats:sc>\n            achieved up to an order of magnitude reduction in cumulative run time compared to state-of-the-art machine learning tools.\n          <\/jats:p>","DOI":"10.14778\/3229863.3236234","type":"journal-article","created":{"date-parts":[[2018,9,10]],"date-time":"2018-09-10T12:12:28Z","timestamp":1536581548000},"page":"1958-1961","source":"Crossref","is-referenced-by-count":13,"title":["H\n            <scp>elix<\/scp>"],"prefix":"10.14778","volume":"11","author":[{"given":"Doris","family":"Xin","sequence":"first","affiliation":[{"name":"University of Illinois (UIUC)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Litian","family":"Ma","sequence":"additional","affiliation":[{"name":"University of Illinois (UIUC)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jialin","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Illinois (UIUC)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen","family":"Macke","sequence":"additional","affiliation":[{"name":"University of Illinois (UIUC)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuchen","family":"Song","sequence":"additional","affiliation":[{"name":"University of Illinois (UIUC)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aditya","family":"Parameswaran","sequence":"additional","affiliation":[{"name":"University of Illinois (UIUC)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2742797"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2213836.2213874"},{"key":"e_1_2_1_3_1","volume-title":"Pearson Education","author":"Kleinberg J.","year":"2006","unstructured":"J. Kleinberg and E. Tardos . Algorithm design . Pearson Education , 2006 . J. Kleinberg and E. Tardos. Algorithm design. Pearson Education, 2006."},{"key":"e_1_2_1_4_1","volume-title":"CIDR","author":"Kraska T.","year":"2013","unstructured":"T. Kraska : A distributed machine-learning system . In CIDR , 2013 . T. Kraska et al. Mlbase: A distributed machine-learning system. In CIDR, 2013."},{"key":"e_1_2_1_5_1","volume-title":"UCI machine learning repository","author":"Lichman M.","year":"2013","unstructured":"M. Lichman . UCI machine learning repository , 2013 . M. Lichman. UCI machine learning repository, 2013."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-5010"},{"key":"e_1_2_1_7_1","volume-title":"JMLR","author":"Meng X.","year":"2016","unstructured":"X. Meng : Machine learning in apache spark . JMLR , 2016 . X. Meng et al. Mllib: Machine learning in apache spark. JMLR, 2016."},{"key":"e_1_2_1_8_1","volume-title":"JMLR","author":"Pedregosa F.","year":"2011","unstructured":"F. Pedregosa : Machine learning in python . JMLR , 2011 . F. Pedregosa et al. Scikit-learn: Machine learning in python. JMLR, 2011."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1090\/S0002-9947-1953-0053041-6"},{"key":"e_1_2_1_10_1","volume-title":"Tupaq: An efficient planner for large-scale predictive analytic queries. arXiv preprint arXiv:1502.00068","author":"Sparks E. R.","year":"2015","unstructured":"E. R. Sparks Tupaq: An efficient planner for large-scale predictive analytic queries. arXiv preprint arXiv:1502.00068 , 2015 . E. R. Sparks et al. Tupaq: An efficient planner for large-scale predictive analytic queries. arXiv preprint arXiv:1502.00068, 2015."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2017.109"},{"key":"e_1_2_1_13_1","volume-title":"NSDI","author":"Zaharia M.","year":"2012","unstructured":"M. Zaharia Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing . In NSDI , 2012 . M. Zaharia et al. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In NSDI, 2012."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2877204"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3229863.3236234","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:14:11Z","timestamp":1672222451000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3229863.3236234"}},"subtitle":["accelerating human-in-the-loop machine learning"],"short-title":[],"issued":{"date-parts":[[2018,8]]},"references-count":13,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2018,8]]}},"alternative-id":["10.14778\/3229863.3236234"],"URL":"https:\/\/doi.org\/10.14778\/3229863.3236234","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2018,8]]}}}