{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T17:10:34Z","timestamp":1784913034952,"version":"3.55.0"},"reference-count":12,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:p>\n            Existing automated machine learning solutions and intelligent discovery assistants are popular tools that facilitate the end-user with the design of data science (DS) pipelines. However, they yield limited applicability for a wide range of real-world use cases and application domains due to (a) the limited support of DS tasks; (b) a small, static set of available operators; and (c) restriction to evaluation processes with quantifiable loss functions. We demonstrate DORIAN, a human-in-the-loop approach for the\n            <jats:italic>assisted design of data science pipelines<\/jats:italic>\n            that supports a large and growing set of DS tasks, operators, and arbitrary user-defined evaluation processes. Based on the user query, i.e., a dataset and a DS task, DORIAN computes a ranked list of candidate pipelines that the end-user can choose from, alter, execute and evaluate. It stores executed pipelines in an experiment database and utilizes similarity-based search to identify relevant previously-run pipelines from the experiment database. DORIAN also takes user interaction into account to improve suggestions over time. We show how users can interact with DORIAN to create and compare DS pipelines on various real-world DS tasks without the need for writing any code.\n          <\/jats:p>","DOI":"10.14778\/3554821.3554882","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T22:28:39Z","timestamp":1664490519000},"page":"3714-3717","source":"Crossref","is-referenced-by-count":10,"title":["DORIAN in action"],"prefix":"10.14778","volume":"15","author":[{"given":"Sergey","family":"Redyuk","sequence":"first","affiliation":[{"name":"TU Berlin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zoi","family":"Kaoudi","sequence":"additional","affiliation":[{"name":"TU Berlin"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Schelter","sequence":"additional","affiliation":[{"name":"University of Amsterdam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Volker","family":"Markl","sequence":"additional","affiliation":[{"name":"TU Berlin"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csi.2017.05.004"},{"key":"e_1_2_1_2_1","volume-title":"OpenML Benchmarking Suites. In 35th Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).","unstructured":"Bernd Bischl et al. 2021 . OpenML Benchmarking Suites. In 35th Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). Bernd Bischl et al. 2021. OpenML Benchmarking Suites. In 35th Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)."},{"key":"e_1_2_1_3_1","volume-title":"AutoML Workshop at ICML.","author":"Drori I.","year":"2018","unstructured":"I. Drori 2018 . AlphaD3M: Machine learning pipeline synthesis . In AutoML Workshop at ICML. I. Drori et al. 2018. AlphaD3M: Machine learning pipeline synthesis. In AutoML Workshop at ICML."},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"M. Feurer etal 2019. Auto-sklearn: efficient and robust automated machine learning. In Automated Machine Learning. Springer Cham 113--134.  M. Feurer et al. 2019. Auto-sklearn: efficient and robust automated machine learning. In Automated Machine Learning. Springer Cham 113--134.","DOI":"10.1007\/978-3-030-05318-5_6"},{"key":"e_1_2_1_5_1","volume-title":"ICML'18 AutoML Workshop.","author":"Gil Y.","year":"2018","unstructured":"Y. Gil 2018 . P4ML: A phased performance-based pipeline planner for automated machine learning . In ICML'18 AutoML Workshop. Y. Gil et al. 2018. P4ML: A phased performance-based pipeline planner for automated machine learning. In ICML'18 AutoML Workshop."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106622"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1476589.1476628"},{"key":"e_1_2_1_8_1","first-page":"1","article-title":"Using Meta-Mining to Support Data Mining Workflow Planning and Optimization","volume":"51","author":"Nguyen P.","year":"2014","unstructured":"P. Nguyen , M. Hilario , and A. Kalousis . 2014 . Using Meta-Mining to Support Data Mining Workflow Planning and Optimization . J. Artif. Int. Res. 51 , 1 (Sept. 2014), 605--644. P. Nguyen, M. Hilario, and A. Kalousis. 2014. Using Meta-Mining to Support Data Mining Workflow Planning and Optimization. J. Artif. Int. Res. 51, 1 (Sept. 2014), 605--644.","journal-title":"J. Artif. Int. Res."},{"key":"e_1_2_1_9_1","volume-title":"ICML'16 AutoML Workshop. JMLR, 66--74","author":"Olson R.S.","unstructured":"R.S. Olson and J.H. Moore . 2016. TPOT: A tree-based pipeline optimization tool for automating machine learning . In ICML'16 AutoML Workshop. JMLR, 66--74 . R.S. Olson and J.H. Moore. 2016. TPOT: A tree-based pipeline optimization tool for automating machine learning. In ICML'16 AutoML Workshop. JMLR, 66--74."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3319863"},{"key":"e_1_2_1_11_1","volume-title":"ICML'18 AutoML Workshop.","author":"Wever M.D.","unstructured":"M.D. Wever , F. Mohr , and E. H\u00fcllermeier . 2018. Ml-plan for unlimited-length machine learning pipelines . In ICML'18 AutoML Workshop. M.D. Wever, F. Mohr, and E. H\u00fcllermeier. 2018. Ml-plan for unlimited-length machine learning pipelines. In ICML'18 AutoML Workshop."},{"key":"e_1_2_1_12_1","unstructured":"Q. Yao etal 2018. Taking human out of learning applications: A survey on automated machine learning. arXiv preprint arXiv:1810.13306 (2018).  Q. Yao et al. 2018. Taking human out of learning applications: A survey on automated machine learning. arXiv preprint arXiv:1810.13306 (2018)."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3554821.3554882","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:35:42Z","timestamp":1672227342000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3554821.3554882"}},"subtitle":["assisted design of data science pipelines"],"short-title":[],"issued":{"date-parts":[[2022,8]]},"references-count":12,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10.14778\/3554821.3554882"],"URL":"https:\/\/doi.org\/10.14778\/3554821.3554882","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,8]]}}}