{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T17:11:19Z","timestamp":1784913079492,"version":"3.55.0"},"reference-count":11,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2020,8]]},"abstract":"<jats:p>\n            Data pipelines are the new code. Consequently, data scientists need new tools to support the often time-consuming process of debugging their pipelines. We introduce\n            <jats:italic toggle=\"yes\">Dagger<\/jats:italic>\n            , an end-to-end system to debug and mitigate data-centric errors in data pipelines, such as a data transformation gone wrong or a classifier underperforming due to noisy training data.\n            <jats:italic toggle=\"yes\">Dagger<\/jats:italic>\n            supports inter-module debugging, where the pipeline blocks are treated as black boxes, as well as intra-module debugging, where users can debug data objects in Python scripts (e.g., DataFrames). In this demo, we will walk the audience through a rich, real-world business intelligence use case from our industrial collaborators at Intel, to highlight how\n            <jats:italic toggle=\"yes\">Dagger<\/jats:italic>\n            enables data scientists to productively identify and mitigate data-centric problems at different stages of pipeline development.\n          <\/jats:p>","DOI":"10.14778\/3415478.3415527","type":"journal-article","created":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T18:46:35Z","timestamp":1600109195000},"page":"2993-2996","source":"Crossref","is-referenced-by-count":11,"title":["Debugging large-scale data science pipelines using dagger"],"prefix":"10.14778","volume":"13","author":[{"given":"El Kindi","family":"Rezig","sequence":"first","affiliation":[{"name":"MIT CSAIL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashrita","family":"Brahmaroutu","sequence":"additional","affiliation":[{"name":"Intel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nesime","family":"Tatbul","sequence":"additional","affiliation":[{"name":"Intel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mourad","family":"Ouzzani","sequence":"additional","affiliation":[{"name":"HBKU"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Tang","sequence":"additional","affiliation":[{"name":"HBKU"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy","family":"Mattson","sequence":"additional","affiliation":[{"name":"Intel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Madden","sequence":"additional","affiliation":[{"name":"MIT CSAIL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Stonebraker","sequence":"additional","affiliation":[{"name":"MIT CSAIL"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"https:\/\/www.gnu.org\/software\/gdb\/","author":"Debugger GNU","year":"2020","unstructured":"GNU Debugger, https:\/\/www.gnu.org\/software\/gdb\/. Accessed: March 2020."},{"key":"e_1_2_1_2_1","volume-title":"https:\/\/jupyter.org\/","author":"Notebooks Jupyter","year":"2020","unstructured":"Jupyter Notebooks, https:\/\/jupyter.org\/. Accessed: March 2020."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/2994509.2994518"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3320246"},{"key":"e_1_2_1_5_1","volume-title":"CIDR","author":"Deng D.","year":"2017","unstructured":"D. Deng, R. C. Fernandez, Z. Abedjan, S. Wang, M. Stonebraker, A. K. Elmagarmid, I. F. Ilyas, S. Madden, M. Ouzzani, and N. Tang. The Data Civilizer System. In CIDR, 2017."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/3229863.3240493"},{"key":"e_1_2_1_7_1","volume-title":"CIDR","author":"Rezig E. K.","year":"2020","unstructured":"E. K. Rezig, L. Cao, G. Simonini, M. Schoemans, S. Madden, N. Tang, M. Ouzzani, and M. Stonebraker. Dagger: A Data (not code) Debugger. In CIDR, 2020."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352108"},{"key":"e_1_2_1_9_1","first-page":"1","volume-title":"Provenance-Enabled Data Exploration and Visualization with VisTrails. In SIBGRAPI Conference on Graphics, Patterns and Images","author":"Silva C. T.","year":"2010","unstructured":"C. T. Silva, J. Freire, E. Santos, and E. W. Anderson. Provenance-Enabled Data Exploration and Visualization with VisTrails. In SIBGRAPI Conference on Graphics, Patterns and Images, pages 1--9, 2010."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196934"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.14778\/3229863.3236234"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3415478.3415527","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T02:20:38Z","timestamp":1758075638000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3415478.3415527"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8]]},"references-count":11,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2020,8]]}},"alternative-id":["10.14778\/3415478.3415527"],"URL":"https:\/\/doi.org\/10.14778\/3415478.3415527","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2020,8]]}}}