{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T01:03:09Z","timestamp":1789434189966,"version":"build-2803163510"},"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":[[2019,8]]},"abstract":"<jats:p>\n            Machine learning has enabled many interesting applications and is extensively being used in big data systems. The popular approach - training machine learning models in frameworks like Tensorflow, Pytorch and Keras - requires movement of data from database engines to analytical engines, which adds an excessive overhead on data scientists and becomes a performance bottleneck for model training. In this demonstration, we give a practical exhibition of a solution for the enablement of distributed machine learning natively inside database engines. During the demo, the audience will interactively use Python APIs in Jupyter Notebooks to train\n            <jats:italic>multiple linear regression<\/jats:italic>\n            models on synthetic regression datasets and\n            <jats:italic>neural network models<\/jats:italic>\n            on vision and sensory datasets directly inside Teradata SQL Engine.\n          <\/jats:p>","DOI":"10.14778\/3352063.3352083","type":"journal-article","created":{"date-parts":[[2019,9,18]],"date-time":"2019-09-18T18:36:11Z","timestamp":1568831771000},"page":"1854-1857","source":"Crossref","is-referenced-by-count":13,"title":["In-database distributed machine learning"],"prefix":"10.14778","volume":"12","author":[{"given":"Sandeep Singh","family":"Sandha","sequence":"first","affiliation":[{"name":"UCLA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wellington","family":"Cabrera","sequence":"additional","affiliation":[{"name":"Teradata Labs"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Al-Kateb","sequence":"additional","affiliation":[{"name":"Teradata Labs"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjay","family":"Nair","sequence":"additional","affiliation":[{"name":"Teradata Labs"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mani","family":"Srivastava","sequence":"additional","affiliation":[{"name":"UCLA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Google BigQuery ML. https:\/\/cloud.google.com\/bigquery\/docs\/bigquery. Accessed: 2019-03-14.  Google BigQuery ML. https:\/\/cloud.google.com\/bigquery\/docs\/bigquery. Accessed: 2019-03-14."},{"key":"e_1_2_1_2_1","unstructured":"Neural Networks in Microsoft SQL Server. https:\/\/docs.microsoft.com\/en-us\/sql\/analysis-services\/data-mining\/microsoft-neural-network-algorithm. Accessed: 2019-03-14.  Neural Networks in Microsoft SQL Server. https:\/\/docs.microsoft.com\/en-us\/sql\/analysis-services\/data-mining\/microsoft-neural-network-algorithm. Accessed: 2019-03-14."},{"key":"e_1_2_1_3_1","unstructured":"Neural Networks in Oracle. https:\/\/docs.oracle.com\/en\/database\/oracle\/oracle-database\/18\/dmcon\/neural-network.html. Accessed: 2019-03-14.  Neural Networks in Oracle. https:\/\/docs.oracle.com\/en\/database\/oracle\/oracle-database\/18\/dmcon\/neural-network.html. Accessed: 2019-03-14."},{"issue":"2","key":"e_1_2_1_4_1","first-page":"3","article-title":"Born To Be Parallel: Why Parallel Origins Give Teradata an Enduring Performance Edge","volume":"20","author":"Ballinger C.","year":"1997","journal-title":"IEEE Data Eng. Bull."},{"key":"e_1_2_1_5_1","volume-title":"VLDB","author":"Catozzi J.","year":"2001"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2858933"},{"key":"e_1_2_1_7_1","unstructured":"G. Hinton N. Srivastava and K. Swersky. Neural Networks for Machine Learning Lecture 6a Overview of Mini-batch Gradient Descent. Coursera Lecture slides 2012.  G. Hinton N. Srivastava and K. Swersky. Neural Networks for Machine Learning Lecture 6a Overview of Mini-batch Gradient Descent. Coursera Lecture slides 2012."},{"key":"e_1_2_1_8_1","unstructured":"J. Kone\u010dn\u1ef3 H. B. McMahan F. X. Yu P. Richt\u00e1rik A. T. Suresh and D. Bacon. Federated Learning: Strategies for Improving Communication Efficiency. arXiv preprint arXiv:1610.05492 2016.  J. Kone\u010dn\u1ef3 H. B. McMahan F. X. Yu P. Richt\u00e1rik A. T. Suresh and D. Bacon. Federated Learning: Strategies for Improving Communication Efficiency. arXiv preprint arXiv:1610.05492 2016."},{"key":"e_1_2_1_9_1","unstructured":"Y. LeCun. The MNIST database of handwritten digits. http:\/\/yann.lecun.com\/exdb\/mnist\/ 1998.  Y. LeCun. The MNIST database of handwritten digits. http:\/\/yann.lecun.com\/exdb\/mnist\/ 1998."},{"issue":"1","key":"e_1_2_1_10_1","first-page":"4","article-title":"The Case for Shared Nothing","volume":"9","author":"Stonebraker Michael","year":"1986","journal-title":"IEEE Database Eng. Bull."},{"key":"e_1_2_1_11_1","volume-title":"USENIX Annual Technical Conference","author":"Qiao A.","year":"2018"},{"key":"e_1_2_1_12_1","unstructured":"H. Xiao K. Rasul and R. Vollgraf. Fashion-mnist: A Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv preprint arXiv:1708.07747 2017.  H. Xiao K. Rasul and R. Vollgraf. Fashion-mnist: A Novel Image Dataset for Benchmarking Machine Learning Algorithms. arXiv preprint arXiv:1708.07747 2017."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3352063.3352083","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:40:45Z","timestamp":1672224045000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3352063.3352083"}},"subtitle":["demonstration using Teradata SQL engine"],"short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":12,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["10.14778\/3352063.3352083"],"URL":"https:\/\/doi.org\/10.14778\/3352063.3352083","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2019,8]]}}}