{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:28:31Z","timestamp":1778048911799,"version":"3.51.4"},"reference-count":83,"publisher":"Association for Computing Machinery (ACM)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:p>Serving deep learning models from relational databases brings significant benefits. First, features extracted from databases do not need to be transferred to any decoupled deep learning systems for inferences, and thus the system management overhead can be significantly reduced. Second, in a relational database, data management along the storage hierarchy is fully integrated with query processing, and thus it can continue model serving even if the working set size exceeds the available memory. Applying model deduplication can greatly reduce the storage space, memory footprint, cache misses, and inference latency. However, existing data deduplication techniques are not applicable to the deep learning model serving applications in relational databases. They do not consider the impacts on model inference accuracy as well as the inconsistency between tensor blocks and database pages. This work proposed synergistic storage optimization techniques for duplication detection, page packing, and caching, to enhance database systems for model serving. Evaluation results show that our proposed techniques significantly improved the storage efficiency and the model inference latency, and outperformed existing deep learning frameworks in targeting scenarios.<\/jats:p>","DOI":"10.14778\/3547305.3547325","type":"journal-article","created":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T16:09:53Z","timestamp":1662566993000},"page":"2230-2243","source":"Crossref","is-referenced-by-count":15,"title":["Serving deep learning models with deduplication from relational databases"],"prefix":"10.14778","volume":"15","author":[{"given":"Lixi","family":"Zhou","sequence":"first","affiliation":[{"name":"Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqing","family":"Chen","sequence":"additional","affiliation":[{"name":"Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amitabh","family":"Das","sequence":"additional","affiliation":[{"name":"Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Min","sequence":"additional","affiliation":[{"name":"IBM T. J. Watson Research Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Yu","sequence":"additional","affiliation":[{"name":"IBM T. J. Watson Research Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Zhao","sequence":"additional","affiliation":[{"name":"Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Zou","sequence":"additional","affiliation":[{"name":"Arizona State University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"[n.d.]. The Extreme Classification Repository: Multi-label Datasets & Code. http:\/\/manikvarma.org\/downloads\/XC\/XMLRepository.html.  [n.d.]. The Extreme Classification Repository: Multi-label Datasets & Code. http:\/\/manikvarma.org\/downloads\/XC\/XMLRepository.html."},{"key":"e_1_2_1_2_1","unstructured":"[n.d.]. NNLM128 Tensorflow Hub. \"https:\/\/tfhub.dev\/google\/nnlm-en-dim128\/2\".  [n.d.]. NNLM128 Tensorflow Hub. \"https:\/\/tfhub.dev\/google\/nnlm-en-dim128\/2\"."},{"key":"e_1_2_1_3_1","unstructured":"[n.d.]. NNLM50 Tensorflow Hub. \"https:\/\/tfhub.dev\/google\/nnlm-en-dim50\/2\".  [n.d.]. NNLM50 Tensorflow Hub. \"https:\/\/tfhub.dev\/google\/nnlm-en-dim50\/2\"."},{"key":"e_1_2_1_4_1","unstructured":"[n.d.]. shakespeare.txt. 'https:\/\/storage.googleapis.com\/download.tensorflow.org\/data\/shakespeare.txt'  [n.d.]. shakespeare.txt. 'https:\/\/storage.googleapis.com\/download.tensorflow.org\/data\/shakespeare.txt'"},{"key":"e_1_2_1_5_1","unstructured":"[n.d.]. Tensorflow Hub. \"https:\/\/www.tensorflow.org\/hub\".  [n.d.]. Tensorflow Hub. \"https:\/\/www.tensorflow.org\/hub\"."},{"key":"e_1_2_1_6_1","unstructured":"[n.d.]. TensorFlow Wikipedia Dataset. https:\/\/www.tensorflow.org\/datasets\/catalog\/wikipedia.  [n.d.]. TensorFlow Wikipedia Dataset. https:\/\/www.tensorflow.org\/datasets\/catalog\/wikipedia."},{"key":"e_1_2_1_7_1","unstructured":"[n.d.]. The total cost of ownership (tco) of amazon sagemaker. ([n.d.]). https:\/\/pages.awscloud.com\/NAMER-ln-GC-400-machine-learning-sagemaker-tco-learn-ty.html.  [n.d.]. The total cost of ownership (tco) of amazon sagemaker. ([n.d.]). https:\/\/pages.awscloud.com\/NAMER-ln-GC-400-machine-learning-sagemaker-tco-learn-ty.html."},{"key":"e_1_2_1_8_1","unstructured":"[n.d.]. Web Text Corpus. 'https:\/\/www.kaggle.com\/nltkdata\/web-text-corpus'  [n.d.]. Web Text Corpus. 'https:\/\/www.kaggle.com\/nltkdata\/web-text-corpus'"},{"key":"e_1_2_1_9_1","unstructured":"[n.d.]. Wiki250 Tensorflow Hub. \"https:\/\/tfhub.dev\/google\/Wiki-words-250\/2\".  [n.d.]. Wiki250 Tensorflow Hub. \"https:\/\/tfhub.dev\/google\/Wiki-words-250\/2\"."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-155860869-6\/50058-5"},{"key":"e_1_2_1_11_1","volume-title":"A neural probabilistic language model. Advances in Neural Information Processing Systems 13","author":"Bengio Yoshua","year":"2000","unstructured":"Yoshua Bengio , R\u00e9jean Ducharme , and Pascal Vincent . 2000. A neural probabilistic language model. Advances in Neural Information Processing Systems 13 ( 2000 ). Yoshua Bengio, R\u00e9jean Ducharme, and Pascal Vincent. 2000. A neural probabilistic language model. Advances in Neural Information Processing Systems 13 (2000)."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/MASCOT.2009.5366623"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2006.13"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/3007263.3007279"},{"key":"e_1_2_1_15_1","unstructured":"Daniel Borkan Lucas Dixon Jeffrey Sorensen Nithum Thain and Lucy Vasserman. 2019. Civil Comments Dataset. https:\/\/www.kaggle.com\/c\/jigsaw-unintended-bias-in-toxicity-classification\/data  Daniel Borkan Lucas Dixon Jeffrey Sorensen Nithum Thain and Lucy Vasserman. 2019. Civil Comments Dataset. https:\/\/www.kaggle.com\/c\/jigsaw-unintended-bias-in-toxicity-classification\/data"},{"key":"e_1_2_1_16_1","volume-title":"Scalable Blocking for Very Large Databases. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 303--319","author":"Borthwick Andrew","year":"2020","unstructured":"Andrew Borthwick , Stephen Ash , Bin Pang , Shehzad Qureshi , and Timothy Jones . 2020 . Scalable Blocking for Very Large Databases. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 303--319 . Andrew Borthwick, Stephen Ash, Bin Pang, Shehzad Qureshi, and Timothy Jones. 2020. Scalable Blocking for Very Large Databases. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 303--319."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/829502.830043"},{"key":"e_1_2_1_18_1","volume-title":"Large-scale multi-label text classification on EU legislation. arXiv preprint arXiv:1906.02192","author":"Chalkidis Ilias","year":"2019","unstructured":"Ilias Chalkidis , Manos Fergadiotis , Prodromos Malakasiotis , and Ion Androutsopoulos . 2019. Large-scale multi-label text classification on EU legislation. arXiv preprint arXiv:1906.02192 ( 2019 ). Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, and Ion Androutsopoulos. 2019. Large-scale multi-label text classification on EU legislation. arXiv preprint arXiv:1906.02192 (2019)."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/509907.509965"},{"key":"e_1_2_1_20_1","unstructured":"Lin Chen Hossein Esfandiari Gang Fu and Vahab Mirrokni. 2019. Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond. In Advances in Neural Information Processing Systems. 10044--10054.  Lin Chen Hossein Esfandiari Gang Fu and Vahab Mirrokni. 2019. Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond. In Advances in Neural Information Processing Systems. 10044--10054."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01840450"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.14778\/2983200.2983203"},{"key":"e_1_2_1_23_1","volume-title":"NIPS 2015 Workshop on Machine Learning Systems (LearningSys).","author":"Crankshaw Daniel","year":"2015","unstructured":"Daniel Crankshaw , Xin Wang , Joseph E Gonzalez , and Michael J Franklin . 2015 . Scalable training and serving of personalized models . In NIPS 2015 Workshop on Machine Learning Systems (LearningSys). Daniel Crankshaw, Xin Wang, Joseph E Gonzalez, and Michael J Franklin. 2015. Scalable training and serving of personalized models. In NIPS 2015 Workshop on Machine Learning Systems (LearningSys)."},{"key":"e_1_2_1_24_1","volume-title":"Clipper: A low-latency online prediction serving system. In 14th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 17). 613--627.","author":"Crankshaw Daniel","year":"2017","unstructured":"Daniel Crankshaw , Xin Wang , Guilio Zhou , Michael J Franklin , Joseph E Gonzalez , and Ion Stoica . 2017 . Clipper: A low-latency online prediction serving system. In 14th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 17). 613--627. Daniel Crankshaw, Xin Wang, Guilio Zhou, Michael J Franklin, Joseph E Gonzalez, and Ion Stoica. 2017. Clipper: A low-latency online prediction serving system. In 14th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 17). 613--627."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/997817.997857"},{"key":"e_1_2_1_26_1","unstructured":"Biplob K Debnath Sudipta Sengupta and Jin Li. 2010. ChunkStash: Speeding Up Inline Storage Deduplication Using Flash Memory. In USENIX annual technical conference. 1--16.  Biplob K Debnath Sudipta Sengupta and Jin Li. 2010. ChunkStash: Speeding Up Inline Storage Deduplication Using Flash Memory. In USENIX annual technical conference. 1--16."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389747"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2007.250581"},{"key":"e_1_2_1_29_1","volume-title":"Computers and intractability","author":"Garey Michael R","unstructured":"Michael R Garey and David S Johnson . 1979. Computers and intractability . Vol. 174 . freeman San Francisco . Michael R Garey and David S Johnson. 1979. Computers and intractability. Vol. 174. freeman San Francisco."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/355616.361019"},{"key":"e_1_2_1_31_1","volume-title":"word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722","author":"Goldberg Yoav","year":"2014","unstructured":"Yoav Goldberg and Omer Levy . 2014. word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722 ( 2014 ). Yoav Goldberg and Omer Levy. 2014. word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method. arXiv preprint arXiv:1402.3722 (2014)."},{"key":"e_1_2_1_32_1","volume-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149","author":"Han Song","year":"2015","unstructured":"Song Han , Huizi Mao , and William J Dally . 2015. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 ( 2015 ). Song Han, Huizi Mao, and William J Dally. 2015. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)."},{"key":"e_1_2_1_33_1","volume-title":"Learning both weights and connections for efficient neural networks. arXiv preprint arXiv:1506.02626","author":"Han Song","year":"2015","unstructured":"Song Han , Jeff Pool , John Tran , and William J Dally . 2015. Learning both weights and connections for efficient neural networks. arXiv preprint arXiv:1506.02626 ( 2015 ). Song Han, Jeff Pool, John Tran, and William J Dally. 2015. Learning both weights and connections for efficient neural networks. arXiv preprint arXiv:1506.02626 (2015)."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/568271.223807"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1002\/net.3230070308"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3070607.3070608"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/276698.276876"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.14778\/3317315.3317323"},{"key":"e_1_2_1_40_1","unstructured":"Konstantinos Karanasos Matteo Interlandi Doris Xin Fotis Psallidas Rathijit Sen Kwanghyun Park Ivan Popivanov Supun Nakandal Subru Krishnan Markus Weimer etal 2019. Extending relational query processing with ML inference. arXiv preprint arXiv:1911.00231 (2019).  Konstantinos Karanasos Matteo Interlandi Doris Xin Fotis Psallidas Rathijit Sen Kwanghyun Park Ivan Popivanov Supun Nakandal Subru Krishnan Markus Weimer et al. 2019. Extending relational query processing with ML inference. arXiv preprint arXiv:1911.00231 (2019)."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.14778\/2367502.2367527"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2012.22"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.14778\/3467861.3467869"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386901.3388947"},{"key":"e_1_2_1_45_1","volume-title":"Markus Weimer, and Matteo Interlandi.","author":"Lee Yunseong","year":"2018","unstructured":"Yunseong Lee , Alberto Scolari , Byung-Gon Chun , Marco Domenico Santambrogio , Markus Weimer, and Matteo Interlandi. 2018 . {PRETZEL}: Opening the Black Box of Machine Learning Prediction Serving Systems. In 13th {USENIX} Symposium on Operating Systems Design and Implementation ( {OSDI} 18). 611--626. Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Markus Weimer, and Matteo Interlandi. 2018. {PRETZEL}: Opening the Black Box of Machine Learning Prediction Serving Systems. In 13th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 18). 611--626."},{"key":"e_1_2_1_46_1","first-page":"361","article-title":"Rcv1: A new benchmark collection for text categorization research","author":"Lewis David D","year":"2004","unstructured":"David D Lewis , Yiming Yang , Tony Russell-Rose , and Fan Li . 2004 . Rcv1: A new benchmark collection for text categorization research . Journal of machine learning research 5 , Apr (2004), 361 -- 397 . David D Lewis, Yiming Yang, Tony Russell-Rose, and Fan Li. 2004. Rcv1: A new benchmark collection for text categorization research. Journal of machine learning research 5, Apr (2004), 361--397.","journal-title":"Journal of machine learning research 5"},{"key":"e_1_2_1_47_1","volume-title":"Proceedings of 2nd Asian conference on machine learning. JMLR Workshop and Conference Proceedings, 241--252","author":"Li Peipei","year":"2010","unstructured":"Peipei Li , Xindong Wu , and Xuegang Hu . 2010 . Mining recurring concept drifts with limited labeled streaming data . In Proceedings of 2nd Asian conference on machine learning. JMLR Workshop and Conference Proceedings, 241--252 . Peipei Li, Xindong Wu, and Xuegang Hu. 2010. Mining recurring concept drifts with limited labeled streaming data. In Proceedings of 2nd Asian conference on machine learning. JMLR Workshop and Conference Proceedings, 241--252."},{"key":"e_1_2_1_48_1","volume-title":"14th {USENIX} Conference on File and Storage Technologies ({FAST} 16). 301--314.","author":"Li Wenji","unstructured":"Wenji Li , Gregory Jean-Baptise , Juan Riveros , Giri Narasimhan , Tony Zhang , and Ming Zhao . 2016. CacheDedup: In-line deduplication for flash caching . In 14th {USENIX} Conference on File and Storage Technologies ({FAST} 16). 301--314. Wenji Li, Gregory Jean-Baptise, Juan Riveros, Giri Narasimhan, Tony Zhang, and Ming Zhao. 2016. CacheDedup: In-line deduplication for flash caching. In 14th {USENIX} Conference on File and Storage Technologies ({FAST} 16). 301--314."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/18.61115"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536354.2536355"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2827988"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmp.2017.05.006"},{"key":"e_1_2_1_53_1","unstructured":"Andrew Maas Raymond E Daly Peter T Pham Dan Huang Andrew Y Ng and Christopher Potts. 2011. Large Movie Review Dataset. http:\/\/ai.stanford.edu\/~amaas\/data\/sentiment\/  Andrew Maas Raymond E Daly Peter T Pham Dan Huang Andrew Y Ng and Christopher Potts. 2011. Large Movie Review Dataset. http:\/\/ai.stanford.edu\/~amaas\/data\/sentiment\/"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/2507157.2507163"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783381"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946679"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/2078861.2078864"},{"key":"e_1_2_1_59_1","volume-title":"Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov , Kai Chen , Greg Corrado , and Jeffrey Dean . 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 ( 2013 ). Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)."},{"key":"e_1_2_1_60_1","unstructured":"Simon Mo Edward Oakes and Michael Galarnyk. [n.d.]. Serving ML Models in Production: Common Patterns. ([n.d.]).  Simon Mo Edward Oakes and Michael Galarnyk. [n.d.]. Serving ML Models in Production: Common Patterns. ([n.d.])."},{"key":"e_1_2_1_61_1","volume-title":"Markus Weimer, and Matteo Interlandi.","author":"Nakandala Supun","year":"2020","unstructured":"Supun Nakandala , Karla Saur , Gyeong-In Yu , Konstantinos Karanasos , Carlo Curino , Markus Weimer, and Matteo Interlandi. 2020 . A Tensor Compiler for Unified Machine Learning Prediction Serving. In 14th {USENIX} Symposium on Operating Systems Design and Implementation ( {OSDI} 20). 899--917. Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, and Matteo Interlandi. 2020. A Tensor Compiler for Unified Machine Learning Prediction Serving. In 14th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 20). 899--917."},{"key":"e_1_2_1_62_1","volume-title":"Tensorflow-serving: Flexible, high-performance ml serving. arXiv preprint arXiv:1712.06139","author":"Olston Christopher","year":"2017","unstructured":"Christopher Olston , Noah Fiedel , Kiril Gorovoy , Jeremiah Harmsen , Li Lao , Fangwei Li , Vinu Rajashekhar , Sukriti Ramesh , and Jordan Soyke . 2017 . Tensorflow-serving: Flexible, high-performance ml serving. arXiv preprint arXiv:1712.06139 (2017). Christopher Olston, Noah Fiedel, Kiril Gorovoy, Jeremiah Harmsen, Li Lao, Fangwei Li, Vinu Rajashekhar, Sukriti Ramesh, and Jordan Soyke. 2017. Tensorflow-serving: Flexible, high-performance ml serving. arXiv preprint arXiv:1712.06139 (2017)."},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415572"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341301.3359658"},{"key":"e_1_2_1_65_1","unstructured":"Larry J Stockmeyer. 1975. The set basis problem is NP-complete. IBM Thomas J. Watson Research Division Research reports.  Larry J Stockmeyer. 1975. The set basis problem is NP-complete. IBM Thomas J. Watson Research Division Research reports."},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.5555\/2032397.2032399"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/1266840.1266870"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196934"},{"key":"e_1_2_1_69_1","volume-title":"2020 USENIX Annual Technical Conference (USENIX ATC 20)","author":"Wang Qiuping","year":"2020","unstructured":"Qiuping Wang , Jinhong Li , Wen Xia , Erik Kruus , Biplob Debnath , and Patrick PC Lee . 2020 . Austere flash caching with deduplication and compression . In 2020 USENIX Annual Technical Conference (USENIX ATC 20) . 713--726. Qiuping Wang, Jinhong Li, Wen Xia, Erik Kruus, Biplob Debnath, and Patrick PC Lee. 2020. Austere flash caching with deduplication and compression. In 2020 USENIX Annual Technical Conference (USENIX ATC 20). 713--726."},{"key":"e_1_2_1_70_1","volume-title":"Teck Khim Ng, and Beng Chin Ooi","author":"Wang Wei","year":"2018","unstructured":"Wei Wang , Sheng Wang , Jinyang Gao , Meihui Zhang , Gang Chen , Teck Khim Ng, and Beng Chin Ooi . 2018 . Rafiki : machine learning as an analytics service system. arXiv preprint arXiv:1804.06087 (2018). Wei Wang, Sheng Wang, Jinyang Gao, Meihui Zhang, Gang Chen, Teck Khim Ng, and Beng Chin Ooi. 2018. Rafiki: machine learning as an analytics service system. arXiv preprint arXiv:1804.06087 (2018)."},{"key":"e_1_2_1_71_1","volume-title":"SPORES: sum-product optimization via relational equality saturation for large scale linear algebra. arXiv preprint arXiv:2002.07951","author":"Wang Yisu Remy","year":"2020","unstructured":"Yisu Remy Wang , Shana Hutchison , Jonathan Leang , Bill Howe , and Dan Suciu . 2020. SPORES: sum-product optimization via relational equality saturation for large scale linear algebra. arXiv preprint arXiv:2002.07951 ( 2020 ). Yisu Remy Wang, Shana Hutchison, Jonathan Leang, Bill Howe, and Dan Suciu. 2020. SPORES: sum-product optimization via relational equality saturation for large scale linear algebra. arXiv preprint arXiv:2002.07951 (2020)."},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.14778\/1453856.1453957"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2638838"},{"key":"e_1_2_1_74_1","volume-title":"Tensor Relational Algebra for Machine Learning System Design. arXiv preprint arXiv:2009.00524","author":"Yuan Binhang","year":"2020","unstructured":"Binhang Yuan , Dimitrije Jankov , Jia Zou , Yuxin Tang , Daniel Bourgeois , and Chris Jermaine . 2020. Tensor Relational Algebra for Machine Learning System Design. arXiv preprint arXiv:2009.00524 ( 2020 ). Binhang Yuan, Dimitrije Jankov, Jia Zou, Yuxin Tang, Daniel Bourgeois, and Chris Jermaine. 2020. Tensor Relational Algebra for Machine Learning System Design. arXiv preprint arXiv:2009.00524 (2020)."},{"key":"e_1_2_1_75_1","unstructured":"Matei Zaharia Mosharaf Chowdhury Michael J Franklin Scott Shenker and Ion Stoica. 2010. Spark: cluster computing with working sets. In USENIX HotCloud. 1--10.  Matei Zaharia Mosharaf Chowdhury Michael J Franklin Scott Shenker and Ion Stoica. 2010. Spark: cluster computing with working sets. In USENIX HotCloud. 1--10."},{"key":"e_1_2_1_76_1","unstructured":"Xiang Zhang Junbo Zhao and Yann LeCun. 2015. Yelp polarity Reviews Dataset. http:\/\/goo.gl\/JyCnZq  Xiang Zhang Junbo Zhao and Yann LeCun. 2015. Yelp polarity Reviews Dataset. http:\/\/goo.gl\/JyCnZq"},{"key":"e_1_2_1_77_1","volume-title":"It's the Best Only When It Fits You Most: Finding Related Models for Serving Based on Dynamic Locality Sensitive Hashing. arXiv preprint arXiv:2010.09474","author":"Zhou Lixi","year":"2020","unstructured":"Lixi Zhou , Zijie Wang , Amitabh Das , and Jia Zou . 2020. It's the Best Only When It Fits You Most: Finding Related Models for Serving Based on Dynamic Locality Sensitive Hashing. arXiv preprint arXiv:2010.09474 ( 2020 ). Lixi Zhou, Zijie Wang, Amitabh Das, and Jia Zou. 2020. It's the Best Only When It Fits You Most: Finding Related Models for Serving Based on Dynamic Locality Sensitive Hashing. arXiv preprint arXiv:2010.09474 (2020)."},{"key":"e_1_2_1_78_1","first-page":"269","article-title":"Avoiding the disk bottleneck in the data domain deduplication file system","volume":"8","author":"Zhu Benjamin","year":"2008","unstructured":"Benjamin Zhu , Kai Li , and R Hugo Patterson . 2008 . Avoiding the disk bottleneck in the data domain deduplication file system . In Fast , Vol. 8. 269 -- 282 . Benjamin Zhu, Kai Li, and R Hugo Patterson. 2008. Avoiding the disk bottleneck in the data domain deduplication file system. In Fast, Vol. 8. 269--282.","journal-title":"Fast"},{"key":"e_1_2_1_79_1","volume-title":"LSH ensemble: Internet-scale domain search. arXiv preprint arXiv:1603.07410","author":"Zhu Erkang","year":"2016","unstructured":"Erkang Zhu , Fatemeh Nargesian , Ken Q Pu , and Ren\u00e9e J Miller . 2016. LSH ensemble: Internet-scale domain search. arXiv preprint arXiv:1603.07410 ( 2016 ). Erkang Zhu, Fatemeh Nargesian, Ken Q Pu, and Ren\u00e9e J Miller. 2016. LSH ensemble: Internet-scale domain search. arXiv preprint arXiv:1603.07410 (2016)."},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196933"},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.14778\/3457390.3457392"},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.14778\/3311880.3311885"},{"key":"e_1_2_1_83_1","volume-title":"Architecture of a distributed storage that combines file system, memory and computation in a single layer. The VLDB Journal","author":"Zou Jia","year":"2020","unstructured":"Jia Zou , Arun Iyengar , and Chris Jermaine . 2020. Architecture of a distributed storage that combines file system, memory and computation in a single layer. The VLDB Journal ( 2020 ), 1--25. Jia Zou, Arun Iyengar, and Chris Jermaine. 2020. Architecture of a distributed storage that combines file system, memory and computation in a single layer. The VLDB Journal (2020), 1--25."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3547305.3547325","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T11:18:05Z","timestamp":1672226285000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3547305.3547325"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6]]},"references-count":83,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["10.14778\/3547305.3547325"],"URL":"https:\/\/doi.org\/10.14778\/3547305.3547325","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,6]]}}}