{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T01:34:42Z","timestamp":1773192882207,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":56,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T00:00:00Z","timestamp":1686960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,6,17]]},"DOI":"10.1145\/3579371.3589093","type":"proceedings-article","created":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T20:25:28Z","timestamp":1686947128000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["ECSSD: Hardware\/Data Layout Co-Designed In-Storage-Computing Architecture for Extreme Classification"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4671-9808","authenticated-orcid":false,"given":"Siqi","family":"Li","sequence":"first","affiliation":[{"name":"University of California, Santa Barbara, Santa Barbara, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2228-8829","authenticated-orcid":false,"given":"Fengbin","family":"Tu","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0792-8146","authenticated-orcid":false,"given":"Liu","family":"Liu","sequence":"additional","affiliation":[{"name":"Rensselaer Polytechnic Institute, Troy, New York, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1969-6728","authenticated-orcid":false,"given":"Jilan","family":"Lin","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara, Santa Barbara, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8575-9432","authenticated-orcid":false,"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara, Santa Barbara, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9536-1894","authenticated-orcid":false,"given":"Yangwook","family":"Kang","sequence":"additional","affiliation":[{"name":"Samsung Semiconductor Inc., San Jose, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8716-5793","authenticated-orcid":false,"given":"Yufei","family":"Ding","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara, Santa Barbara, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2093-1788","authenticated-orcid":false,"given":"Yuan","family":"Xie","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Sunnyvale, California, USA"}]}],"member":"320","published-online":{"date-parts":[[2023,6,17]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Amazon. 2022. Price of typical 512GB DDR4 DRAM memory. https:\/\/www.amazon.com\/512GB-8x64GB-288-Pin-Reduced-Memory\/dp\/B07X22FZJJ  Amazon. 2022. Price of typical 512GB DDR4 DRAM memory. https:\/\/www.amazon.com\/512GB-8x64GB-288-Pin-Reduced-Memory\/dp\/B07X22FZJJ"},{"key":"e_1_3_2_1_2_1","unstructured":"ARM. 2011. ARM R5 Processor. https:\/\/developer.arm.com\/Processors\/Cortex-R5  ARM. 2011. ARM R5 Processor. https:\/\/developer.arm.com\/Processors\/Cortex-R5"},{"key":"e_1_3_2_1_3_1","unstructured":"ARM. 2022. ARM SSD Controller Solutions. https:\/\/www.arm.com\/solutions\/storage  ARM. 2022. ARM SSD Controller Solutions. https:\/\/www.arm.com\/solutions\/storage"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSCCC.2018.8703316"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2021.3061394"},{"key":"e_1_3_2_1_6_1","volume-title":"Very deep convolutional networks for natural language processing. arXiv preprint arXiv:1606.01781 2, 1","author":"Conneau Alexis","year":"2016","unstructured":"Alexis Conneau , Holger Schwenk , Lo\u0131c Barrault , and Yann Lecun . 2016. Very deep convolutional networks for natural language processing. arXiv preprint arXiv:1606.01781 2, 1 ( 2016 ). Alexis Conneau, Holger Schwenk, Lo\u0131c Barrault, and Yann Lecun. 2016. Very deep convolutional networks for natural language processing. arXiv preprint arXiv:1606.01781 2, 1 (2016)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2380656.2380672"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2017.11.153"},{"key":"e_1_3_2_1_9_1","volume-title":"2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 845--848","author":"Ibrahim Ali","year":"2018","unstructured":"Ali Ibrahim , Mario Osta , Mohamad Alameh , Moustafa Saleh , Hussein Chible , and Maurizio Valle . 2018 . Approximate computing methods for embedded machine learning . In 2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 845--848 . Ali Ibrahim, Mario Osta, Mohamad Alameh, Moustafa Saleh, Hussein Chible, and Maurizio Valle. 2018. Approximate computing methods for embedded machine learning. In 2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 845--848."},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the International Symposium on Low Power Electronics and Design. 1--6.","author":"Imani Mohsen","year":"2018","unstructured":"Mohsen Imani , Ricardo Garcia , Saransh Gupta , and Tajana Rosing . 2018 . Rmac: Runtime configurable floating point multiplier for approximate computing . In Proceedings of the International Symposium on Low Power Electronics and Design. 1--6. Mohsen Imani, Ricardo Garcia, Saransh Gupta, and Tajana Rosing. 2018. Rmac: Runtime configurable floating point multiplier for approximate computing. In Proceedings of the International Symposium on Low Power Electronics and Design. 1--6."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3034117"},{"key":"e_1_3_2_1_12_1","volume-title":"Behemoth: A Flash-centric Training Accelerator for Extreme-scale DNNs. In 19th USENIX Conference on File and Storage Technologies (FAST 21)","author":"Kim Shine","unstructured":"Shine Kim , Yunho Jin , Gina Sohn , Jonghyun Bae , Tae Jun Ham , and Jae W. Lee . 2021 . Behemoth: A Flash-centric Training Accelerator for Extreme-scale DNNs. In 19th USENIX Conference on File and Storage Technologies (FAST 21) . USENIX Association, 371--385. https:\/\/www.usenix.org\/conference\/fast21\/presentation\/kim Shine Kim, Yunho Jin, Gina Sohn, Jonghyun Bae, Tae Jun Ham, and Jae W. Lee. 2021. Behemoth: A Flash-centric Training Accelerator for Extreme-scale DNNs. In 19th USENIX Conference on File and Storage Technologies (FAST 21). USENIX Association, 371--385. https:\/\/www.usenix.org\/conference\/fast21\/presentation\/kim"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-7351-7_6"},{"key":"e_1_3_2_1_14_1","volume-title":"Analysis and optimization of loss functions for multiclass, top-k, and multilabel classification","author":"Lapin Maksim","year":"2017","unstructured":"Maksim Lapin , Matthias Hein , and Bernt Schiele . 2017. Analysis and optimization of loss functions for multiclass, top-k, and multilabel classification . IEEE transactions on pattern analysis and machine intelligence 40, 7 ( 2017 ), 1533--1554. Maksim Lapin, Matthias Hein, and Bernt Schiele. 2017. Analysis and optimization of loss functions for multiclass, top-k, and multilabel classification. IEEE transactions on pattern analysis and machine intelligence 40, 7 (2017), 1533--1554."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISLPED52811.2021.9502476"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCA.2020.3009347"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCA.2020.3009347"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC42614.2022.9731711"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3470496.3527391"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3470496.3527433"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080834"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3466752.3480090"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO50266.2020.00066"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA.2018.00035"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503222.3507702"},{"key":"e_1_3_2_1_26_1","unstructured":"MARVELL. 2022. MARVELL SSD Controller Solutions. https:\/\/www.arm.com\/solutions\/storage  MARVELL. 2022. MARVELL SSD Controller Solutions. https:\/\/www.arm.com\/solutions\/storage"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2507157.2507163"},{"key":"e_1_3_2_1_28_1","volume-title":"Extreme classification in log memory using countmin sketch: A case study of amazon search with 50m products. Advances in Neural Information Processing Systems 32","author":"Reddy Medini Tharun Kumar","year":"2019","unstructured":"Tharun Kumar Reddy Medini , Qixuan Huang , Yiqiu Wang , Vijai Mohan , and Anshumali Shrivastava . 2019. Extreme classification in log memory using countmin sketch: A case study of amazon search with 50m products. Advances in Neural Information Processing Systems 32 ( 2019 ). Tharun Kumar Reddy Medini, Qixuan Huang, Yiqiu Wang, Vijai Mohan, and Anshumali Shrivastava. 2019. Extreme classification in log memory using countmin sketch: A case study of amazon search with 50m products. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_29_1","volume-title":"Nitish Shirish Keskar, and Richard Socher","author":"Merity Stephen","year":"2017","unstructured":"Stephen Merity , Nitish Shirish Keskar, and Richard Socher . 2017 . Regularizing and optimizing LSTM language models. arXiv preprint arXiv:1708.02182 (2017). Stephen Merity, Nitish Shirish Keskar, and Richard Socher. 2017. Regularizing and optimizing LSTM language models. arXiv preprint arXiv:1708.02182 (2017)."},{"key":"e_1_3_2_1_30_1","unstructured":"MicroCenter. 2023. Inland Platinum 4TB SSD. https:\/\/www.microcenter.com\/product\/627020\/inland-platinum-4tb-ssd-m2-2280-nvme-pcie-gen-30x4-3d-nand-internal-solid-state-drive -pcie-express-31-and-nvme-13-compatible -ultimate-gaming-solutio  MicroCenter. 2023. Inland Platinum 4TB SSD. https:\/\/www.microcenter.com\/product\/627020\/inland-platinum-4tb-ssd-m2-2280-nvme-pcie-gen-30x4-3d-nand-internal-solid-state-drive -pcie-express-31-and-nvme-13-compatible -ultimate-gaming-solutio"},{"key":"e_1_3_2_1_31_1","unstructured":"Micron. 2023. How Much Power Does Memory Use? https:\/\/www.crucial.com\/support\/articles-faq-memory\/how-much-power-does-memory-use  Micron. 2023. How Much Power Does Memory Use? https:\/\/www.crucial.com\/support\/articles-faq-memory\/how-much-power-does-memory-use"},{"key":"e_1_3_2_1_32_1","unstructured":"MUSE. 2023. 28nm chip fabrication cost. https:\/\/www.musesemi.com\/shared-block-tapeout-pricing  MUSE. 2023. 28nm chip fabrication cost. https:\/\/www.musesemi.com\/shared-block-tapeout-pricing"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3164541.3164560"},{"key":"e_1_3_2_1_34_1","unstructured":"Maxim Naumov Dheevatsa Mudigere Hao-Jun Michael Shi Jianyu Huang Narayanan Sundaraman Jongsoo Park Xiaodong Wang Udit Gupta Carole-Jean Wu Alisson G Azzolini Dmytro Dzhulgakov Andrey Mallevich Ilia Cherniavskii Yinghai Lu Raghuraman Krishnamoorthi Ansha Yu Volodymyr Kondratenko Stephanie Pereira Xianjie Chen Wenlin Chen Vijay Rao Bill Jia Liang Xiong and Misha Smelyanskiy. 2019. Deep learning recommendation model for personalization and recommendation systems. arXiv preprint arXiv:1906.00091 (2019).  Maxim Naumov Dheevatsa Mudigere Hao-Jun Michael Shi Jianyu Huang Narayanan Sundaraman Jongsoo Park Xiaodong Wang Udit Gupta Carole-Jean Wu Alisson G Azzolini Dmytro Dzhulgakov Andrey Mallevich Ilia Cherniavskii Yinghai Lu Raghuraman Krishnamoorthi Ansha Yu Volodymyr Kondratenko Stephanie Pereira Xianjie Chen Wenlin Chen Vijay Rao Bill Jia Liang Xiong and Misha Smelyanskiy. 2019. Deep learning recommendation model for personalization and recommendation systems. arXiv preprint arXiv:1906.00091 (2019)."},{"key":"e_1_3_2_1_35_1","unstructured":"NVIDIA. 2022. GeForce RTX 3090 Family. https:\/\/www.nvidia.com\/en-us\/geforce\/graphics-cards\/30-series\/rtx-3090-3090ti\/  NVIDIA. 2022. GeForce RTX 3090 Family. https:\/\/www.nvidia.com\/en-us\/geforce\/graphics-cards\/30-series\/rtx-3090-3090ti\/"},{"key":"e_1_3_2_1_36_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , Alban Desmaison , Andreas K\u00f6pf , Edward Yang , Zach DeVito , Martin Raison , Alykhan Tejani , Sasank Chilamkurthy , Benoit Steiner , Lu Fang , Junjie Bai , and Soumith Chintala . 2019 . Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019). Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas K\u00f6pf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3185998"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3431920.3439298"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3472769"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2018.2858362"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.23919\/ICACT.2018.8323642"},{"key":"e_1_3_2_1_42_1","unstructured":"Kyuhong Shim Minjae Lee Iksoo Choi Yoonho Boo and Wonyong Sung. 2017. SVD-softmax: Fast softmax approximation on large vocabulary neural networks. In Neural Information Processing Systems (NeurIPS). 5464--5474.  Kyuhong Shim Minjae Lee Iksoo Choi Yoonho Boo and Wonyong Sung. 2017. SVD-softmax: Fast softmax approximation on large vocabulary neural networks. In Neural Information Processing Systems (NeurIPS). 5464--5474."},{"key":"e_1_3_2_1_43_1","volume-title":"2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin). IEEE, 301--305","author":"Shin Yong-Goo","year":"2019","unstructured":"Yong-Goo Shin , Yoon-Jae Yeo , Min-Cheol Sagong , Seo-Won Ji , and Sung-Jea Ko . 2019 . Deep fashion recommendation system with style feature decomposition . In 2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin). IEEE, 301--305 . Yong-Goo Shin, Yoon-Jae Yeo, Min-Cheol Sagong, Seo-Won Ji, and Sung-Jea Ko. 2019. Deep fashion recommendation system with style feature decomposition. In 2019 IEEE 9th International Conference on Consumer Electronics (ICCE-Berlin). IEEE, 301--305."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA53966.2022.00081"},{"key":"e_1_3_2_1_45_1","volume-title":"Sequence to sequence learning with neural networks. Advances in neural information processing systems 27","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever , Oriol Vinyals , and Quoc V Le. 2014. Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 ( 2014 ). Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014)."},{"key":"e_1_3_2_1_46_1","volume-title":"MQSim: A Framework for Enabling Realistic Studies of Modern Multi-Queue SSD Devices. In 16th USENIX Conference on File and Storage Technologies (FAST 18)","author":"Tavakkol Arash","year":"2018","unstructured":"Arash Tavakkol , Juan G\u00f3mez-Luna , Mohammad Sadrosadati , Saugata Ghose , and Onur Mutlu . 2018 . MQSim: A Framework for Enabling Realistic Studies of Modern Multi-Queue SSD Devices. In 16th USENIX Conference on File and Storage Technologies (FAST 18) . 49--66. Arash Tavakkol, Juan G\u00f3mez-Luna, Mohammad Sadrosadati, Saugata Ghose, and Onur Mutlu. 2018. MQSim: A Framework for Enabling Realistic Studies of Modern Multi-Queue SSD Devices. In 16th USENIX Conference on File and Storage Technologies (FAST 18). 49--66."},{"key":"e_1_3_2_1_47_1","volume-title":"Active Computation on SSD. In 2012 Workshop on Power-Aware Computing and Systems (HotPower 12)","author":"Tiwari Devesh","unstructured":"Devesh Tiwari , Sudharshan S. Vazhkudai , Youngjae Kim , Xiaosong Ma , Simona Boboila , and Peter J. Desnoyers . 2012. Reducing Data Movement Costs Using Energy-Efficient , Active Computation on SSD. In 2012 Workshop on Power-Aware Computing and Systems (HotPower 12) . USENIX Association, Hollywood, CA. https:\/\/www.usenix.org\/conference\/hotpower12\/workshop-program\/presentation\/Tiwari Devesh Tiwari, Sudharshan S. Vazhkudai, Youngjae Kim, Xiaosong Ma, Simona Boboila, and Peter J. Desnoyers. 2012. Reducing Data Movement Costs Using Energy-Efficient, Active Computation on SSD. In 2012 Workshop on Power-Aware Computing and Systems (HotPower 12). USENIX Association, Hollywood, CA. https:\/\/www.usenix.org\/conference\/hotpower12\/workshop-program\/presentation\/Tiwari"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3029453"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSCC42614.2022.9731659"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2933349.2933353"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01921-y"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3445814.3446763"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3329785.3329916"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_1_55_1","volume-title":"2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 113--114","author":"Younes Hamoud","year":"2019","unstructured":"Hamoud Younes , Ali Ibrahim , Mostafa Rizk , and Maurizio Valle . 2019 . Algorithmic level approximate computing for machine learning classifiers . In 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 113--114 . Hamoud Younes, Ali Ibrahim, Mostafa Rizk, and Maurizio Valle. 2019. Algorithmic level approximate computing for machine learning classifiers. In 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 113--114."},{"key":"e_1_3_2_1_56_1","volume-title":"2017 IEEE international conference on web services (ICWS). IEEE, 404--411","author":"Zhuang Hang","year":"2017","unstructured":"Hang Zhuang , Chao Wang , Changlong Li , Qingfeng Wang , and Xuehai Zhou . 2017 . Natural language processing service based on stroke-level convolutional networks for Chinese text classification . In 2017 IEEE international conference on web services (ICWS). IEEE, 404--411 . Hang Zhuang, Chao Wang, Changlong Li, Qingfeng Wang, and Xuehai Zhou. 2017. Natural language processing service based on stroke-level convolutional networks for Chinese text classification. In 2017 IEEE international conference on web services (ICWS). IEEE, 404--411."}],"event":{"name":"ISCA '23: 50th Annual International Symposium on Computer Architecture","location":"Orlando FL USA","acronym":"ISCA '23","sponsor":["SIGARCH ACM Special Interest Group on Computer Architecture","IEEE"]},"container-title":["Proceedings of the 50th Annual International Symposium on Computer Architecture"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3579371.3589093","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:39Z","timestamp":1750178799000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3579371.3589093"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,17]]},"references-count":56,"alternative-id":["10.1145\/3579371.3589093","10.1145\/3579371"],"URL":"https:\/\/doi.org\/10.1145\/3579371.3589093","relation":{},"subject":[],"published":{"date-parts":[[2023,6,17]]},"assertion":[{"value":"2023-06-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}