{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:50:33Z","timestamp":1773481833217,"version":"3.50.1"},"reference-count":84,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T00:00:00Z","timestamp":1685059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2023,5,26]]},"abstract":"<jats:p>The dynamic nature of resource allocation and runtime conditions on Cloud can result in high variability in a job's runtime across multiple iterations, leading to a poor experience. Identifying the sources of such variation and being able to predict and adjust for them is crucial to cloud service providers to design reliable data processing pipelines, provision and allocate resources, adjust pricing services, meet SLOs and debug performance hazards.<\/jats:p>\n          <jats:p>In this paper, we analyze the runtime variation of millions of production Scope jobs on Cosmos, an exabyte-scale internal analytics platform at Microsoft. We propose an innovative 2-step approach to predict job runtime distribution by characterizing typical distribution shapes combined with a classification model with an average accuracy of &gt;96%, using an innovative interpretable machine-learning algorithm out-performing traditional regression models and better capturing long tails. We examine factors such as job plan characteristics and inputs, resource allocation, physical cluster heterogeneity and utilization, and scheduling policies.<\/jats:p>\n          <jats:p>To the best of our knowledge, this is the first study on predicting categories of runtime distributions for enterprise analytics workloads at scale. Furthermore, we examine how our methods can be used to analyze what-if scenarios, focusing on the impact of resource allocation, scheduling, and physical cluster provisioning decisions on a job's runtime consistency and predictability.<\/jats:p>","DOI":"10.1145\/3588921","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T17:42:05Z","timestamp":1685468525000},"page":"1-20","source":"Crossref","is-referenced-by-count":4,"title":["Runtime Variation in Big Data Analytics"],"prefix":"10.1145","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6857-7505","authenticated-orcid":false,"given":"Yiwen","family":"Zhu","sequence":"first","affiliation":[{"name":"Microsoft, Redmond, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4736-2837","authenticated-orcid":false,"given":"Rathijit","family":"Sen","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7305-354X","authenticated-orcid":false,"given":"Robert","family":"Horton","sequence":"additional","affiliation":[{"name":"Microsoft, Mountain View, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9826-6509","authenticated-orcid":false,"given":"John Mark","family":"Agosta","sequence":"additional","affiliation":[{"name":"Microsoft, Mountain View, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"https:\/\/aws.amazon.com\/aws\/ec2 Retrieved","author":"Amazon","year":"2022","unstructured":"Amazon. 2022. Amazon EC2. https:\/\/aws.amazon.com\/aws\/ec2 Retrieved Feb 15, 2022 from"},{"key":"e_1_2_2_2_1","unstructured":"020)]% aws-athena Amazon.com Inc. 2020. Amazon Athena. https:\/\/aws.amazon.com\/athena\/ Retrieved July 4 2020 from"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2011.62"},{"key":"e_1_2_2_4_1","volume-title":"Pattern recognition and machine learning","author":"Bishop Christopher M","unstructured":"Christopher M Bishop and Nasser M Nasrabadi. 2006. Pattern recognition and machine learning. Vol. 4. Springer."},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.14778\/2824032.2824066"},{"key":"e_1_2_2_6_1","volume-title":"11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14)","author":"Boutin Eric","year":"2014","unstructured":"Eric Boutin, Jaliya Ekanayake, Wei Lin, Bing Shi, Jingren Zhou, Zhengping Qian, Ming Wu, and Lidong Zhou. 2014. Apollo: Scalable and coordinated scheduling for cloud-scale computing. In 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14). 285--300."},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2213836.2213959"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/2536222.2536223"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-011-0221-2"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/1454159.1454166"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/WiCOM.2012.6478433"},{"key":"e_1_2_2_12_1","volume-title":"Carlo Curino, and Gregory R Ganger","author":"Chung Andrew","year":"2020","unstructured":"Andrew Chung, Subru Krishnan, Konstantinos Karanasos, Carlo Curino, and Gregory R Ganger. 2020a. Unearthing inter-job dependencies for better cluster scheduling. In 14th $$USENIX$$ Symposium on Operating Systems Design and Implementation ($$OSDI$$ 20). 1205--1223."},{"key":"e_1_2_2_13_1","volume-title":"14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20)","author":"Chung Andrew","unstructured":"Andrew Chung, Subru Krishnan, Konstantinos Karanasos, Carlo Curino, and Gregory R. Ganger. 2020b. Unearthing inter-job dependencies for better cluster scheduling. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20). 1205--1223."},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3399579.3399927"},{"key":"e_1_2_2_15_1","volume-title":"16th USENIX Symposium on Networked Systems Design and Implementation (NSDI 19)","author":"Curino Carlo","year":"2019","unstructured":"Carlo Curino, Subru Krishnan, Konstantinos Karanasos, Sriram Rao, Giovanni M Fumarola, Botong Huang, Kishore Chaliparambil, Arun Suresh, Young Chen, Solom Heddaya, et al. 2019. Hydra: a federated resource manager for data-center scale analytics. In 16th USENIX Symposium on Networked Systems Design and Implementation (NSDI 19). 177--192."},{"key":"e_1_2_2_16_1","volume-title":"Cloud Data Design, Orchestration, and Management Using Microsoft Azure","author":"Diaz Francesco","unstructured":"Francesco Diaz and Roberto Freato. 2018. Azure Data Lake Store and Azure Data Lake Analytics. In Cloud Data Design, Orchestration, and Management Using Microsoft Azure. Springer, 327--392."},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2009.115"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3401071.3401656"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/646376.689370"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2168836.2168847"},{"key":"e_1_2_2_21_1","volume-title":"A history of the central limit theorem: From classical to modern probability theory","author":"Fischer Hans","unstructured":"Hans Fischer. 2011. A history of the central limit theorem: From classical to modern probability theory. Springer."},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2807591.2807646"},{"key":"e_1_2_2_23_1","first-page":"1","article-title":"Probability plotting methods for the analysis of data","volume":"55","author":"Gnanadesikan Ramanathan","year":"1968","unstructured":"Ramanathan Gnanadesikan and Martin B Wilk. 1968. Probability plotting methods for the analysis of data. Biometrika, Vol. 55, 1 (1968), 1--17.","journal-title":"Biometrika"},{"key":"e_1_2_2_24_1","volume-title":"CPU Utilization is Wrong. https:\/\/www.brendangregg.com\/blog\/2017-05-09\/cpu-utilization-is-wrong.html Retrieved","year":"2022","unstructured":"Gregg, Brendan. 2022. CPU Utilization is Wrong. https:\/\/www.brendangregg.com\/blog\/2017-05-09\/cpu-utilization-is-wrong.html Retrieved Oct 4, 2022 from"},{"key":"e_1_2_2_25_1","volume-title":"Asian Conference on Supercomputing Frontiers. Springer, Cham, 179--198","author":"Guo Jian","year":"2018","unstructured":"Jian Guo, Akihiro Nomura, Ryan Barton, Haoyu Zhang, and Satoshi Matsuoka. 2018. Machine learning predictions for underestimation of job runtime on HPC system. In Asian Conference on Supercomputing Frontiers. Springer, Cham, 179--198."},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/3468.541341"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2038916.2038934"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155521"},{"key":"e_1_2_2_29_1","volume-title":"Identifying the major sources of variance in transaction latencies: Towards more predictable databases. arXiv preprint arXiv:1602.01871","author":"Huang Jiamin","year":"2016","unstructured":"Jiamin Huang, Barzan Mozafari, Grant Schoenebeck, and Thomas Wenisch. 2016. Identifying the major sources of variance in transaction latencies: Towards more predictable databases. arXiv preprint arXiv:1602.01871 (2016)."},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064016"},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357223.3362726"},{"key":"e_1_2_2_32_1","volume-title":"12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)","author":"Jyothi Sangeetha Abdu","year":"2016","unstructured":"Sangeetha Abdu Jyothi, Carlo Curino, Ishai Menache, Shravan Matthur Narayanamurthy, Alexey Tumanov, Jonathan Yaniv, Ruslan Mavlyutov, \u00cd nigo Goiri, Subru Krishnan, Janardhan Kulkarni, et al. 2016. Morpheus: Towards automated slos for enterprise clusters. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). 117--134."},{"key":"e_1_2_2_33_1","volume-title":"2015 USENIX Annual Technical Conference (USENIX ATC 15)","author":"Karanasos Konstantinos","year":"2015","unstructured":"Konstantinos Karanasos, Sriram Rao, Carlo Curino, Chris Douglas, Kishore Chaliparambil, Giovanni Matteo Fumarola, Solom Heddaya, Raghu Ramakrishnan, and Sarvesh Sakalanaga. 2015. Mercury: Hybrid centralized and distributed scheduling in large shared clusters. In 2015 USENIX Annual Technical Conference (USENIX ATC 15). 485--497."},{"key":"e_1_2_2_34_1","volume-title":"RG Laha, and J.","author":"Karson Marvin","year":"1967","unstructured":"Marvin Karson. 1968. Handbook of Methods of Applied Statistics. Volume I: Techniques of Computation Descriptive Methods, and Statistical Inference. Volume II: Planning of Surveys and Experiments. IM Chakravarti, RG Laha, and J. Roy, New York, John Wiley; 1967, $9.00."},{"key":"e_1_2_2_35_1","volume-title":"Chuck Cranor, Elisabeth Moore, Nathan DeBardeleben, and George Amvrosiadis.","author":"Kuchnik Michael","year":"2019","unstructured":"Michael Kuchnik, Jun Woo Park, Chuck Cranor, Elisabeth Moore, Nathan DeBardeleben, and George Amvrosiadis. 2019. This is why ML-driven cluster scheduling remains widely impractical. Tech. rep. (2019)."},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2697063"},{"key":"e_1_2_2_37_1","volume-title":"Understanding variable importances in forests of randomized trees. Advances in neural information processing systems","author":"Louppe Gilles","year":"2013","unstructured":"Gilles Louppe, Louis Wehenkel, Antonio Sutera, and Pierre Geurts. 2013. Understanding variable importances in forests of randomized trees. Advances in neural information processing systems, Vol. 26 (2013)."},{"key":"e_1_2_2_38_1","volume-title":"A unified approach to interpreting model predictions. Advances in neural information processing systems","author":"Lundberg Scott M","year":"2017","unstructured":"Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098062"},{"key":"e_1_2_2_40_1","volume-title":"https:\/\/lightgbm.readthedocs.io\/en\/latest\/pythonapi\/lightgbm.LGBMClassifier.html Retrieved","author":"Microsoft Corporation","year":"2022","unstructured":"Microsoft Corporation. 2022. LGBMClassifier. https:\/\/lightgbm.readthedocs.io\/en\/latest\/pythonapi\/lightgbm.LGBMClassifier.html Retrieved Feb 4, 2022 from"},{"key":"e_1_2_2_41_1","unstructured":"Christoph Molnar. 2020. Interpretable machine learning. Lulu. com."},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/CLOUD.2018.00104"},{"key":"e_1_2_2_43_1","volume-title":"Introduction to HPC with MPI for Data Science","author":"Nielsen Frank","unstructured":"Frank Nielsen. 2016. Introduction to HPC with MPI for Data Science. Springer."},{"key":"e_1_2_2_44_1","volume-title":"Interpretml: A unified framework for machine learning interpretability. arXiv preprint arXiv:1909.09223","author":"Nori Harsha","year":"2019","unstructured":"Harsha Nori, Samuel Jenkins, Paul Koch, and Rich Caruana. 2019. Interpretml: A unified framework for machine learning interpretability. arXiv preprint arXiv:1909.09223 (2019)."},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/WORKS.2014.12"},{"key":"e_1_2_2_46_1","volume-title":"Towards Optimal Resource Allocation for Big Data Analytics. In 25th International Conference on Extending Database Technology (EDBT). 338--350","author":"Pimpley Anish","year":"2022","unstructured":"Anish Pimpley, Shuo Li, Rathijit Sen, Soundararajan Srinivasan, and Alekh Jindal. 2022. Towards Optimal Resource Allocation for Big Data Analytics. In 25th International Conference on Extending Database Technology (EDBT). 338--350."},{"key":"e_1_2_2_47_1","volume-title":"Optimal Resource Allocation for Serverless Queries. arXiv preprint arXiv:2107.08594","author":"Pimpley Anish","year":"2021","unstructured":"Anish Pimpley, Shuo Li, Anubha Srivastava, Vishal Rohra, Yi Zhu, Soundararajan Srinivasan, Alekh Jindal, Hiren Patel, Shi Qiao, and Rathijit Sen. 2021. Optimal Resource Allocation for Serverless Queries. arXiv preprint arXiv:2107.08594 (2021)."},{"key":"e_1_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476311.3476390"},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.14778\/3339490.3339495"},{"key":"e_1_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2987550.2987566"},{"key":"e_1_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3056100"},{"key":"e_1_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1002\/sta4.183"},{"key":"e_1_2_2_53_1","volume-title":"Data mining and knowledge discovery handbook","author":"Rokach Lior","unstructured":"Lior Rokach and Oded Maimon. 2005. Clustering methods. In Data mining and knowledge discovery handbook. Springer, 321--352."},{"key":"e_1_2_2_54_1","volume-title":"Learning AWS: Design, build, and deploy responsive applications using AWS Cloud components","author":"Sarkar Aurobindo","unstructured":"Aurobindo Sarkar and Amit Shah. 2018. Learning AWS: Design, build, and deploy responsive applications using AWS Cloud components. Packt Publishing Ltd."},{"key":"e_1_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920902"},{"key":"e_1_2_2_56_1","volume-title":"https:\/\/scikit-learn.org\/stable\/modules\/ensemble.html Retrieved","year":"2022","unstructured":"Scikit-Learn. 2022a. EnsembledClassifier. https:\/\/scikit-learn.org\/stable\/modules\/ensemble.html Retrieved Feb 4, 2022 from"},{"key":"e_1_2_2_57_1","volume-title":"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.naive_bayes.GaussianNB.html Retrieved","author":"NB.","year":"2022","unstructured":"Scikit-Learn. 2022b. GaussianNB. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.naive_bayes.GaussianNB.html Retrieved Feb 4, 2022 from"},{"key":"e_1_2_2_58_1","volume-title":"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.GradientBoostingClassifier.html Retrieved","year":"2022","unstructured":"Scikit-Learn. 2022c. GradientBoostingClassifier. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.GradientBoostingClassifier.html Retrieved Feb 4, 2022 from"},{"key":"e_1_2_2_59_1","volume-title":"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.GradientBoostingClassifier.html Retrieved","year":"2022","unstructured":"Scikit-Learn. 2022d. GradientBoostingClassifier. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.GradientBoostingClassifier.html Retrieved Feb 4, 2022 from"},{"key":"e_1_2_2_60_1","volume-title":"2022 e. RandomForestClassifier. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.RandomForestClassifier.html Retrieved","year":"2022","unstructured":"Scikit-Learn. 2022 e. RandomForestClassifier. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.ensemble.RandomForestClassifier.html Retrieved Feb 4, 2022 from"},{"key":"e_1_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772862"},{"key":"e_1_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415554"},{"key":"e_1_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476311.3476362"},{"key":"e_1_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357223.3362716"},{"key":"e_1_2_2_65_1","article-title":"A value for n-person games. Contributions to the Theory of Games II","volume":"28","author":"Ll S Shapley","year":"1953","unstructured":"S Shapley Ll. 1953. A value for n-person games. Contributions to the Theory of Games II, Annals of Mathematical Studies, Vol. 28 (1953).","journal-title":"Annals of Mathematical Studies"},{"key":"e_1_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/1551609.1551632"},{"key":"e_1_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687553.1687609"},{"key":"e_1_2_2_68_1","volume-title":"Google BigQuery Analytics","author":"Tigani Jordan","unstructured":"Jordan Tigani and Siddartha Naidu. 2014. Google BigQuery Analytics. John Wiley & Sons."},{"key":"e_1_2_2_69_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2007.70606"},{"key":"e_1_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687707"},{"key":"e_1_2_2_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064029"},{"key":"e_1_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/2523616.2523633"},{"key":"e_1_2_2_73_1","first-page":"10","article-title":"Spark: Cluster computing with working sets","volume":"10","author":"Zaharia Matei","year":"2010","unstructured":"Matei Zaharia, Mosharaf Chowdhury, Michael J Franklin, Scott Shenker, Ion Stoica, et al. 2010. Spark: Cluster computing with working sets. HotCloud, Vol. 10, 10--10 (2010), 95.","journal-title":"HotCloud"},{"key":"e_1_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3300085"},{"key":"e_1_2_2_75_1","volume-title":"Presented as part of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12). 295--308.","author":"Zhang Jiaxing","unstructured":"Jiaxing Zhang, Hucheng Zhou, Rishan Chen, Xuepeng Fan, Zhenyu Guo, Haoxiang Lin, Jack Y Li, Wei Lin, Jingren Zhou, and Lidong Zhou. 2012. Optimizing data shuffling in data-parallel computation by understanding user-defined functions. In Presented as part of the 9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12). 295--308."},{"key":"e_1_2_2_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389749"},{"key":"e_1_2_2_77_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-25255-1_58"},{"key":"e_1_2_2_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/3179420"},{"key":"e_1_2_2_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/2213836.2213839"},{"key":"e_1_2_2_80_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2010.5447802"},{"key":"e_1_2_2_81_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476249.3476298"},{"key":"e_1_2_2_82_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457569"},{"key":"e_1_2_2_83_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPDC.1999.805303"},{"key":"e_1_2_2_84_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2022.01.003"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3588921","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3588921","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:47:37Z","timestamp":1750178857000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3588921"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,26]]},"references-count":84,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,5,26]]}},"alternative-id":["10.1145\/3588921"],"URL":"https:\/\/doi.org\/10.1145\/3588921","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,26]]}}}