{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T12:28:30Z","timestamp":1764332910383,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,8,17]],"date-time":"2020-08-17T00:00:00Z","timestamp":1597622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000105","name":"Office of Advanced Cyberinfrastructure","doi-asserted-by":"publisher","award":["1724845"],"award-info":[{"award-number":["1724845"]}],"id":[{"id":"10.13039\/100000105","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1827674"],"award-info":[{"award-number":["1827674"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000143","name":"Division of Computing and Communication Foundations","doi-asserted-by":"publisher","award":["1822965"],"award-info":[{"award-number":["1822965"]}],"id":[{"id":"10.13039\/100000143","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006112","name":"Microsoft Research","doi-asserted-by":"publisher","award":["8300751"],"award-info":[{"award-number":["8300751"]}],"id":[{"id":"10.13039\/100006112","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,8,17]]},"DOI":"10.1145\/3404397.3404466","type":"proceedings-article","created":{"date-parts":[[2020,8,9]],"date-time":"2020-08-09T03:54:26Z","timestamp":1596945266000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["A Reinforcement Learning Based System for Minimizing Cloud Storage Service Cost"],"prefix":"10.1145","author":[{"given":"Haoyu","family":"Wang","sequence":"first","affiliation":[{"name":"University of Virginia, USA"}]},{"given":"Haiying","family":"Shen","sequence":"additional","affiliation":[{"name":"University of Virginia, USA"}]},{"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Virginia, USA"}]},{"given":"Kevin","family":"Zheng","sequence":"additional","affiliation":[{"name":"University of Virginia, USA"}]},{"given":"Jie","family":"Xu","sequence":"additional","affiliation":[{"name":"George Mason University, USA"}]}],"member":"320","published-online":{"date-parts":[[2020,8,17]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"https:\/\/aws.amazon.com\/cn\/s3\/, [accessed","author":"Amazon","year":"2020","unstructured":"[n.d.]. Amazon S3. https:\/\/aws.amazon.com\/cn\/s3\/, [accessed in Jan. 2020 ]. [n.d.]. Amazon S3. https:\/\/aws.amazon.com\/cn\/s3\/, [accessed in Jan. 2020]."},{"volume-title":"ARIMA model for Time Series Forecasting. https:\/\/machinelearningmastery.com\/arima-for-time-series-forecasting-with-python\/, [accessed","year":"2020","key":"e_1_3_2_1_2_1","unstructured":"[n.d.]. ARIMA model for Time Series Forecasting. https:\/\/machinelearningmastery.com\/arima-for-time-series-forecasting-with-python\/, [accessed in Jan. 2020 ]. [n.d.]. ARIMA model for Time Series Forecasting. https:\/\/machinelearningmastery.com\/arima-for-time-series-forecasting-with-python\/, [accessed in Jan. 2020]."},{"volume-title":"Azure Storage Pricing Policy. https:\/\/azure.microsoft.com\/en-us\/pricing\/details\/storage\/blobs\/ , [accessed","year":"2020","key":"e_1_3_2_1_3_1","unstructured":"[n.d.]. Azure Storage Pricing Policy. https:\/\/azure.microsoft.com\/en-us\/pricing\/details\/storage\/blobs\/ , [accessed in Jan. 2020 ]. [n.d.]. Azure Storage Pricing Policy. https:\/\/azure.microsoft.com\/en-us\/pricing\/details\/storage\/blobs\/ , [accessed in Jan. 2020]."},{"volume-title":"Google Cloud Storage. https:\/\/cloud.google.com\/storage\/, [accessed","year":"2020","key":"e_1_3_2_1_4_1","unstructured":"[n.d.]. Google Cloud Storage. https:\/\/cloud.google.com\/storage\/, [accessed in Jan. 2020 ]. [n.d.]. Google Cloud Storage. https:\/\/cloud.google.com\/storage\/, [accessed in Jan. 2020]."},{"key":"e_1_3_2_1_5_1","volume-title":"https:\/\/azure.microsoft.com\/en-us\/, [accessed","author":"Azure Microsoft","year":"2020","unstructured":"[n.d.]. Microsoft Azure . https:\/\/azure.microsoft.com\/en-us\/, [accessed in Jan. 2020 ]. [n.d.]. Microsoft Azure. https:\/\/azure.microsoft.com\/en-us\/, [accessed in Jan. 2020]."},{"volume-title":"Page View statistics from Wikimedia Projects. https:\/\/dumps.wikimedia.org\/other\/pagecounts-ez\/, [accessed","year":"2020","key":"e_1_3_2_1_6_1","unstructured":"[n.d.]. Page View statistics from Wikimedia Projects. https:\/\/dumps.wikimedia.org\/other\/pagecounts-ez\/, [accessed in Jan. 2020 ]. [n.d.]. Page View statistics from Wikimedia Projects. https:\/\/dumps.wikimedia.org\/other\/pagecounts-ez\/, [accessed in Jan. 2020]."},{"volume-title":"Proc. of OSDI.","author":"Mart\u00edn","key":"e_1_3_2_1_7_1","unstructured":"Mart\u00edn A., Paul B., Jianmin C., Zhifeng C., Andy D., Jeffrey D., Matthieu D., Sanjay G., Geoffrey I., and Michael I . 2016. Tensorflow: a system for large-scale machine learning .. In Proc. of OSDI. Mart\u00edn A., Paul B., Jianmin C., Zhifeng C., Andy D., Jeffrey D., Matthieu D., Sanjay G., Geoffrey I., and Michael I.2016. Tensorflow: a system for large-scale machine learning.. In Proc. of OSDI."},{"volume-title":"Proc. of SOCC.","author":"Abu-Libdeh H.","key":"e_1_3_2_1_8_1","unstructured":"H. Abu-Libdeh , L. Princehouse , and H. Weatherspoon . 2010. RACS: a case for cloud storage diversity . In Proc. of SOCC. H. Abu-Libdeh, L. Princehouse, and H. Weatherspoon. 2010. RACS: a case for cloud storage diversity. In Proc. of SOCC."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1060289.1060291"},{"key":"e_1_3_2_1_10_1","volume":"200","author":"Alvarez G.","unstructured":"G. Alvarez , E. Borowsky , S. Go , T. Romer , R. Becker-Szendy , R. Golding , A. Merchant , M. Spasojevic , A. Veitch , and J. Wilkes. 200 1. Minerva: An automated resource provisioning tool for large-scale storage systems. Trans. on TOCS (2001). G. Alvarez, E. Borowsky, S. Go, T. Romer, R. Becker-Szendy, R. Golding, A. Merchant, M. Spasojevic, A. Veitch, and J. Wilkes. 2001. Minerva: An automated resource provisioning tool for large-scale storage systems. Trans. on TOCS (2001).","journal-title":"J. Wilkes."},{"volume-title":"Proc. of FAST.","author":"Anderson E.","key":"e_1_3_2_1_11_1","unstructured":"E. Anderson , M. Hobbs , K. Keeton , S. Spence , M. Uysal , and A. Veitch . 2002. Hippodrome: Running Circles Around Storage Administration .. In Proc. of FAST. E. Anderson, M. Hobbs, K. Keeton, S. Spence, M. Uysal, and A. Veitch. 2002. Hippodrome: Running Circles Around Storage Administration.. In Proc. of FAST."},{"volume-title":"Proc. of SIGCOM.","author":"Chen L.","key":"e_1_3_2_1_12_1","unstructured":"L. Chen , J. Lingys , K. Chen , and F. Liu . 2018. Auto: Scaling deep reinforcement learning for datacenter-scale automatic traffic optimization . In Proc. of SIGCOM. L. Chen, J. Lingys, K. Chen, and F. Liu. 2018. Auto: Scaling deep reinforcement learning for datacenter-scale automatic traffic optimization. In Proc. of SIGCOM."},{"volume-title":"Proc. of INFOCOM.","author":"Cui J. E, Y.","key":"e_1_3_2_1_13_1","unstructured":"J. E, Y. Cui , M. Ruan , Z. Li , and E. Zhai . 2019. HyCloud: Tweaking Hybrid Cloud Storage Services for Cost-Efficient Filesystem Hosting . In Proc. of INFOCOM. J. E, Y. Cui, M. Ruan, Z. Li, and E. Zhai. 2019. HyCloud: Tweaking Hybrid Cloud Storage Services for Cost-Efficient Filesystem Hosting. In Proc. of INFOCOM."},{"volume-title":"Proc. of IPDPS.","author":"Gao J.","key":"e_1_3_2_1_14_1","unstructured":"J. Gao , H. Wang , and H. Shen . 2020. Smartly Handling Renewable Energy Instability in Supporting A Cloud Datacenter . In Proc. of IPDPS. J. Gao, H. Wang, and H. Shen. 2020. Smartly Handling Renewable Energy Instability in Supporting A Cloud Datacenter. In Proc. of IPDPS."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"S. Hillmer and G. Tiao. 1982. An ARIMA-model-based approach to seasonal adjustment. J. Amer. Statist. Assoc.(1982).  S. Hillmer and G. Tiao. 1982. An ARIMA-model-based approach to seasonal adjustment. J. Amer. Statist. Assoc.(1982).","DOI":"10.1080\/01621459.1982.10477767"},{"key":"e_1_3_2_1_16_1","unstructured":"R. Howard. 1964. Dynamic programming and Markov processes. (1964).  R. Howard. 1964. Dynamic programming and Markov processes. (1964)."},{"volume-title":"Proc. of INFOCOM.","author":"Jin H.","key":"e_1_3_2_1_17_1","unstructured":"H. Jin , H. Guo , L. Su , K. Nahrstedt , and X. Wang . 2019. Dynamic Task Pricing in Multi-Requester Mobile Crowd Sensing with Markov Correlated Equilibrium . In Proc. of INFOCOM. H. Jin, H. Guo, L. Su, K. Nahrstedt, and X. Wang. 2019. Dynamic Task Pricing in Multi-Requester Mobile Crowd Sensing with Markov Correlated Equilibrium. In Proc. of INFOCOM."},{"volume-title":"Proc. of USENIX ATC.","author":"Ana","key":"e_1_3_2_1_18_1","unstructured":"Ana K., Heiner L., and Christos K . 2018. Selecta: heterogeneous cloud storage configuration for data analytics . In Proc. of USENIX ATC. Ana K., Heiner L., and Christos K.2018. Selecta: heterogeneous cloud storage configuration for data analytics. In Proc. of USENIX ATC."},{"key":"e_1_3_2_1_19_1","unstructured":"Leslieb K. and Andrew L. Michaeland\u00a0M.1996. Reinforcement learning: A survey. Journal of artificial intelligence research(1996).  Leslieb K. and Andrew L. Michaeland\u00a0M.1996. Reinforcement learning: A survey. Journal of artificial intelligence research(1996)."},{"volume-title":"Proc. of ATC.","author":"Kotla R.","key":"e_1_3_2_1_20_1","unstructured":"R. Kotla , L. Alvisi , and M. Dahlin . 2007. SafeStore: A durable and practical storage system . In Proc. of ATC. R. Kotla, L. Alvisi, and M. Dahlin. 2007. SafeStore: A durable and practical storage system. In Proc. of ATC."},{"volume-title":"Proc. of CLOUD.","author":"Yang","key":"e_1_3_2_1_21_1","unstructured":"Yang L., Li G., Akara S., and Yike G . 2014. Enabling performance as a service for a cloud storage system . In Proc. of CLOUD. Yang L., Li G., Akara S., and Yike G.2014. Enabling performance as a service for a cloud storage system. In Proc. of CLOUD."},{"volume-title":"Proc. of Cloud.","author":"Li H.","key":"e_1_3_2_1_22_1","unstructured":"H. Li , L. Zhong , J. Liu , B. Li , and K. Xu . 2011. Cost-effective partial migration of VoD services to content clouds . In Proc. of Cloud. H. Li, L. Zhong, J. Liu, B. Li, and K. Xu. 2011. Cost-effective partial migration of VoD services to content clouds. In Proc. of Cloud."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"M. Li C. Qin J. Li and P. Lee. 2016. CDStore: Toward reliable secure and cost-efficient cloud storage via convergent dispersal. Prof. of ATC (2016).  M. Li C. Qin J. Li and P. Lee. 2016. CDStore: Toward reliable secure and cost-efficient cloud storage via convergent dispersal. Prof. of ATC (2016).","DOI":"10.1109\/MIC.2016.45"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"G. Liu H. Shen and H. Wang. 2017. An economical and SLO-guaranteed cloud storage service across multiple cloud service providers. Trans. on TPDS (2017).  G. Liu H. Shen and H. Wang. 2017. An economical and SLO-guaranteed cloud storage service across multiple cloud service providers. Trans. on TPDS (2017).","DOI":"10.1109\/INFOCOM.2016.7524629"},{"volume-title":"Proc. of FAST.","author":"Madhyastha H.","key":"e_1_3_2_1_25_1","unstructured":"H. Madhyastha , J. McCullough , G. Porter , R. Kapoor , S. Savage , A. Snoeren , and A. Vahdat . 2012. scc: cluster storage provisioning informed by application characteristics and SLAs .. In Proc. of FAST. H. Madhyastha, J. McCullough, G. Porter, R. Kapoor, S. Savage, A. Snoeren, and A. Vahdat. 2012. scc: cluster storage provisioning informed by application characteristics and SLAs.. In Proc. of FAST."},{"volume-title":"Proc. of HotNet.","author":"Mao H.","key":"e_1_3_2_1_26_1","unstructured":"H. Mao , M. Alizadeh , I. Menache , and S. Kandula . 2016. Resource management with deep reinforcement learning . In Proc. of HotNet. H. Mao, M. Alizadeh, I. Menache, and S. Kandula. 2016. Resource management with deep reinforcement learning. In Proc. of HotNet."},{"volume-title":"Proc. of SIGCOM.","author":"Mao H.","key":"e_1_3_2_1_27_1","unstructured":"H. Mao , R. Netravali , and M. Alizadeh . 2017. Neural adaptive video streaming with pensieve . In Proc. of SIGCOM. H. Mao, R. Netravali, and M. Alizadeh. 2017. Neural adaptive video streaming with pensieve. In Proc. of SIGCOM."},{"volume-title":"Proc. of INFOCOM.","author":"Mao W.","key":"e_1_3_2_1_28_1","unstructured":"W. Mao , Z. Zheng , and F. Wu . 2019. Pricing for revenue maximization in iot data markets: An information design perspective . In Proc. of INFOCOM. W. Mao, Z. Zheng, and F. Wu. 2019. Pricing for revenue maximization in iot data markets: An information design perspective. In Proc. of INFOCOM."},{"volume-title":"International conference on machine learning.","author":"Mnih V.","key":"e_1_3_2_1_29_1","unstructured":"V. Mnih , P. Badia , M. Mirza , A. Graves , T. Lillicrap , T. Harley , D. Silver , and K. Kavukcuoglu . 2016. Asynchronous methods for deep reinforcement learning . In International conference on machine learning. V. Mnih, P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. 2016. Asynchronous methods for deep reinforcement learning. In International conference on machine learning."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"V. Mnih K. Kavukcuoglu D. Silver A. Rusu J. Veness M. Bellemare A. Graves M. Riedmiller A. Fidjeland and G. Ostrovski. 2015. Human-level control through deep reinforcement learning. Nature (2015).  V. Mnih K. Kavukcuoglu D. Silver A. Rusu J. Veness M. Bellemare A. Graves M. Riedmiller A. Fidjeland and G. Ostrovski. 2015. Human-level control through deep reinforcement learning. Nature (2015).","DOI":"10.1038\/nature14236"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2012.6195785"},{"key":"e_1_3_2_1_32_1","volume-title":"Google Analytics for measuring website performance. Tourism Management","author":"Plaza B.","year":"2011","unstructured":"B. Plaza . 2011. Google Analytics for measuring website performance. Tourism Management ( 2011 ). B. Plaza. 2011. Google Analytics for measuring website performance. Tourism Management (2011)."},{"volume-title":"Proc. of INFOCOM workshop.","author":"Pooranian Z.","key":"e_1_3_2_1_33_1","unstructured":"Z. Pooranian , K. Chen , C. Yu , and M. Conti . 2018. RARE: Defeating side channels based on data-deduplication in cloud storage . In Proc. of INFOCOM workshop. Z. Pooranian, K. Chen, C. Yu, and M. Conti. 2018. RARE: Defeating side channels based on data-deduplication in cloud storage. In Proc. of INFOCOM workshop."},{"volume-title":"Proc. of INFOCOM.","author":"Roh H.","key":"e_1_3_2_1_34_1","unstructured":"H. Roh , C. Jung , W. Lee , and D. Du . 2013. Resource pricing game in geo-distributed clouds . In Proc. of INFOCOM. H. Roh, C. Jung, W. Lee, and D. Du. 2013. Resource pricing game in geo-distributed clouds. In Proc. of INFOCOM."},{"key":"e_1_3_2_1_35_1","volume-title":"1999. Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. AI","author":"Richard","year":"1999","unstructured":"Richard S., Doina P., and Satinder S . 1999. Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. AI ( 1999 ). Richard S., Doina P., and Satinder S.1999. Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. AI (1999)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"D. Silver A. Huang C. Maddison A. Guez L. Sifre G. Van Den\u00a0Driessche J. Schrittwieser I. Antonoglou and V. Panneershelvam. 2016. Mastering the game of Go with deep neural networks and tree search. nature (2016).  D. Silver A. Huang C. Maddison A. Guez L. Sifre G. Van Den\u00a0Driessche J. Schrittwieser I. Antonoglou and V. Panneershelvam. 2016. Mastering the game of Go with deep neural networks and tree search. nature (2016).","DOI":"10.1038\/nature16961"},{"volume-title":"Proc. of INFOCOM.","author":"Song Y.","key":"e_1_3_2_1_37_1","unstructured":"Y. Song , M. Zafer , and K. Lee . 2012. Optimal bidding in spot instance market . In Proc. of INFOCOM. Y. Song, M. Zafer, and K. Lee. 2012. Optimal bidding in spot instance market. In Proc. of INFOCOM."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"R. Sutton A. Barto and F. Bach. 1998. Reinforcement learning: An introduction.  R. Sutton A. Barto and F. Bach. 1998. Reinforcement learning: An introduction.","DOI":"10.1109\/TNN.1998.712192"},{"volume-title":"Proc. of ANIPS.","author":"Sutton R.","key":"e_1_3_2_1_39_1","unstructured":"R. Sutton , D. McAllester , S. Singh , and Y. Mansour . 2000. Policy gradient methods for reinforcement learning with function approximation . In Proc. of ANIPS. R. Sutton, D. McAllester, S. Singh, and Y. Mansour. 2000. Policy gradient methods for reinforcement learning with function approximation. In Proc. of ANIPS."},{"volume-title":"Proc. of NSDI.","author":"Zhe","key":"e_1_3_2_1_40_1","unstructured":"Zhe W., Curtis Y., and Harsha\u00a0 V M.2015. CosTLO : Cost-Effective Redundancy for Lower Latency Variance on Cloud Storage Services .. In Proc. of NSDI. Zhe W., Curtis Y., and Harsha\u00a0V M.2015. CosTLO: Cost-Effective Redundancy for Lower Latency Variance on Cloud Storage Services.. In Proc. of NSDI."},{"volume-title":"Proc. of INFOCOM.","author":"Wang F.","key":"e_1_3_2_1_41_1","unstructured":"F. Wang , J. Liu , and M. Chen . 2012. CALMS: Cloud-assisted live media streaming for globalized demands with time\/region diversities . In Proc. of INFOCOM. F. Wang, J. Liu, and M. Chen. 2012. CALMS: Cloud-assisted live media streaming for globalized demands with time\/region diversities. In Proc. of INFOCOM."},{"volume-title":"Proc. of INFOCOM.","author":"Wang H.","key":"e_1_3_2_1_42_1","unstructured":"H. Wang and H. Shen . 2018. Proactive incast congestion control in a datacenter serving web applications . In Proc. of INFOCOM. H. Wang and H. Shen. 2018. Proactive incast congestion control in a datacenter serving web applications. In Proc. of INFOCOM."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"B. Wickremasinghe and R. Buyya. 2009. CloudAnalyst: A CloudSim-based tool for modelling and analysis of large scale cloud computing environments. Prof. of MEDC (2009).  B. Wickremasinghe and R. Buyya. 2009. CloudAnalyst: A CloudSim-based tool for modelling and analysis of large scale cloud computing environments. Prof. of MEDC (2009).","DOI":"10.1109\/AINA.2010.32"},{"volume-title":"Proc. of NSDI.","author":"Wieder A.","key":"e_1_3_2_1_44_1","unstructured":"A. Wieder , P. Bhatotia , A. Post , and R. Rodrigues . 2012. Orchestrating the Deployment of Computations in the Cloud with Conductor .. In Proc. of NSDI. A. Wieder, P. Bhatotia, A. Post, and R. Rodrigues. 2012. Orchestrating the Deployment of Computations in the Cloud with Conductor.. In Proc. of NSDI."},{"volume-title":"Proc. of SOSP.","author":"Wu Z.","key":"e_1_3_2_1_45_1","unstructured":"Z. Wu , M. Butkiewicz , D. Perkins , E. Katz-Bassett , and H. Madhyastha . 2013. Spanstore: Cost-effective geo-replicated storage spanning multiple cloud services . In Proc. of SOSP. Z. Wu, M. Butkiewicz, D. Perkins, E. Katz-Bassett, and H. Madhyastha. 2013. Spanstore: Cost-effective geo-replicated storage spanning multiple cloud services. In Proc. of SOSP."}],"event":{"name":"ICPP '20: 49th International Conference on Parallel Processing","acronym":"ICPP '20","location":"Edmonton AB Canada"},"container-title":["49th International Conference on Parallel Processing - ICPP"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3404397.3404466","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3404397.3404466","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3404397.3404466","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:31:43Z","timestamp":1750195903000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3404397.3404466"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,17]]},"references-count":45,"alternative-id":["10.1145\/3404397.3404466","10.1145\/3404397"],"URL":"https:\/\/doi.org\/10.1145\/3404397.3404466","relation":{},"subject":[],"published":{"date-parts":[[2020,8,17]]},"assertion":[{"value":"2020-08-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}