{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T18:44:10Z","timestamp":1763923450785,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,8,23]]},"DOI":"10.1145\/3394486.3403355","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T23:03:59Z","timestamp":1597964639000},"page":"3044-3053","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Balanced Order Batching with Task-Oriented Graph Clustering"],"prefix":"10.1145","author":[{"given":"Lu","family":"Duan","sequence":"first","affiliation":[{"name":"Zhejiang Cainiao Supply Chain Management Co., Ltd, HangZhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoyuan","family":"Hu","sequence":"additional","affiliation":[{"name":"Zhejiang Cainiao Supply Chain Management Co. Ltd, HangZhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zili","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang Cainiao Supply Chain Management Co. Ltd, HangZhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guozheng","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang Cainiao Supply Chain Management Co. Ltd, HangZhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinhang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang Cainiao Supply Chain Management Co. Ltd, HangZhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Gong","sequence":"additional","affiliation":[{"name":"Alibaba Group, HangZhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinghui","family":"Xu","sequence":"additional","affiliation":[{"name":"Zhejiang Cainiao Supply Chain Management Co., Ltd, HangZhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM.2018.8508326"},{"key":"e_1_3_2_1_2_1","volume-title":"Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940","author":"Bello Irwan","year":"2016","unstructured":"Irwan Bello , Hieu Pham , Quoc V Le , Mohammad Norouzi , and Samy Bengio . 2016. Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940 ( 2016 ). Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio. 2016. Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940 (2016)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065797000422"},{"key":"e_1_3_2_1_4_1","volume-title":"Link prediction in criminal networks: A tool for criminal intelligence analysis. PloS one","author":"Berlusconi Giulia","year":"2016","unstructured":"Giulia Berlusconi , Francesco Calderoni , Nicola Parolini , Marco Verani , and Carlo Piccardi . 2016. Link prediction in criminal networks: A tool for criminal intelligence analysis. PloS one , Vol. 11 , 4 ( 2016 ). Giulia Berlusconi, Francesco Calderoni, Nicola Parolini, Marco Verani, and Carlo Piccardi. 2016. Link prediction in criminal networks: A tool for criminal intelligence analysis. PloS one, Vol. 11, 4 (2016)."},{"key":"e_1_3_2_1_5_1","volume-title":"Link-prediction enhanced consensus clustering for complex networks. PloS one","author":"Burgess Matthew","year":"2016","unstructured":"Matthew Burgess , Eytan Adar , and Michael Cafarella . 2016. Link-prediction enhanced consensus clustering for complex networks. PloS one , Vol. 11 , 5 ( 2016 ). Matthew Burgess, Eytan Adar, and Michael Cafarella. 2016. Link-prediction enhanced consensus clustering for complex networks. PloS one, Vol. 11, 5 (2016)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132925"},{"key":"e_1_3_2_1_7_1","volume-title":"Task-Based Learning via Task-Oriented Prediction Network. arXiv preprint arXiv:1910.09357","author":"Chen Di","year":"2019","unstructured":"Di Chen , Yada Zhu , Xiaodong Cui , and Carla P Gomes . 2019. Task-Based Learning via Task-Oriented Prediction Network. arXiv preprint arXiv:1910.09357 ( 2019 ). Di Chen, Yada Zhu, Xiaodong Cui, and Carla P Gomes. 2019. Task-Based Learning via Task-Oriented Prediction Network. arXiv preprint arXiv:1910.09357 (2019)."},{"key":"e_1_3_2_1_8_1","unstructured":"Priya Donti Brandon Amos and J Zico Kolter. 2017. Task-based end-to-end model learning in stochastic optimization. In Advances in Neural Information Processing Systems. 5484--5494. Priya Donti Brandon Amos and J Zico Kolter. 2017. Task-based end-to-end model learning in stochastic optimization. In Advances in Neural Information Processing Systems. 5484--5494."},{"key":"e_1_3_2_1_9_1","volume-title":"Smart\" predict, then optimize\". arXiv preprint arXiv:1710.08005","author":"Elmachtoub Adam N","year":"2017","unstructured":"Adam N Elmachtoub and Paul Grigas . 2017. Smart\" predict, then optimize\". arXiv preprint arXiv:1710.08005 ( 2017 ). Adam N Elmachtoub and Paul Grigas. 2017. Smart\" predict, then optimize\". arXiv preprint arXiv:1710.08005 (2017)."},{"key":"e_1_3_2_1_10_1","volume-title":"MIPaaL: Mixed integer program as a layer. arXiv preprint arXiv:1907.05912","author":"Ferber Aaron","year":"2019","unstructured":"Aaron Ferber , Bryan Wilder , Bistra Dilina , and Milind Tambe . 2019. MIPaaL: Mixed integer program as a layer. arXiv preprint arXiv:1907.05912 ( 2019 ). Aaron Ferber, Bryan Wilder, Bistra Dilina, and Milind Tambe. 2019. MIPaaL: Mixed integer program as a layer. arXiv preprint arXiv:1907.05912 (2019)."},{"key":"e_1_3_2_1_11_1","volume-title":"Frazelle","author":"Frazelle Edward","year":"2002","unstructured":"Edward Frazelle and Ed Frazelle . 2002 . World-class warehousing and material handling. Vol. 1 . McGraw-Hill New York . Edward Frazelle and Ed Frazelle. 2002. World-class warehousing and material handling. Vol. 1. McGraw-Hill New York."},{"key":"e_1_3_2_1_12_1","volume-title":"Order batching to minimize total travel time in a parallel-aisle warehouse. IIE transactions","author":"Gademann Noud","year":"2005","unstructured":"Noud Gademann and Steef Velde . 2005. Order batching to minimize total travel time in a parallel-aisle warehouse. IIE transactions , Vol. 37 , 1 ( 2005 ), 63--75. Noud Gademann and Steef Velde. 2005. Order batching to minimize total travel time in a parallel-aisle warehouse. IIE transactions, Vol. 37, 1 (2005), 63--75."},{"key":"e_1_3_2_1_13_1","volume-title":"Deep clustering with concrete k-means. arXiv preprint arXiv:1910.08031","author":"Gao Boyan","year":"2019","unstructured":"Boyan Gao , Yongxin Yang , Henry Gouk , and Timothy M Hospedales . 2019. Deep clustering with concrete k-means. arXiv preprint arXiv:1910.08031 ( 2019 ). Boyan Gao, Yongxin Yang, Henry Gouk, and Timothy M Hospedales. 2019. Deep clustering with concrete k-means. arXiv preprint arXiv:1910.08031 (2019)."},{"key":"e_1_3_2_1_14_1","volume-title":"Differentiable Deep Clustering with Cluster Size Constraints. arXiv preprint arXiv:1910.09036","author":"Genevay Aude","year":"2019","unstructured":"Aude Genevay , Gabriel Dulac-Arnold , and Jean-Philippe Vert . 2019. Differentiable Deep Clustering with Cluster Size Constraints. arXiv preprint arXiv:1910.09036 ( 2019 ). Aude Genevay, Gabriel Dulac-Arnold, and Jean-Philippe Vert. 2019. Differentiable Deep Clustering with Cluster Size Constraints. arXiv preprint arXiv:1910.09036 (2019)."},{"key":"e_1_3_2_1_15_1","unstructured":"Google. 2019. OR-Tools. https:\/\/developers.google.com\/optimization Google. 2019. OR-Tools. https:\/\/developers.google.com\/optimization"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Xifeng Guo Long Gao Xinwang Liu and Jianping Yin. 2017. Improved deep embedded clustering with local structure preservation.. In IJCAI. 1753--1759. Xifeng Guo Long Gao Xinwang Liu and Jianping Yin. 2017. Improved deep embedded clustering with local structure preservation.. In IJCAI. 1753--1759.","DOI":"10.24963\/ijcai.2017\/243"},{"key":"e_1_3_2_1_17_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in neural information processing systems. 1024--1034. Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in neural information processing systems. 1024--1034."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF03342717"},{"key":"e_1_3_2_1_19_1","volume-title":"A fast learning algorithm for deep belief nets. Neural computation","author":"Hinton Geoffrey E","year":"2006","unstructured":"Geoffrey E Hinton , Simon Osindero , and Yee-Whye Teh . 2006. A fast learning algorithm for deep belief nets. Neural computation , Vol. 18 , 7 ( 2006 ), 1527--1554. Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh. 2006. A fast learning algorithm for deep belief nets. Neural computation, Vol. 18, 7 (2006), 1527--1554."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2007.12.018"},{"key":"e_1_3_2_1_21_1","volume-title":"Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991","author":"Huang Zhiheng","year":"2015","unstructured":"Zhiheng Huang , Wei Xu , and Kai Yu. 2015. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 ( 2015 ). Zhiheng Huang, Wei Xu, and Kai Yu. 2015. Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)."},{"key":"e_1_3_2_1_22_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_23_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 ( 2016 ). Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_24_1","volume-title":"Herke Van Hoof, and Max Welling","author":"Kool Wouter","year":"2018","unstructured":"Wouter Kool , Herke Van Hoof, and Max Welling . 2018 . Attention , learn to solve routing problems! arXiv preprint arXiv:1803.08475 (2018). Wouter Kool, Herke Van Hoof, and Max Welling. 2018. Attention, learn to solve routing problems! arXiv preprint arXiv:1803.08475 (2018)."},{"key":"e_1_3_2_1_25_1","volume-title":"Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971","author":"Lillicrap Timothy P","year":"2015","unstructured":"Timothy P Lillicrap , Jonathan J Hunt , Alexander Pritzel , Nicolas Heess , Tom Erez , Yuval Tassa , David Silver , and Daan Wierstra . 2015. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 ( 2015 ). Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3272010"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-44415-3_4"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104425"},{"key":"e_1_3_2_1_29_1","volume-title":"The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621","author":"Perez Luis","year":"2017","unstructured":"Luis Perez and Jason Wang . 2017. The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621 ( 2017 ). Luis Perez and Jason Wang. 2017. The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621 (2017)."},{"key":"e_1_3_2_1_30_1","volume-title":"Decision-focused learning of adversary behavior in security games. arXiv preprint arXiv:1903.00958","author":"Perrault Andrew","year":"2019","unstructured":"Andrew Perrault , Bryan Wilder , Eric Ewing , Aditya Mate , Bistra Dilkina , and Milind Tambe . 2019. Decision-focused learning of adversary behavior in security games. arXiv preprint arXiv:1903.00958 ( 2019 ). Andrew Perrault, Bryan Wilder, Eric Ewing, Aditya Mate, Bistra Dilkina, and Milind Tambe. 2019. Decision-focused learning of adversary behavior in security games. arXiv preprint arXiv:1903.00958 (2019)."},{"key":"e_1_3_2_1_31_1","volume-title":"On the capacitated vehicle routing problem. Mathematical programming","author":"Ralphs Ted K","year":"2003","unstructured":"Ted K Ralphs , Leonid Kopman , William R Pulleyblank , and Leslie E Trotter . 2003. On the capacitated vehicle routing problem. Mathematical programming , Vol. 94 , 2--3 ( 2003 ), 343--359. Ted K Ralphs, Leonid Kopman, William R Pulleyblank, and Leslie E Trotter. 2003. On the capacitated vehicle routing problem. Mathematical programming, Vol. 94, 2--3 (2003), 343--359."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341161.3342890"},{"key":"e_1_3_2_1_33_1","volume-title":"Edge attention-based multi-relational graph convolutional networks. arXiv preprint arXiv:1802.04944","author":"Shang Chao","year":"2018","unstructured":"Chao Shang , Qinqing Liu , Ko-Shin Chen , Jiangwen Sun , Jin Lu , Jinfeng Yi , and Jinbo Bi. 2018. Edge attention-based multi-relational graph convolutional networks. arXiv preprint arXiv:1802.04944 ( 2018 ). Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi, and Jinbo Bi. 2018. Edge attention-based multi-relational graph convolutional networks. arXiv preprint arXiv:1802.04944 (2018)."},{"key":"e_1_3_2_1_34_1","volume-title":"Efficient network disintegration under incomplete information: the comic effect of link prediction. Scientific reports","author":"Tan Suo-Yi","year":"2016","unstructured":"Suo-Yi Tan , Jun Wu , Linyuan L\u00fc , Meng-Jun Li , and Xin Lu. 2016. Efficient network disintegration under incomplete information: the comic effect of link prediction. Scientific reports , Vol. 6 , 1 ( 2016 ), 1--9. Suo-Yi Tan, Jun Wu, Linyuan L\u00fc, Meng-Jun Li, and Xin Lu. 2016. Efficient network disintegration under incomplete information: the comic effect of link prediction. Scientific reports, Vol. 6, 1 (2016), 1--9."},{"volume-title":"Facilities planning","author":"Tompkins James A","key":"e_1_3_2_1_35_1","unstructured":"James A Tompkins , John A White , Yavuz A Bozer , and Jose Mario Aza na Tanchoco . 2010. Facilities planning . John Wiley & Sons . James A Tompkins, John A White, Yavuz A Bozer, and Jose Mario Aza na Tanchoco. 2010. Facilities planning. John Wiley & Sons."},{"key":"e_1_3_2_1_36_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998--6008. Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998--6008."},{"key":"e_1_3_2_1_37_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Petar Velivc","year":"2017","unstructured":"Petar Velivc kovi\u0107, Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Velivc kovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_38_1","unstructured":"Oriol Vinyals Meire Fortunato and Navdeep Jaitly. 2015. Pointer networks. In Advances in neural information processing systems. 2692--2700. Oriol Vinyals Meire Fortunato and Navdeep Jaitly. 2015. Pointer networks. In Advances in neural information processing systems. 2692--2700."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219869"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011658"},{"key":"e_1_3_2_1_41_1","unstructured":"Bryan Wilder Eric Ewing Bistra Dilkina and Milind Tambe. 2019 b. End to end learning and optimization on graphs. In Advances in Neural Information Processing Systems. 4674--4685. Bryan Wilder Eric Ewing Bistra Dilkina and Milind Tambe. 2019 b. End to end learning and optimization on graphs. In Advances in Neural Information Processing Systems. 4674--4685."},{"key":"e_1_3_2_1_42_1","volume-title":"International conference on machine learning. 478--487","author":"Xie Junyuan","year":"2016","unstructured":"Junyuan Xie , Ross Girshick , and Ali Farhadi . 2016 . Unsupervised deep embedding for clustering analysis . In International conference on machine learning. 478--487 . Junyuan Xie, Ross Girshick, and Ali Farhadi. 2016. Unsupervised deep embedding for clustering analysis. In International conference on machine learning. 478--487."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2012\/09\/P09008"},{"key":"e_1_3_2_1_44_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning-Volume 70","author":"Yang Bo","year":"2017","unstructured":"Bo Yang , Xiao Fu , Nicholas D Sidiropoulos , and Mingyi Hong . 2017 . Towards k-means-friendly spaces: Simultaneous deep learning and clustering . In Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 3861--3870. Bo Yang, Xiao Fu, Nicholas D Sidiropoulos, and Mingyi Hong. 2017. Towards k-means-friendly spaces: Simultaneous deep learning and clustering. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 3861--3870."},{"key":"e_1_3_2_1_45_1","volume-title":"A Framework for Deep Constrained Clustering-Algorithms and Advances. arXiv preprint arXiv:1901.10061","author":"Zhang Hongjing","year":"2019","unstructured":"Hongjing Zhang , Sugato Basu , and Ian Davidson . 2019. A Framework for Deep Constrained Clustering-Algorithms and Advances. arXiv preprint arXiv:1901.10061 ( 2019 ). Hongjing Zhang, Sugato Basu, and Ian Davidson. 2019. A Framework for Deep Constrained Clustering-Algorithms and Advances. arXiv preprint arXiv:1901.10061 (2019)."},{"key":"e_1_3_2_1_46_1","volume-title":"Relation Structure-Aware Heterogeneous Graph Neural Network. In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 1534--1539","author":"Zhu Shichao","year":"2019","unstructured":"Shichao Zhu , Chuan Zhou , Shirui Pan , Xingquan Zhu , and Bin Wang . 2019 . Relation Structure-Aware Heterogeneous Graph Neural Network. In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 1534--1539 . Shichao Zhu, Chuan Zhou, Shirui Pan, Xingquan Zhu, and Bin Wang. 2019. Relation Structure-Aware Heterogeneous Graph Neural Network. In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 1534--1539."}],"event":{"name":"KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Virtual Event CA USA","acronym":"KDD '20"},"container-title":["Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403355","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403355","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:31:29Z","timestamp":1750195889000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403355"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":46,"alternative-id":["10.1145\/3394486.3403355","10.1145\/3394486"],"URL":"https:\/\/doi.org\/10.1145\/3394486.3403355","relation":{},"subject":[],"published":{"date-parts":[[2020,8,20]]},"assertion":[{"value":"2020-08-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}