{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T22:32:29Z","timestamp":1759530749333,"version":"3.41.0"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2020,12,7]],"date-time":"2020-12-07T00:00:00Z","timestamp":1607299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior - Brasil","doi-asserted-by":"crossref","award":["001"],"award-info":[{"award-number":["001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2021,2,28]]},"abstract":"<jats:p>Internet of Things (IoT) systems usually involve interconnected, low processing capacity, and low memory sensor nodes (devices) that collect data in several sorts of applications that interconnect people and things. In this scenario, mining tasks, such as clustering, have been commonly deployed to detect behavioral patterns from the collected data. The centralized clustering of IoT data demands high network traffic to transmit the data from the devices to a central node, where a clustering algorithm must be applied. This approach does not scale as the number of devices increases, and the amount of data grows. However, distributing the clustering process through the devices may not be a feasible approach as well, since the devices are usually simple and may not have the ability to execute complex procedures. This work proposes a centralized IoT data clustering method that demands reduced network traffic and low processing power in the devices. The proposed method uses a data grid to summarize the information at the devices before transmitting it to the central node, reducing network traffic. After the data transfer, the proposed method applies a clustering algorithm that was developed to process data in the summarized representation. Tests with seven datasets provided experimental evidence that the proposed method reduces network traffic and produces results comparable to the ones generated by DBSCAN and HDBSCAN, two robust centralized clustering algorithms.<\/jats:p>","DOI":"10.1145\/3423139","type":"journal-article","created":{"date-parts":[[2020,12,7]],"date-time":"2020-12-07T19:04:16Z","timestamp":1607367856000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["A Reduced Network Traffic Method for IoT Data Clustering"],"prefix":"10.1145","volume":"15","author":[{"given":"Ricardo De","family":"Azevedo","sequence":"first","affiliation":[{"name":"Brand\u00e3o, Rio de Janeiro, RJ, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabriel Resende","family":"Machado","sequence":"additional","affiliation":[{"name":"Military Institute of Engineering, Rio de Janeiro, RJ, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronaldo Ribeiro","family":"Goldschmidt","sequence":"additional","affiliation":[{"name":"Military Institute of Engineering, Rio de Janeiro, RJ, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ricardo","family":"Choren","sequence":"additional","affiliation":[{"name":"Military Institute of Engineering, Rio de Janeiro, RJ, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,12,7]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Retrieved","author":"Abdelshkour M.","year":"2015","unstructured":"M. Abdelshkour . 2015 . IoT, from Cloud to Fog Computing . Retrieved April 12, 2019 from https:\/\/tinyurl.com\/ydalpr5s. M. Abdelshkour. 2015. IoT, from Cloud to Fog Computing. Retrieved April 12, 2019 from https:\/\/tinyurl.com\/ydalpr5s."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.08.220"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1389-1286(01)00302-4"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.3844\/jcssp.2006.735.739"},{"volume-title":"Proceedings of the 2002 International Conference on Information Quality. MIT, Massachusetts, 47--60","author":"Arndt D.","key":"e_1_2_1_5_1","unstructured":"D. Arndt and N. Langbein . 2002. Data quality in the context of customer segmentation . In Proceedings of the 2002 International Conference on Information Quality. MIT, Massachusetts, 47--60 . D. Arndt and N. Langbein. 2002. Data quality in the context of customer segmentation. In Proceedings of the 2002 International Conference on Information Quality. MIT, Massachusetts, 47--60."},{"volume-title":"Proceedings of the 2015 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services. IEEE Computer Society","author":"Bendechache M.","key":"e_1_2_1_6_1","unstructured":"M. Bendechache and M. Kechadi . 2015. Distributed clustering algorithm for spatial data mining . In Proceedings of the 2015 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services. IEEE Computer Society , Los Alamitos, CA, 60--65. M. Bendechache and M. Kechadi. 2015. Distributed clustering algorithm for spatial data mining. In Proceedings of the 2015 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services. IEEE Computer Society, Los Alamitos, CA, 60--65."},{"volume-title":"Proceedings of the 2010 International Conference on Image Analysis and Signal Processing. IEEE Computer Society","author":"Bin S.","key":"e_1_2_1_7_1","unstructured":"S. Bin , L. Yuan , and W. Xiaoyiu . 2010. Research on data mining models for the internet of things . In Proceedings of the 2010 International Conference on Image Analysis and Signal Processing. IEEE Computer Society , Los Alamitos, CA, 127--132. S. Bin, L. Yuan, and W. Xiaoyiu. 2010. Research on data mining models for the internet of things. In Proceedings of the 2010 International Conference on Image Analysis and Signal Processing. IEEE Computer Society, Los Alamitos, CA, 127--132."},{"volume-title":"Proceedings of the 21st International Conference on Enterprise Information Systems. INSTICC, SciTePress, 563--570","author":"Brandao R.","key":"e_1_2_1_8_1","unstructured":"R. Brandao , R. Goldschmidt , and R. Choren . 2019. A data traffic reduction approach towards centralized mining in the IoT context . In Proceedings of the 21st International Conference on Enterprise Information Systems. INSTICC, SciTePress, 563--570 . DOI:https:\/\/doi.org\/10.5220\/0007674505630570 10.5220\/0007674505630570 R. Brandao, R. Goldschmidt, and R. Choren. 2019. A data traffic reduction approach towards centralized mining in the IoT context. In Proceedings of the 21st International Conference on Enterprise Information Systems. INSTICC, SciTePress, 563--570. DOI:https:\/\/doi.org\/10.5220\/0007674505630570"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CCGRID.2019.00075"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-37456-2_14"},{"volume-title":"Retrieved","year":"2017","key":"e_1_2_1_11_1","unstructured":"Cisco. 2017 . Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016--2021 White Paper . Retrieved January 31, 2019 from https:\/\/tinyurl.com\/y8kuucvk. Cisco. 2017. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016--2021 White Paper. Retrieved January 31, 2019 from https:\/\/tinyurl.com\/y8kuucvk."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2840129"},{"volume-title":"Proceedings of the International Conference on Knowledge Discovery and Data Mining. ACM","author":"Ester M.","key":"e_1_2_1_13_1","unstructured":"M. Ester , H.-P. Kriegel , J. Sander , and X. Xu . 1996. A density-based algorithm for discovering clusters in large spatial databases with noise . In Proceedings of the International Conference on Knowledge Discovery and Data Mining. ACM , New York, NY, 226--231. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, 226--231."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1983.10478008"},{"key":"e_1_2_1_15_1","volume-title":"Philippe Fournier-Viger, Han-Chieh Chao, and Philip S. Yu.","author":"Gan Wensheng","year":"2019","unstructured":"Wensheng Gan , Jerry Chun-Wei Lin , Philippe Fournier-Viger, Han-Chieh Chao, and Philip S. Yu. 2019 . A survey of parallel sequential pattern mining. ACM Transactions on Knowledge Discovery from Data 13, 3 (June 2019), 34. DOI:https:\/\/doi.org\/10.1145\/3314107 10.1145\/3314107 Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, Han-Chieh Chao, and Philip S. Yu. 2019. A survey of parallel sequential pattern mining. ACM Transactions on Knowledge Discovery from Data 13, 3 (June 2019), 34. DOI:https:\/\/doi.org\/10.1145\/3314107"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2935224"},{"key":"e_1_2_1_17_1","first-page":"1556","article-title":"Clustering aggregation","volume":"1","author":"Gionis A.","year":"2007","unstructured":"A. Gionis , H. Mannila , and P. Tsaparas . 2007 . Clustering aggregation . ACM Transactions on Knowledge Discovery Data 1 , 1 (2007), 1556 -- 4681 . A. Gionis, H. Mannila, and P. Tsaparas. 2007. Clustering aggregation. ACM Transactions on Knowledge Discovery Data 1, 1 (2007), 1556--4681.","journal-title":"ACM Transactions on Knowledge Discovery Data"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2013.01.010"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2020.106630"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.07.077"},{"key":"e_1_2_1_21_1","volume-title":"DBDC: Density based distributed clustering. In Proceedings of the Advances in Database Technology (EDBT\u201904)","author":"Januzaj E.","year":"2004","unstructured":"E. Januzaj , H.-P. Kriegel , and M. Pfeifle . 2004 . DBDC: Density based distributed clustering. In Proceedings of the Advances in Database Technology (EDBT\u201904) . Springer , Berlin . 88--105. E. Januzaj, H.-P. Kriegel, and M. Pfeifle. 2004. DBDC: Density based distributed clustering. In Proceedings of the Advances in Database Technology (EDBT\u201904). Springer, Berlin. 88--105."},{"key":"e_1_2_1_22_1","first-page":"2455","article-title":"Challenges and data mining model for IoT","volume":"1","author":"Joshi Divya","year":"2016","unstructured":"Divya Joshi , Chanchal Kumari , and Abhishek Srivastava . 2016 . Challenges and data mining model for IoT . International Journal of Engineering Applied Sciences and Technology 1 , 3 (2016), 2455 -- 2143 . Divya Joshi, Chanchal Kumari, and Abhishek Srivastava. 2016. Challenges and data mining model for IoT. International Journal of Engineering Applied Sciences and Technology 1, 3 (2016), 2455--2143.","journal-title":"International Journal of Engineering Applied Sciences and Technology"},{"key":"e_1_2_1_23_1","volume-title":"Retrieved","author":"Karypis G.","year":"2015","unstructured":"G. Karypis . 2015 . CLUTO - Software for Clustering High-Dimensional Datasets . Retrieved April 12, 2019 from https:\/\/tinyurl.com\/pxkr8yl. G. Karypis. 2015. CLUTO - Software for Clustering High-Dimensional Datasets. Retrieved April 12, 2019 from https:\/\/tinyurl.com\/pxkr8yl."},{"volume-title":"Proceedings of the 2016 5th Mediterranean Conference on Embedded Computing (MECO\u201916)","author":"Kholod I.","key":"e_1_2_1_24_1","unstructured":"I. Kholod , M. Kuprianov , and I. Petukhov . 2016. Distributed data mining based on actors for Internet of Things . In Proceedings of the 2016 5th Mediterranean Conference on Embedded Computing (MECO\u201916) . IEEE Computer Society, Los Alamitos, CA, 480--484. I. Kholod, M. Kuprianov, and I. Petukhov. 2016. Distributed data mining based on actors for Internet of Things. In Proceedings of the 2016 5th Mediterranean Conference on Embedded Computing (MECO\u201916). IEEE Computer Society, Los Alamitos, CA, 480--484."},{"volume-title":"Proceedings of the 2003 International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS\u201903)","author":"Klusch M.","key":"e_1_2_1_25_1","unstructured":"M. Klusch , S. Lodi , and G. Moro . 2003. Issues of agent-based distributed data mining . In Proceedings of the 2003 International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS\u201903) . ACM, New York, NY, 1034--1035. M. Klusch, S. Lodi, and G. Moro. 2003. Issues of agent-based distributed data mining. In Proceedings of the 2003 International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS\u201903). ACM, New York, NY, 1034--1035."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2391123"},{"volume-title":"Philosophiae Naturalis Principia Mathematica","author":"Newton Isaac","key":"e_1_2_1_27_1","unstructured":"Isaac Newton . 1687. Philosophiae Naturalis Principia Mathematica . Vol. 1 . S. Pepys Reg.Soc. Pr\u00c6sis, London . Isaac Newton. 1687. Philosophiae Naturalis Principia Mathematica. Vol. 1. S. Pepys Reg.Soc. Pr\u00c6sis, London."},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the 2017 IEEE International Conference on Big Data (Big Data\u201917)","author":"Onal A. C.","year":"2037","unstructured":"A. C. Onal , O. Berat Sezer , M. Ozbayoglu , and E. Dogdu . 2017. Weather data analysis and sensor fault detection using an extended IoT framework with semantics, big data, and machine learning . In Proceedings of the 2017 IEEE International Conference on Big Data (Big Data\u201917) . IEEE Computer Society, Los Alamitos, CA , 2037 --2046. A. C. Onal, O. Berat Sezer, M. Ozbayoglu, and E. Dogdu. 2017. Weather data analysis and sensor fault detection using an extended IoT framework with semantics, big data, and machine learning. In Proceedings of the 2017 IEEE International Conference on Big Data (Big Data\u201917). IEEE Computer Society, Los Alamitos, CA, 2037--2046."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2825231"},{"volume-title":"Proceedings of the International Conference on Cyber Security Intelligence and Analytics. Zheng Xu, Kim-Kwang Raymond Choo","author":"Peng Haibo","key":"e_1_2_1_30_1","unstructured":"Haibo Peng , Qiaoshun Wu , Jie Li , and Rong Zhou . 2020. Design of multi-layer industrial internet of data mine network model based on edge computation . In Proceedings of the International Conference on Cyber Security Intelligence and Analytics. Zheng Xu, Kim-Kwang Raymond Choo , Ali Dehghantanha, Reza Parizi, and Mohammad Hammoudeh (Eds.). Springer International Publishing , Cham , 1034--1040. Haibo Peng, Qiaoshun Wu, Jie Li, and Rong Zhou. 2020. Design of multi-layer industrial internet of data mine network model based on edge computation. In Proceedings of the International Conference on Cyber Security Intelligence and Analytics. Zheng Xu, Kim-Kwang Raymond Choo, Ali Dehghantanha, Reza Parizi, and Mohammad Hammoudeh (Eds.). Springer International Publishing, Cham, 1034--1040."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2618909"},{"volume-title":"Retrieved","year":"2015","key":"e_1_2_1_32_1","unstructured":"Quartz. 2015 . Connected Cars will Send 25 Gigabytes of Data to the Cloud Every Hour . Retrieved April 12, 2019 from https:\/\/qz.com\/344466\/. Quartz. 2015. Connected Cars will Send 25 Gigabytes of Data to the Cloud Every Hour. Retrieved April 12, 2019 from https:\/\/qz.com\/344466\/."},{"volume-title":"Proceedings of the 2016 Cloudification of the Internet of Things (CIoT\u201916)","author":"Rahman H.","key":"e_1_2_1_33_1","unstructured":"H. Rahman , N. Ahmed , and M. I. Hussain . 2016. A hybrid data aggregation scheme for provisioning Quality of Service (QoS) in Internet of Things (IoT) . In Proceedings of the 2016 Cloudification of the Internet of Things (CIoT\u201916) . IEEE Computer Society, Los Alamitos, CA, 1--5. H. Rahman, N. Ahmed, and M. I. Hussain. 2016. A hybrid data aggregation scheme for provisioning Quality of Service (QoS) in Internet of Things (IoT). In Proceedings of the 2016 Cloudification of the Internet of Things (CIoT\u201916). IEEE Computer Society, Los Alamitos, CA, 1--5."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2974035"},{"volume-title":"Proceedings of the International Conference on Big Data Analytics and Knowledge Discovery (DaWaK\u201916)","author":"Roriz M.","key":"e_1_2_1_35_1","unstructured":"M. Roriz , M. Endler , M. A. Casanova , H. Lopes , F. S. Silva , and T. Hara . 2016. A heuristic approach for on-line discovery of unidentified spatial clusters from grid-based streaming algorithms . In Proceedings of the International Conference on Big Data Analytics and Knowledge Discovery (DaWaK\u201916) . Springer, Berlin. 128--142. M. Roriz, M. Endler, M. A. Casanova, H. Lopes, F. S. Silva, and T. Hara. 2016. A heuristic approach for on-line discovery of unidentified spatial clusters from grid-based streaming algorithms. In Proceedings of the International Conference on Big Data Analytics and Knowledge Discovery (DaWaK\u201916). Springer, Berlin. 128--142."},{"volume-title":"Proceedings of the IEEE International Conference on Tools with Artificial Intelligence. IEEE Computer Society","author":"Salvador S.","key":"e_1_2_1_36_1","unstructured":"S. Salvador and P. Chan . 2004. Determining the number of clusters\/segments in hierarchical clustering\/segmentation algorithms . In Proceedings of the IEEE International Conference on Tools with Artificial Intelligence. IEEE Computer Society , Los Alamitos, CA, 576--584. S. Salvador and P. Chan. 2004. Determining the number of clusters\/segments in hierarchical clustering\/segmentation algorithms. In Proceedings of the IEEE International Conference on Tools with Artificial Intelligence. IEEE Computer Society, Los Alamitos, CA, 576--584."},{"volume-title":"Proceedings of the 2019 28th International Conference on Computer Communication and Networks (ICCCN\u201919)","author":"Savaglio C.","key":"e_1_2_1_37_1","unstructured":"C. Savaglio , P. Gerace , G. Di Fatta , and G. Fortino . 2019. Data mining at the IoT edge . In Proceedings of the 2019 28th International Conference on Computer Communication and Networks (ICCCN\u201919) . IEEE Computer Society, Los Alamitos, CA, 1--6. C. Savaglio, P. Gerace, G. Di Fatta, and G. Fortino. 2019. Data mining at the IoT edge. In Proceedings of the 2019 28th International Conference on Computer Communication and Networks (ICCCN\u201919). IEEE Computer Society, Los Alamitos, CA, 1--6."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2579198"},{"volume-title":"Proceedings of the 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC\u201917)","author":"Singh A.","key":"e_1_2_1_39_1","unstructured":"A. Singh and S. Sharma . 2017. Analysis on data mining models for Internet Of Things . In Proceedings of the 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC\u201917) . IEEE Computer Society, Los Alamitos, CA, 94--100. A. Singh and S. Sharma. 2017. Analysis on data mining models for Internet Of Things. In Proceedings of the 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC\u201917). IEEE Computer Society, Los Alamitos, CA, 94--100."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/SURV.2013.103013.00206"},{"key":"e_1_2_1_41_1","volume-title":"Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann","author":"Witten I.","year":"2017","unstructured":"I. Witten , E. Frank , M. Hall , and C. Pal . 2017 . Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann , San Francisco . I. Witten, E. Frank, M. Hall, and C. Pal. 2017. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco."},{"key":"#cr-split#-e_1_2_1_42_1.1","doi-asserted-by":"crossref","unstructured":"H. Yu H. Chen S. Zhao and Q. Shi. 2020. Distributed soft clustering algorithm for IoT based on finite time average consensus. IEEE Internet of Things Journal (2020) 1--1. DOI:10.1109\/JIOT.2020.2981774 10.1109\/JIOT.2020.2981774","DOI":"10.1109\/JIOT.2020.2981774"},{"key":"#cr-split#-e_1_2_1_42_1.2","doi-asserted-by":"crossref","unstructured":"H. Yu H. Chen S. Zhao and Q. Shi. 2020. Distributed soft clustering algorithm for IoT based on finite time average consensus. IEEE Internet of Things Journal (2020) 1--1. DOI:10.1109\/JIOT.2020.2981774","DOI":"10.1109\/JIOT.2020.2981774"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2684807"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2014.6863132"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3423139","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3423139","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:24:56Z","timestamp":1750195496000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3423139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,7]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,2,28]]}},"alternative-id":["10.1145\/3423139"],"URL":"https:\/\/doi.org\/10.1145\/3423139","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2020,12,7]]},"assertion":[{"value":"2020-01-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-09-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-12-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}