{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T11:38:40Z","timestamp":1761824320853,"version":"3.37.3"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T00:00:00Z","timestamp":1620086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T00:00:00Z","timestamp":1620086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Clustering algorithm analysis, including time and space complexity analysis, has always been discussed in the literature. The emergence of big data has also created a lot of challenges for this issue. Because of high complexity and execution time, traditional clustering techniques cannot be used for such an amount of data. This problem has been addressed in this research. To present the clustering algorithm using a bee colony algorithm and high-speed read\/write performance, Map-Reduce architecture is used. Using this architecture allows the proposed method to cluster any volume of data, and there is no limit to the amount of data. The presented algorithm has good performance and high precision. The simulation results on 3 datasets show that the presented algorithm is more efficient than other big data clustering methods. Also, the results of our algorithm execution time on huge datasets are much better than other big data clustering approaches.<\/jats:p>","DOI":"10.1186\/s40537-021-00450-w","type":"journal-article","created":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T20:03:15Z","timestamp":1620158595000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Big data fuzzy C-means algorithm based on bee colony optimization using an Apache Hbase"],"prefix":"10.1186","volume":"8","author":[{"given":"Seyyed Mohammad","family":"Razavi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2603-6066","authenticated-orcid":false,"given":"Mohsen","family":"Kahani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samad","family":"Paydar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,4]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Havens TC, Bezdek JC, Palaniswami M. Scalable single linkage hierarchical clustering for big data. In: 2013 IEEE eighth international conference on intelligent sensors, sensor networks and information processing. IEEE; 2013, pp. 396\u2013401.","key":"450_CR1","DOI":"10.1109\/ISSNIP.2013.6529823"},{"doi-asserted-by":"crossref","unstructured":"Cheng X, Dale C, Liu J. Statistics and social network of youtube videos. In: 2008 16th interntional workshop on quality of service. IEEE; 2008, pp. 229\u2013238.","key":"450_CR2","DOI":"10.1109\/IWQOS.2008.32"},{"issue":"2","key":"450_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/jdwm.2012040101","volume":"8","author":"V Priya","year":"2012","unstructured":"Priya V, Vadivel A. User behaviour pattern mining from weblog. Int J Data Warehousing Mining. 2012;8(2):1\u201322.","journal-title":"Int J Data Warehousing Mining"},{"issue":"2","key":"450_CR4","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.amc.2008.05.020","volume":"205","author":"D Taniar","year":"2008","unstructured":"Taniar D, Rahayu W, Lee V, Daly O. Exception rules in association rule mining. Appl Math Comput. 2008;205(2):735\u201350.","journal-title":"Appl Math Comput"},{"issue":"1","key":"450_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/jdwm.2012010101","volume":"8","author":"PK Williams","year":"2012","unstructured":"Williams PK, Soares CV, Gilbert JE. A clustering rule based approach for classification problems. Int J Data Warehousing Mining. 2012;8(1):1\u201323.","journal-title":"Int J Data Warehousing Mining"},{"issue":"1","key":"450_CR6","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.media.2004.07.002","volume":"9","author":"FG Meyer","year":"2005","unstructured":"Meyer FG, Chinrungrueng J. Spatiotemporal clustering of fmri time series in the spectral domain. Med Image Anal. 2005;9(1):51\u201368.","journal-title":"Med Image Anal"},{"issue":"Suppl 1","key":"450_CR7","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1093\/bioinformatics\/bti1022","volume":"21","author":"J Ernst","year":"2005","unstructured":"Ernst J, Nau GJ, Bar-Joseph Z. Clustering short time series gene expression data. Bioinformatics. 2005;21(Suppl 1):159\u201368.","journal-title":"Bioinformatics"},{"issue":"2","key":"450_CR8","doi-asserted-by":"publisher","first-page":"579","DOI":"10.3390\/en6020579","volume":"6","author":"F Iglesias","year":"2013","unstructured":"Iglesias F, Kastner W. Analysis of similarity measures in times series clustering for the discovery of building energy patterns. Energies. 2013;6(2):579\u201397.","journal-title":"Energies"},{"issue":"1","key":"450_CR9","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.csda.2006.02.008","volume":"51","author":"RJ Hathaway","year":"2006","unstructured":"Hathaway RJ, Bezdek JC. Extending fuzzy and probabilistic clustering to very large data sets. Comput Stat Data Anal. 2006;51(1):215\u201334.","journal-title":"Comput Stat Data Anal"},{"issue":"10","key":"450_CR10","first-page":"60","volume":"90","author":"A McAfee","year":"2012","unstructured":"McAfee A, Brynjolfsson E, Davenport TH, Patil D, Barton D. Big data: the management revolution. Harvard Bus Rev. 2012;90(10):60\u20138.","journal-title":"Harvard Bus Rev"},{"issue":"1","key":"450_CR11","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1145\/1629175.1629198","volume":"53","author":"J Dean","year":"2010","unstructured":"Dean J, Ghemawat S. Mapreduce: a flexible data processing tool. Commun ACM. 2010;53(1):72\u20137.","journal-title":"Commun ACM"},{"issue":"5","key":"450_CR12","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1109\/TKDE.2002.1033770","volume":"14","author":"RT Ng","year":"2002","unstructured":"Ng RT, Han J. Clarans: a method for clustering objects for spatial data mining. IEEE Trans Knowl Data Eng. 2002;14(5):1003\u201316.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"450_CR13","first-page":"45","volume":"2","author":"G-F Zhao","year":"2006","unstructured":"Zhao G-F, Qu G-Q. Analysis and implementation of clara algorithm on clustering. J Shandong Univ Technol. 2006;2:45\u20138.","journal-title":"J Shandong Univ Technol"},{"key":"450_CR14","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1002\/9780470316801.ch2","volume-title":"Partitioning around medoids (program pam). Finding groups in data: an introduction to cluster analysis","author":"L Kaufman","year":"1990","unstructured":"Kaufman L, Rousseeuw PJ. Partitioning around medoids (program pam). Finding groups in data: an introduction to cluster analysis, vol. 344. Hoboken: Wiley; 1990. p. 68\u2013125."},{"issue":"2","key":"450_CR15","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1145\/235968.233324","volume":"25","author":"T Zhang","year":"1996","unstructured":"Zhang T, Ramakrishnan R, Livny M. Birch: an efficient data clustering method for very large databases. ACM Sigmod Rec. 1996;25(2):103\u201314.","journal-title":"ACM Sigmod Rec"},{"issue":"2","key":"450_CR16","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1145\/276305.276312","volume":"27","author":"S Guha","year":"1998","unstructured":"Guha S, Rastogi R, Shim K. Cure: an efficient clustering algorithm for large databases. ACM Sigmod Rec. 1998;27(2):73\u201384.","journal-title":"ACM Sigmod Rec"},{"doi-asserted-by":"crossref","unstructured":"Foley T, Sugerman J. Kd-tree acceleration structures for a gpu raytracer. In: Proceedings of the ACM SIGGRAPH\/EUROGRAPHICS conference on graphics hardware; 2005, pp. 15\u201322.","key":"450_CR17","DOI":"10.1145\/1071866.1071869"},{"issue":"1","key":"450_CR18","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1049\/trit.2019.0048","volume":"5","author":"RM Alguliyev","year":"2020","unstructured":"Alguliyev RM, Aliguliyev RM, Sukhostat LV. Efficient algorithm for big data clustering on single machine. CAAI Trans Intell Technol. 2020;5(1):9\u201314.","journal-title":"CAAI Trans Intell Technol"},{"unstructured":"Fern XZ, Brodley CE. Random projection for high dimensional data clustering: a cluster ensemble approach. In: Proceedings of the 20th international conference on machine learning (ICML-03); 2003, pp. 186\u2013193.","key":"450_CR19"},{"unstructured":"Boutilier C, Goldszmidt M. Proceedings of the sixteenth conference on uncertainty in artificial intelligence (2000); 2013. arXiv preprint arXiv:1304.3842.","key":"450_CR20"},{"unstructured":"Boutsidis C, Zouzias A, Drineas P. Random projections for $$k$$-means clustering. In: Advances in neural information processing systems; 2010, pp. 298\u2013306.","key":"450_CR21"},{"doi-asserted-by":"crossref","unstructured":"Golub GH, Reinsch C. Singular value decomposition and least squares solutions. In: Linear algebra. Berlin: Springer; 1971, pp. 134\u2013151.","key":"450_CR22","DOI":"10.1007\/978-3-662-39778-7_10"},{"issue":"1","key":"450_CR23","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1137\/S0097539704442702","volume":"36","author":"P Drineas","year":"2006","unstructured":"Drineas P, Kannan R, Mahoney MW. Fast monte carlo algorithms for matrices iii: computing a compressed approximate matrix decomposition. SIAM J Comput. 2006;36(1):184\u2013206.","journal-title":"SIAM J Comput"},{"doi-asserted-by":"crossref","unstructured":"Sun J, Xie Y, Zhang H, Faloutsos C. Less is more: Compact matrix decomposition for large sparse graphs. In: Proceedings of the 2007 SIAM international conference on data mining. SIAM; 2007, pp. 366\u201377.","key":"450_CR24","DOI":"10.1137\/1.9781611972771.33"},{"issue":"2","key":"450_CR25","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1109\/TCBB.2007.70272","volume":"6","author":"V Olman","year":"2008","unstructured":"Olman V, Mao F, Wu H, Xu Y. Parallel clustering algorithm for large data sets with applications in bioinformatics. IEEE\/ACM Trans Comput Biol Bioinform. 2008;6(2):344\u201352.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"doi-asserted-by":"crossref","unstructured":"Januzaj E. Kriegel H-P, Pfeifle M. Dbdc: Density based distributed clustering. In: International conference on extending database technology. Springer; 2004, pp. 88\u2013105.","key":"450_CR26","DOI":"10.1007\/978-3-540-24741-8_7"},{"doi-asserted-by":"crossref","unstructured":"Aggarwal CC, Reddy C. An introduction to cluster analysis; 2013.","key":"450_CR27","DOI":"10.1007\/978-1-4614-6396-2_1"},{"issue":"3","key":"450_CR28","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1002\/widm.30","volume":"1","author":"H-P Kriegel","year":"2011","unstructured":"Kriegel H-P, Kr\u00f6ger P, Sander J, Zimek A. Density-based clustering. Wiley Interdiscipl Rev Data Mining Knowl Discov. 2011;1(3):231\u201340.","journal-title":"Wiley Interdiscipl Rev Data Mining Knowl Discov"},{"key":"450_CR29","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.procs.2013.05.200","volume":"18","author":"G Andrade","year":"2013","unstructured":"Andrade G, Ramos G, Madeira D, Sachetto R, Ferreira R, Rocha L. G-dbscan: a gpu accelerated algorithm for density-based clustering. Procedia Comput Sci. 2013;18:369\u201378.","journal-title":"Procedia Comput Sci"},{"issue":"2","key":"450_CR30","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1137\/S0036144598334138","volume":"41","author":"G Karypis","year":"1999","unstructured":"Karypis G, Kumar V. Parallel multilevel series k-way partitioning scheme for irregular graphs. Siam Rev. 1999;41(2):278\u2013300.","journal-title":"Siam Rev"},{"issue":"1","key":"450_CR31","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1006\/jpdc.1997.1404","volume":"48","author":"G Karypis","year":"1998","unstructured":"Karypis G, Kumar V. Multilevelk-way partitioning scheme for irregular graphs. J Parallel Distrib Comput. 1998;48(1):96\u2013129.","journal-title":"J Parallel Distrib Comput"},{"doi-asserted-by":"crossref","unstructured":"Zhao W, Ma H, He Q. Parallel k-means clustering based on mapreduce. In: IEEE international conference on cloud computing. Springer; 2009, pp. 674-9.","key":"450_CR32","DOI":"10.1007\/978-3-642-10665-1_71"},{"key":"450_CR33","volume-title":"Clustering: a data recovery approach","author":"B Mirkin","year":"2012","unstructured":"Mirkin B. Clustering: a data recovery approach. Boca Raton: CRC Press; 2012."},{"issue":"1","key":"450_CR34","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s11704-013-3158-3","volume":"8","author":"Y He","year":"2014","unstructured":"He Y, Tan H, Luo W, Feng S, Fan J. Mr-dbscan: a scalable mapreduce-based dbscan algorithm for heavily skewed data. Front Comput Sci. 2014;8(1):83\u201399.","journal-title":"Front Comput Sci"},{"doi-asserted-by":"crossref","unstructured":"Ma C, Liang X, Ma Y. A succinct distributive big data clustering algorithm based on local-remote coordination. In: 2015 IEEE international conference on systems, man, and cybernetics. IEEE; 2015, pp. 1839\u2013844.","key":"450_CR35","DOI":"10.1109\/SMC.2015.322"},{"key":"450_CR36","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.knosys.2018.01.031","volume":"145","author":"G Zhang","year":"2018","unstructured":"Zhang G, Zhang C, Zhang H. Improved k-means algorithm based on density canopy. Knowledge-Based Syst. 2018;145:289\u201397.","journal-title":"Knowledge-Based Syst"},{"issue":"1","key":"450_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0169-7439(99)00047-7","volume":"50","author":"R De Maesschalck","year":"2000","unstructured":"De Maesschalck R, Jouan-Rimbaud D, Massart DL. The mahalanobis distance. Chemometri Intell Lab Syst. 2000;50(1):1\u201318.","journal-title":"Chemometri Intell Lab Syst"},{"issue":"4","key":"450_CR38","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1109\/TFUZZ.2004.840099","volume":"13","author":"NR Pal","year":"2005","unstructured":"Pal NR, Pal K, Keller JM, Bezdek JC. A possibilistic fuzzy c-means clustering algorithm. IEEE Trans Fuzzy Syst. 2005;13(4):517\u201330.","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"9","key":"450_CR39","doi-asserted-by":"publisher","first-page":"1378","DOI":"10.1002\/dac.2844","volume":"27","author":"Q Zhang","year":"2014","unstructured":"Zhang Q, Chen Z. A weighted kernel possibilistic c-means algorithm based on cloud computing for clustering big data. Int J Commun Syst. 2014;27(9):1378\u201391.","journal-title":"Int J Commun Syst"},{"doi-asserted-by":"crossref","unstructured":"Nguyen CD, Nguyen DT, Pham V-H. Parallel two-phase k-means. In: International conference on computational science and its applications. Springer; 2013, pp. 224\u201331.","key":"450_CR40","DOI":"10.1007\/978-3-642-39640-3_16"},{"issue":"7","key":"450_CR41","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1243\/0954406041319509","volume":"218","author":"DT Pham","year":"2004","unstructured":"Pham DT, Dimov SS, Nguyen C. An incremental k-means algorithm. Proc Inst Mech Eng Part C J Mech Eng Sci. 2004;218(7):783\u201395.","journal-title":"Proc Inst Mech Eng Part C J Mech Eng Sci"},{"unstructured":"Hu C, Kang X, Luo N, Zhao Q. Parallel clustering of big data of spatio-temporal trajectory. In: 2015 11th international conference on natural computation (ICNC). IEEE; 2015, pp. 769\u2013774.","key":"450_CR42"},{"doi-asserted-by":"crossref","unstructured":"Pokhrel AR, Wang S. Design of fast and scalable clustering algorithm on spark. In: Proceedings of the 2020 4th international conference on cloud and big data computing; 2020, pp. 43\u20137.","key":"450_CR43","DOI":"10.1145\/3416921.3416942"},{"issue":"3","key":"450_CR44","doi-asserted-by":"publisher","first-page":"2134","DOI":"10.1109\/TII.2020.2995680","volume":"17","author":"AK Tripathi","year":"2020","unstructured":"Tripathi AK, Sharma K, Bala M, Kumar A, Menon VG, Bashir AK. A parallel military-dog-based algorithm for clustering big data in cognitive industrial internet of things. IEEE Trans Ind Inform. 2020;17(3):2134\u201342.","journal-title":"IEEE Trans Ind Inform"},{"issue":"3","key":"450_CR45","doi-asserted-by":"publisher","first-page":"6915","DOI":"10.4249\/scholarpedia.6915","volume":"5","author":"D Karaboga","year":"2010","unstructured":"Karaboga D. Artificial bee colony algorithm. Scholarpedia. 2010;5(3):6915.","journal-title":"Scholarpedia"},{"issue":"2\u20133","key":"450_CR46","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","volume":"10","author":"JC Bezdek","year":"1984","unstructured":"Bezdek JC, Ehrlich R, Full W. Fcm: The fuzzy c-means clustering algorithm. Comput Geosci. 1984;10(2\u20133):191\u2013203.","journal-title":"Comput Geosci"},{"issue":"1","key":"450_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.scico.2007.07.001","volume":"70","author":"R L\u00e4mmel","year":"2008","unstructured":"L\u00e4mmel R. Google mapreduce programming model revisited. Sci Comput Program. 2008;70(1):1\u201330.","journal-title":"Sci Comput Program"},{"key":"450_CR48","volume-title":"Hadoop fundamentals livelessons (video training)","author":"D Eadline","year":"2013","unstructured":"Eadline D. Hadoop fundamentals livelessons (video training). Boston: Addison-Wesley Professional; 2013."},{"key":"450_CR49","doi-asserted-by":"publisher","first-page":"1890","DOI":"10.1016\/j.sbspro.2015.06.429","volume":"195","author":"C Uzunkaya","year":"2015","unstructured":"Uzunkaya C, Ensari T, Kavurucu Y. Hadoop ecosystem and its analysis on tweets. Procedia-Soc Behav Sci. 2015;195:1890\u20137.","journal-title":"Procedia-Soc Behav Sci"},{"issue":"3","key":"450_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"C-C Chang","year":"2011","unstructured":"Chang C-C, Lin C-J. Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol. 2011;2(3):1\u201327.","journal-title":"ACM Trans Intell Syst Technol"},{"unstructured":"Griffin G, Holub A, Perona P. Caltech-256 object category dataset; 2007.","key":"450_CR51"},{"doi-asserted-by":"crossref","unstructured":"Winn J, Jojic N. Locus: learning object classes with unsupervised segmentation. In: Tenth IEEE international conference on computer vision (ICCV\u201905) Volume 1, vol. 1. IEEE; 2005, pp. 756\u201363.","key":"450_CR52","DOI":"10.1109\/ICCV.2005.148"},{"doi-asserted-by":"crossref","unstructured":"Zhang Q, Yang LT, Chen Z, Li P. Pphopcm: privacy-preserving high-order possibilistic c-means algorithm for big data clustering with cloud computing. IEEE Transactions on Big Data; 2017.","key":"450_CR53","DOI":"10.1109\/TBDATA.2017.2701816"},{"doi-asserted-by":"crossref","unstructured":"Gosain A, Sardana S. Handling class imbalance problem using oversampling techniques: a review. In: 2017 international conference on advances in computing, communications and informatics (ICACCI). IEEE; 2017, pp. 79\u2013 85.","key":"450_CR54","DOI":"10.1109\/ICACCI.2017.8125820"},{"doi-asserted-by":"crossref","unstructured":"Shirkhorshidi AS, Aghabozorgi S, Wah TY, Herawan T. Big data clustering: a review. In: International conference on computational science and its applications. Springer; 2014, pp. 707\u201320.","key":"450_CR55","DOI":"10.1007\/978-3-319-09156-3_49"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-021-00450-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-021-00450-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-021-00450-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T07:35:45Z","timestamp":1698996945000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-021-00450-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,4]]},"references-count":55,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["450"],"URL":"https:\/\/doi.org\/10.1186\/s40537-021-00450-w","relation":{},"ISSN":["2196-1115"],"issn-type":[{"type":"electronic","value":"2196-1115"}],"subject":[],"published":{"date-parts":[[2021,5,4]]},"assertion":[{"value":"6 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"All of the Authors declare that they do not have any particular competing interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"64"}}