{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T04:30:19Z","timestamp":1791606619764,"version":"4.3.4"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T00:00:00Z","timestamp":1574035200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T00:00:00Z","timestamp":1574035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100010665","name":"H2020 Marie Sklodowska-Curie Actions","doi-asserted-by":"publisher","award":["745829"],"award-info":[{"award-number":["745829"]}],"id":[{"id":"10.13039\/100010665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2020,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Data management at the edge of the network can increase the performance of applications as the processing is realized close to end users limiting the observed latency in the provision of responses. A typical data processing involves the execution of queries\/tasks defined by users or applications asking for responses in the form of analytics. Query\/task execution can be realized at the edge nodes that can undertake the responsibility of delivering the desired analytics to the interested users or applications. In this paper, we deal with the problem of allocating queries to a number of edge nodes. The aim is to support the goal of eliminating further the latency by allocating queries to nodes that exhibit a low load and high processing speed, thus, they can respond in the minimum time. Before any allocation, we propose a method for estimating the computational burden that a query\/task will add to a node and, afterwards, we proceed with the final assignment. The allocation is concluded by the assistance of an ensemble similarity scheme responsible to deliver the complexity class for each query\/task and a probabilistic decision making model. The proposed scheme matches the characteristics of the incoming queries and edge nodes trying to conclude the optimal allocation. We discuss our mechanism and through a large set of simulations and the adoption of benchmarking queries, we reveal the potentials of the proposed model supported by numerical results.\n<\/jats:p>","DOI":"10.1007\/s00607-019-00767-8","type":"journal-article","created":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T14:02:38Z","timestamp":1574085758000},"page":"865-892","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A probabilistic model for assigning queries at the edge"],"prefix":"10.1007","volume":"102","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9442-3340","authenticated-orcid":false,"given":"Kostas","family":"Kolomvatsos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christos","family":"Anagnostopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,11,18]]},"reference":[{"key":"767_CR1","first-page":"2360","volume":"29","author":"K Ackermann","year":"2014","unstructured":"Ackermann K, Angus SD (2014) A resource efficient big data analysis method for the social sciences: the case of global IP activity. PCS 29:2360\u20132369","journal-title":"PCS"},{"issue":"37","key":"767_CR2","first-page":"1","volume":"9","author":"P Akilandeswari","year":"2016","unstructured":"Akilandeswari P, Srimathi H (2016) Survey and analysis on task scheduling in cloud environment. Indian J Sci Technol 9(37):1\u20136","journal-title":"Indian J Sci Technol"},{"key":"767_CR3","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1007\/s10115-013-0614-1","volume":"39","author":"J Aligon","year":"2014","unstructured":"Aligon J et al (2014) Similarity measures for OLAP sessions. Knowl Inf Syst 39:463\u2013489","journal-title":"Knowl Inf Syst"},{"key":"767_CR4","first-page":"11","volume":"2","author":"F Almeida","year":"2012","unstructured":"Almeida F, Calistru C (2012) The main challenges and issues of big data management. Int J Res Stud Comput 2:11\u201320","journal-title":"Int J Res Stud Comput"},{"key":"767_CR5","first-page":"179","volume":"23","author":"G Antara","year":"2002","unstructured":"Antara G et al (2002) Plan selection based on query clustering. VLDB Endow 23:179\u2013190","journal-title":"VLDB Endow"},{"issue":"4","key":"767_CR6","first-page":"460","volume":"2","author":"H Artail","year":"2008","unstructured":"Artail H, El Amine H, Sakkal F (2008) SQL query space and time complexity estimation. Int J Intell Inf Database Syst 2(4):460\u2013480","journal-title":"Int J Intell Inf Database Syst"},{"issue":"2","key":"767_CR7","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1109\/TII.2017.2738841","volume":"14","author":"G Aujla","year":"2018","unstructured":"Aujla G, Kumar N, Zomaya A, Ranjan R (2018) Optimal decision making for big data processing at edge-cloud environment: an SDN perspective. IEEE Trans Ind Inf 14(2):778\u2013789","journal-title":"IEEE Trans Ind Inf"},{"key":"767_CR8","unstructured":"Balkensen C, Tatbul N (2011) Scalable data partitioning techniques for parallel sliding window processing over data streams. In: 8th International DMSN"},{"issue":"6","key":"767_CR9","first-page":"773","volume":"9","author":"G Beliakov","year":"2001","unstructured":"Beliakov G, Warren J (2001) Appropriate choice of a Aggregation operators in fuzzy decision support systems. IEEE TFS 9(6):773\u2013784","journal-title":"IEEE TFS"},{"key":"767_CR10","volume-title":"Pattern recognition and machine learning","author":"C Bishop","year":"2006","unstructured":"Bishop C (2006) Pattern recognition and machine learning. Springer, Berlin"},{"key":"767_CR11","doi-asserted-by":"crossref","unstructured":"Breitbach M, Schafer D, Edinger J, Becker C (2019) Context-aware data and task placement in edge computing environments","DOI":"10.1109\/PERCOM.2019.8767386"},{"key":"767_CR12","unstructured":"Bruno N et al (2002) Evaluating top-k queries over web-accessible databases. In: ICDE"},{"issue":"4","key":"767_CR13","doi-asserted-by":"publisher","first-page":"265","DOI":"10.14778\/2732240.2732245","volume":"7","author":"L Cao","year":"2013","unstructured":"Cao L, Rundensteiner EA (2013) High performance stream query processing with correlation-aware partitioning. VLDB Endow 7(4):265\u2013276","journal-title":"VLDB Endow"},{"key":"767_CR14","doi-asserted-by":"crossref","unstructured":"Carvalho O, Garcia M, Roloff E, Carreno E, Navaux P (2017) IoT workload distribution impactbetween edge and cloud computingin a smart grid application. In: Proceedings of CARLA","DOI":"10.1007\/978-3-319-73353-1_14"},{"issue":"2","key":"767_CR15","first-page":"55","volume":"34","author":"G Chatzopoulou","year":"2011","unstructured":"Chatzopoulou G et al (2011) The QueRIE system for personalized query recommendations. IEEE Data Eng Bull 34(2):55\u201360","journal-title":"IEEE Data Eng Bull"},{"issue":"2","key":"767_CR16","doi-asserted-by":"publisher","first-page":"202","DOI":"10.4218\/etrij.17.2816.0109","volume":"39","author":"J-H Choi","year":"2017","unstructured":"Choi J-H, Park J, Park HD, Min O (2017) DART: fast and efficient distributed stream processing framework for internet of things. ETRI J 39(2):202\u2013212","journal-title":"ETRI J"},{"issue":"1\u20133","key":"767_CR17","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/S0019-9958(83)80059-X","volume":"58","author":"SS Cosmadakis","year":"1983","unstructured":"Cosmadakis SS (1983) The complexity of evaluating relational queries. Inf Control 58(1\u20133):101\u2013112","journal-title":"Inf Control"},{"key":"767_CR18","volume-title":"Fundamentals on aggregation operators. Berkeley Initiative in Soft Computing","author":"M Detyniecki","year":"2001","unstructured":"Detyniecki M (2001) Fundamentals on aggregation operators. Berkeley Initiative in Soft Computing. University of California, California"},{"issue":"3","key":"767_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijfls.2012.2301","volume":"2","author":"F Farahbod","year":"2012","unstructured":"Farahbod F, Eftekhari N (2012) Comparison of different T-norm operators in classification problems. IJFLS 2(3):1","journal-title":"IJFLS"},{"key":"767_CR20","doi-asserted-by":"crossref","unstructured":"Fan C, Deng H, Wang F, Wei S, Dai W, Liang B (2015) A survey on task scheduling method in heterogeneous computing system. In: Proceedings of the 8th international conference on intelligent networks and intelligent systems, pp 90\u201393","DOI":"10.1109\/ICINIS.2015.42"},{"key":"767_CR21","doi-asserted-by":"crossref","unstructured":"Fischer L, Bernstein A (2015) Workload scheduling in distributed stream processors using graph partitioning. In: Proceedings of the IEEE international conference on big data","DOI":"10.1109\/BigData.2015.7363749"},{"issue":"4","key":"767_CR22","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/s00778-013-0335-9","volume":"23","author":"B Gedik","year":"2014","unstructured":"Gedik B (2014) Partitioning functions for stateful data parallelism in stream processing. VLDB J 23(4):517\u2013539","journal-title":"VLDB J"},{"key":"767_CR23","doi-asserted-by":"crossref","unstructured":"Habak K, Ammar M, Zegura E, Harras K (2017) Workload management for dynamic mobile device clusters in edge femtoclouds. In: Proceedings of the second ACM\/IEEE symposium on edge computing","DOI":"10.1145\/3132211.3134455"},{"key":"767_CR24","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/978-3-642-03722-1_9","volume":"3","author":"A Hameurlain","year":"2009","unstructured":"Hameurlain A, Morvan F (2009) Evolution of query optimization methods. Trans Large-Scale Data Knowl Cent Syst I 3:211\u2013242","journal-title":"Trans Large-Scale Data Knowl Cent Syst I"},{"key":"767_CR25","unstructured":"Hossain KMM, Raihan Z, Hashem MMA (2013) On appropriate selection of fuzzy aggregation operators in medical decision support system"},{"key":"767_CR26","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/ACCESS.2014.2332453","volume":"2","author":"H Hu","year":"2014","unstructured":"Hu H, Wen Y, Chua TS, Li X (2014) Toward scalable systems for big data analytics: s technology tutorial. IEEE Access 2:652\u2013687","journal-title":"IEEE Access"},{"issue":"5","key":"767_CR27","doi-asserted-by":"publisher","first-page":"977","DOI":"10.3390\/s17050977","volume":"17","author":"RC Huacarpuma","year":"2017","unstructured":"Huacarpuma RC et al (2017) Distributed data service for data management in internet of things middleware. Sensors 17(5):977","journal-title":"Sensors"},{"key":"767_CR28","unstructured":"Hwang S, Chang K (2005) \u2019Optimizing access cost for top-k queries over web sources: a unified cost-based approach. In: ICDE"},{"key":"767_CR29","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1364\/OPN.25.2.000024","volume":"25","author":"B Jalali","year":"2014","unstructured":"Jalali B, Asghari MH (2014) The anamorphic stretch transform, putting the squeeze on big data. Opt Photon N 25:24\u201331","journal-title":"Opt Photon N"},{"issue":"6","key":"767_CR30","first-page":"779","volume":"36","author":"S Jones","year":"2000","unstructured":"Jones S et al (2000) A probabilistic model of information retrieval: development and comparative experiments. IPM 36(6):779\u2013808","journal-title":"IPM"},{"issue":"1","key":"767_CR31","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1109\/TNET.2018.2880874","volume":"27","author":"S Jo\u0161ilo","year":"2019","unstructured":"Jo\u0161ilo S, D\u00e1n G (2019) Decentralized algorithm for randomized task allocation in fog computing systems. IEEE\/ACM Trans Netw 27(1):85\u201397","journal-title":"IEEE\/ACM Trans Netw"},{"key":"767_CR32","doi-asserted-by":"crossref","unstructured":"Kandula S et al (2016) Quickr: lazily approximating complex ad-hoc queries in big data clusters. In: SIGMOD","DOI":"10.1145\/2882903.2882940"},{"key":"767_CR33","doi-asserted-by":"crossref","unstructured":"Kolcun R, McCann JA (2014) Dragon: data discovery and collection architecture for distributed IoT. In: IoT","DOI":"10.1109\/IOT.2014.7030121"},{"key":"767_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-017-1099-5","author":"K Kolomvatsos","year":"2018","unstructured":"Kolomvatsos K (2018) An intelligent scheme for assigning queries. Springer Appl Intell. https:\/\/doi.org\/10.1007\/s10489-017-1099-5","journal-title":"Springer Appl Intell"},{"key":"767_CR35","unstructured":"Kolomvatsos K, Anagnostopoulos C (2018) An edge-centric ensemble scheme for queries assignment. In: 8th CIMA"},{"issue":"1","key":"767_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10489-016-0821-z","volume":"46","author":"K Kolomvatsos","year":"2017","unstructured":"Kolomvatsos K, Hadjiefthymiades S (2017) Learning the engagement of query processors for intelligent analytics. Appl Intell J 46(1):1\u201317","journal-title":"Appl Intell J"},{"issue":"3","key":"767_CR37","doi-asserted-by":"publisher","first-page":"16","DOI":"10.3390\/informatics4030016","volume":"4","author":"K Kolomvatsos","year":"2017","unstructured":"Kolomvatsos K, Anagnostopoulos C (2017) Reinforcement machine learning for predictive analytics in Smart Cities. Informatics 4(3):16","journal-title":"Informatics"},{"key":"767_CR38","doi-asserted-by":"crossref","unstructured":"Krishnan DR et al (2016) IncApprox: a data analytics system for incremental approximate computing. In: 25th WWW","DOI":"10.1145\/2872427.2883026"},{"key":"767_CR39","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2831214","author":"G Kul","year":"2018","unstructured":"Kul G, Luong TA, Xie T, Chandola V, Kennedy O, Upadhyaya S (2018) Similarity metrics for SQL query clustering. IEEE Trans Knowl Data Eng. https:\/\/doi.org\/10.1109\/TKDE.2018.2831214","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"767_CR40","doi-asserted-by":"crossref","unstructured":"Liu J, Mao Y, Zhang J, Letaief, K (2016) Delay-optimal computation task scheduling for mobile-edge computing systems. In: Proceedings of the IEEE international symposium on information theory, pp 1451\u20131455","DOI":"10.1109\/ISIT.2016.7541539"},{"key":"767_CR41","doi-asserted-by":"crossref","unstructured":"Ma X, Zhang S, Li W, Zhang P, Lin C, Shen X (2017) Cost-efficient workload scheduling in cloud assisted mobile edge computing. In: IEEE\/ACM 25th international symposium on quality of service (IWQoS)","DOI":"10.1109\/IWQoS.2017.7969148"},{"key":"767_CR42","doi-asserted-by":"crossref","unstructured":"Mei Q, Fang H, ZHai C (2007) A study of poisson query generation model for information retrieval. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, pp 319\u2013326","DOI":"10.1145\/1277741.1277797"},{"key":"767_CR43","doi-asserted-by":"crossref","unstructured":"Meriam E, Tabbane N (2016) A survey on cloud computing scheduling algorithms. In: Proceedings of the 2016 global summit on computer and information technology, pp 42\u201347","DOI":"10.1109\/GSCIT.2016.6"},{"key":"767_CR44","doi-asserted-by":"crossref","unstructured":"Mijumbi R, Serrat J, Gorricho J, Bouten N, De Turck F, Davy S (2015) Design and evaluation of algorithms for mapping and scheduling of virtual network functions. In: Proceedings of the 1st IEEE conference on network softwarization, pp 1\u20139","DOI":"10.1109\/NETSOFT.2015.7116120"},{"issue":"3","key":"767_CR45","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1145\/3180143","volume":"65","author":"H Ngo","year":"2018","unstructured":"Ngo H, Porat E, Re C, Rudra A (2018) Worst-case optimal join algorithms. J ACM 65(3):16","journal-title":"J ACM"},{"key":"767_CR46","first-page":"205","volume":"3","author":"MT Ozgu","year":"2011","unstructured":"Ozgu MT, Valduriez P (2011) Overview of query processing. Princ Distrib Database Syst 3:205\u2013220","journal-title":"Princ Distrib Database Syst"},{"key":"767_CR47","doi-asserted-by":"crossref","unstructured":"Papageorgiou A, Cheng B, Kovacs E (2015) Real-time data reduction at the network edge of Internet-of-Things systems. In: Proceedings of the 11th international conference on network and service management (CNSM)","DOI":"10.1109\/CNSM.2015.7367373"},{"key":"767_CR48","doi-asserted-by":"crossref","unstructured":"Pu Q, Ananthanarayanan G, Bodik P, Kandula S, Akella A, Bahl P, Stoica I (2015) Low latency geo-distributed data analytics. In: Proceeding of the ACM SIGCOMM","DOI":"10.1145\/2785956.2787505"},{"issue":"3","key":"767_CR49","first-page":"416","volume":"4","author":"P Samal","year":"2013","unstructured":"Samal P, Mishra P (2013) Analysis of variants in Round Robin Algorithms for load balancing in cloud computing. Proc Int J Comput Sci Inf Technol 4(3):416\u2013419","journal-title":"Proc Int J Comput Sci Inf Technol"},{"key":"767_CR50","doi-asserted-by":"crossref","unstructured":"Satyala NT, Pieper RJ (2008) A unified approach for predicting long and short-term capability indices with dependence on manufacturing target bias. Int J Qual Stat Reliab","DOI":"10.1155\/2008\/594753"},{"key":"767_CR51","doi-asserted-by":"crossref","unstructured":"Scoca V, Aral A, Brandic I, De Nicola R, Uriarte R (2018) Scheduling latency-sensitive applications in edge computing. In: Proceedings of the CLOSER","DOI":"10.5220\/0006706201580168"},{"key":"767_CR52","unstructured":"Stefanidis K et al (2009) You may also like results in relational databases. In: PersDB"},{"issue":"4","key":"767_CR53","first-page":"543","volume":"95","author":"K Teerapabolarn","year":"2014","unstructured":"Teerapabolarn K (2014) Poisson approximation for random sums of poisson random variables. IJPAM 95(4):543\u2013546","journal-title":"IJPAM"},{"key":"767_CR54","first-page":"13","volume":"2018","author":"C Tseng","year":"2018","unstructured":"Tseng C, Tseng F, Yang Y, Liu C, Chou L (2018) Task scheduling for edge computing with agile VNFs on-demand service model toward 5G and beyond. Wirel Commun Mobile Comput 2018:13","journal-title":"Wirel Commun Mobile Comput"},{"key":"767_CR55","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1109\/LCOMM.2019.2895828","volume":"23","author":"S Wan","year":"2019","unstructured":"Wan S, Lu J, Fan P, Letaief K (2019) Minor probability events detection in big data: an integrated approach with Bayes detection and big data. IEEE Commun Lett 23:418\u2013421","journal-title":"IEEE Commun Lett"},{"key":"767_CR56","doi-asserted-by":"crossref","unstructured":"Wan S, Lu J, Fan P, Letaief K (2019) Towards Big data processing in IoT: network management for online edge data processing. Preprint, arXiv:1905.01663v1 [cs.NI]","DOI":"10.1109\/GLOBECOM38437.2019.9013486"},{"key":"767_CR57","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-4493-1","volume-title":"Kernel smoothing","author":"MP Wand","year":"1995","unstructured":"Wand MP, Jones MC (1995) Kernel smoothing. Chapman and Hall, London"},{"key":"767_CR58","doi-asserted-by":"crossref","unstructured":"Wang H, Gong J, Zhuang Y, Shen H, Lach J (2017) HealthEdge: Task scheduling for edge computing with health emergency and human behavior consideration in smart homes. In: Proceedings of the 2017 IEEE international conference on big data, pp 1213\u20131222","DOI":"10.1109\/BigData.2017.8258047"},{"key":"767_CR59","doi-asserted-by":"crossref","unstructured":"Wen Z et al (2018) ApproxIoT: approximate analytics for edge computing. In: 38th ICDCS, pp 411\u2013421","DOI":"10.1109\/ICDCS.2018.00048"},{"key":"767_CR60","doi-asserted-by":"crossref","unstructured":"Yao Q, An A, Huang X (2005) Finding and analyzing database user sessions. In: DASFAA","DOI":"10.1007\/11408079_77"},{"key":"767_CR61","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3390\/jsan6030017","volume":"6","author":"M Ur Rehman","year":"2017","unstructured":"Ur Rehman M, Jayaraman P, Malik S, Khan A, Gaber MM (2017) RedEdge: a novel architecture for big data processing in mobile edge computing environments. J Sens Actuator Netw 6:17","journal-title":"J Sens Actuator Netw"},{"key":"767_CR62","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.peva.2015.06.013","volume":"91","author":"R Urgaonkar","year":"2015","unstructured":"Urgaonkar R, Wang S, He T, Zafer M, Chan K, Leung K (2015) Dynamic service migration and workload scheduling in edge-clouds. Perform Eval 91:205\u2013228","journal-title":"Perform Eval"},{"key":"767_CR63","unstructured":"Vashistha A, Jain S (2016) Measuring query complexity in SQLShare workload. In: Proceedings of the 2019 international conference on management of data"},{"key":"767_CR64","doi-asserted-by":"crossref","unstructured":"Yang X et al. (2009) Recommending join queries via query log analysis. In: ICDE","DOI":"10.1109\/ICDE.2009.122"},{"key":"767_CR65","first-page":"709","volume":"3","author":"H Yu","year":"2011","unstructured":"Yu H et al (2011) Exact indexing for support vector machines. ACM SIGMOD 3:709\u2013720","journal-title":"ACM SIGMOD"},{"key":"767_CR66","doi-asserted-by":"crossref","unstructured":"Zeitler E, Risch T (2010) Scalable splitting of massive data streams. In: DASFAA","DOI":"10.1007\/978-3-642-12098-5_15"},{"key":"767_CR67","doi-asserted-by":"crossref","unstructured":"Zhang Z et al (2006) Boolean + ranking: querying a database by k-constrained optimization. In: ACM SIGMOD","DOI":"10.1145\/1142473.1142515"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-019-00767-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00607-019-00767-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-019-00767-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T00:30:49Z","timestamp":1605573049000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00607-019-00767-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,18]]},"references-count":67,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["767"],"URL":"https:\/\/doi.org\/10.1007\/s00607-019-00767-8","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"value":"0010-485X","type":"print"},{"value":"1436-5057","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,18]]},"assertion":[{"value":"24 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}