{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T12:50:04Z","timestamp":1775998204969,"version":"3.50.1"},"reference-count":159,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,12,19]],"date-time":"2020-12-19T00:00:00Z","timestamp":1608336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>This article investigates the impact of big data on the actuarial sector. The growing fields of applications of data analytics and data mining raise the ability for insurance companies to conduct more accurate policy pricing by incorporating a broader variety of data due to increased data availability. The analyzed areas of this paper span from automobile insurance policy pricing, mortality and healthcare modeling to estimation of harvest-, climate- and cyber risk as well as assessment of catastrophe risk such as storms, hurricanes, tornadoes, geomagnetic events, earthquakes, floods, and fires. We evaluate the current use of big data in these contexts and how the utilization of data analytics and data mining contribute to the prediction capabilities and accuracy of policy premium pricing of insurance companies. We find a high penetration of insurance policy pricing in almost all actuarial fields except in the modeling and pricing of cyber security risk due to lack of data in this area and prevailing data asymmetries, for which we identify the application of artificial intelligence, in particular machine learning techniques, as a possible solution to improve policy pricing accuracy and results.<\/jats:p>","DOI":"10.3390\/bdcc4040040","type":"journal-article","created":{"date-parts":[[2020,12,20]],"date-time":"2020-12-20T22:33:53Z","timestamp":1608503633000},"page":"40","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Big Data and Actuarial Science"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0897-8663","authenticated-orcid":false,"given":"Hossein","family":"Hassani","sequence":"first","affiliation":[{"name":"Research Institute of Energy Management and Planning, University of Tehran, Tehran 1417466191, Iran"},{"name":"Department of Business &amp; Management, Webster Vienna Private University, 1020 Vienna, Austria"}]},{"given":"Stephan","family":"Unger","sequence":"additional","affiliation":[{"name":"Department of Economics and Business, Saint Anselm College, Manchester, NH 03102, USA"}]},{"given":"Christina","family":"Beneki","sequence":"additional","affiliation":[{"name":"Department of Tourism, Faculty of Economic Sciences, Ionian University, Kalypso Building, 4 P. Vraila Armeni, 49100 Corfu, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,19]]},"reference":[{"key":"ref_1","unstructured":"(2020, December 16). Global Insurance Premiums Exceed $5 Trillion for the First Time. Available online: https:\/\/www.captive.com\/news\/2019\/07\/09\/global-insurance-premiums-exceed-5-trillion."},{"key":"ref_2","unstructured":"GDP (Current US$) (2019, October 15). World Development Indicators. Available online: https:\/\/en.wikipedia.org\/wiki\/List_of_countries_by_GDP_(nominal)."},{"key":"ref_3","unstructured":"(2020, December 16). Encyclopedia Britannica, Historical Development of Insurance. Available online: https:\/\/www.britannica.com\/topic\/insurance\/Historical-development-of-insurance."},{"key":"ref_4","unstructured":"Johnston, M. (1903). Burial places and funeral ceremonies. The Private Life of the Romans, Scott, Foresman and Company. Available online: https:\/\/en.wikipedia.org\/wiki\/Actuarial_science#CITEREFJohnston1932."},{"key":"ref_5","first-page":"92","article-title":"Institutional Bases of the Spontaneous Order: Surety and Assurance","volume":"7","author":"Loan","year":"1991","journal-title":"Hum. Stud. Rev."},{"key":"ref_6","unstructured":"(2017). Practice areas. The Official Guide to Becoming an Actuary, Institute and Faculty of Actuaries. Available online: https:\/\/silo.tips\/download\/the-official-guide-to-becoming-an-actuary."},{"key":"ref_7","unstructured":"(2020, December 17). Deloitte Insights, 2021 Insurance Outlook. Available online: https:\/\/www2.deloitte.com\/us\/en\/pages\/financial-services\/articles\/insurance-industry-outlook.html."},{"key":"ref_8","unstructured":"Pearson, R. (2004). Insuring the Industrial Revolution: Fire Insurance in Great Britain, 1700\u20131850, Ashgate Publishing Company."},{"key":"ref_9","first-page":"596","article-title":"An estimate of the degrees of mortality of mankind, drawn from curious tables of the births and funerals at the city of Breslaw, with an attempt to ascertain the price of annuities upon lives","volume":"17","author":"Halley","year":"1693","journal-title":"Philos. Trans. R. Soc. Lond."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1111\/j.1467-985X.2010.00684.x","article-title":"A new look at Halley\u2019s life table","volume":"174","author":"Bellhouse","year":"2011","journal-title":"J. R. Stat. Soc. A"},{"key":"ref_11","unstructured":"Grattan-Guinness, I. (2005). Landmark Writings in Western Mathematics 1640\u20131940, Elsevier."},{"key":"ref_12","unstructured":"Dunnigton, W.G. (2004). Gauss: The Titan of Science, The Mathematical Association of America (Incorporated)."},{"key":"ref_13","first-page":"91","article-title":"Stochastic Life Contingencies with Solvency Considerations","volume":"42","author":"Frees","year":"1990","journal-title":"Trans. Soc. Actuar."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shapiro, A.F., and Jain, L.C. (2003). Intelligent and Other Computational Techniques in Insurance: Theory and Applications, World Scientific.","DOI":"10.1142\/5441"},{"key":"ref_15","unstructured":"KPMG (2020, December 17). How Augmented and Virtual Reality Are Changing the Insurance Landscape. Seizing the Opportunity. Available online: https:\/\/members.aixr.org\/storage\/how-augmented-and-virtual-reality-changing-insurance-landscape%20(1).pdf."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Corlosquet-Habart, M., and Jansen, J. (2018). Big Data for Insurance Companies, John Wiley and Sons, Inc.","DOI":"10.1002\/9781119489368"},{"key":"ref_17","unstructured":"European Insurance and Occupational Pensions Authority (EIOPA) (2019). Big Data Analytics in Motor and Health Insurance, Publications Office of the European Union."},{"key":"ref_18","unstructured":"BearingPoint Institute (2020, December 17). The Smart Insurer: Embedding Big Data in Corporate Strategy. Available online: https:\/\/www.bearingpoint.com\/en-us\/our-success\/thought-leadership\/the-smart-insurer-embedding-big-data-in-corporate-strategy\/."},{"key":"ref_19","unstructured":"Deloitte Insights (2019). Sector spotlight: Insurance. Global Risk Management, [11th ed.]. Available online: https:\/\/www2.deloitte.com\/content\/dam\/insights\/us\/articles\/4222_Global-risk-management-survey\/DI_global-risk-management-survey.pdf."},{"key":"ref_20","unstructured":"(2020, December 17). Big Data and Insurance: Implications for Innovation, Competition and Privacy. Available online: https:\/\/www.genevaassociation.org\/research-topics\/cyber-and-innovation-digitalization\/big-data-and-insurance-implications-innovation."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Berthel\u00e9, E. (2018). Using Big Data in Insurance. Big Data for Insurance Companies, John Wiley.","DOI":"10.1002\/9781119489368.ch5"},{"key":"ref_22","unstructured":"OECD (2020). The Impact of Big Data and Artificial Intelligence (AI) in the Insurance Sector, OECD. Available online: http:\/\/www.oecd.org\/finance\/Impact-Big-Data-AI-in-the-Insurance-Sector.htm."},{"key":"ref_23","unstructured":"SNS Telecom & IT (2020, December 17). Big Data in the Insurance Industry: 2018\u20132030\u2014Opportunities, Challenges, Strategies & Forecasts. Available online: https:\/\/www.snstelecom.com\/bigdatainsurance."},{"key":"ref_24","unstructured":"Corbett, P., Schroeck, M., and Shockley, R. (2020, December 17). Analytics: The Real-World Use of Big Data in Insurance. Executive Report, IBM Institute for Business Value. Available online: https:\/\/www.ibm.com\/downloads\/cas\/LKMQWLPY."},{"key":"ref_25","unstructured":"(2020, December 17). PWC\u2019s HR Technology Survey. Available online: https:\/\/www.pwc.com\/us\/en\/library\/workforce-of-the-future\/hr-tech-survey.html."},{"key":"ref_26","unstructured":"Topol, E.J. (2015). The Patient Will See You Now: The Future of Medicine Is in Your Hands, Basic Books."},{"key":"ref_27","unstructured":"Swiss Re (2016). Wearables: New technology\u2014New risks. Trend Spotlight, Swiss Re."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Meyers, G., and Van Hoyweghen, I. (2017). Enacting Actuarial Fairness in Insurance: From Fair Discrimination to Behaviour-based Fairness. Sci. Cult., 1\u201329.","DOI":"10.1080\/09505431.2017.1398223"},{"key":"ref_29","unstructured":"(2018, August 23). Available online: https:\/\/www.capgemini.com\/2018\/08\/bigtech-firms-take-measured-steps-toward-the-insurance-sector\/."},{"key":"ref_30","unstructured":"(2020, December 17). World Insurance Report (WIR) 2018: Digital Agility is Key for Insurers as BigTechs Ponder Entering the Market from Capgemini in Collaboration with Efma. Available online: https:\/\/www.capgemini.com\/news\/world-insurance-report-2018-as-more-customers-buy-insurance-from-bigtechs-digital-agility-is-key-for-traditional-insurers\/."},{"key":"ref_31","unstructured":"(2020, December 17). SOA. Available online: https:\/\/www.soa.org\/globalassets\/assets\/files\/resources\/research-report\/2019\/emerging-analytics-techniques-applications.pdf."},{"key":"ref_32","unstructured":"(2020, December 17). Big Data and the Role of the Actuary. Available online: https:\/\/www.actuary.org\/sites\/default\/files\/files\/publications\/BigDataAndTheRoleOfTheActuary.pdf."},{"key":"ref_33","unstructured":"Sondergeld, E.T., and Purushotham, M.C. (2020, December 17). Top Acturial Technologies of 2019, Available online: https:\/\/www.soa.org\/globalassets\/assets\/files\/resources\/research-report\/2019\/actuarial-innovation-technology.pdf."},{"key":"ref_34","unstructured":"Guo, L. (2020, December 17). Applying Data Mining Techniques in Property\/Casualty Insurance. Available online: https:\/\/www.casact.org\/pubs\/forum\/03wforum\/03wf001.pdf."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wedel, M., and Kannan, P.K. (2016). Marketing Analytics for Data-Rich Environments. J. Mark., 80.","DOI":"10.1509\/jm.15.0413"},{"key":"ref_36","unstructured":"Deloitte Insights (2020, December 17). 2020 Insurance Outlook. Available online: https:\/\/www2.deloitte.com\/us\/en\/insights\/industry\/financial-services\/financial-services-industry-outlooks\/insurance-industry-outlook.html."},{"key":"ref_37","unstructured":"BearingPoint Institute (2020, December 17). The Smart Insurer: More than Just Big Data. Available online: https:\/\/www.bearingpoint.com\/en\/our-success\/thought-leadership\/the-smart-insurer-embedding-big-data-in-corporate-strategy\/."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bakratsas, M., Basaras, P., Katsaros, D., and Tassiulas, L. (2017). Hadoop MapReduce performance on SSDs for analyzing social networks. Big Data Res.","DOI":"10.1016\/j.bdr.2017.06.001"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Billot, R., Bothorel, C., and Lenca, P. (2018). Introduction to Big Data and Its Applications in Insurance. Big Data for Insurance Companies, Wiley.","DOI":"10.1002\/9781119489368.ch1"},{"key":"ref_40","unstructured":"Kunce, J., and Chatterjee, S. (2020, December 17). A Machine-Learning Approach to Parameter Estimation, Casualty Actuarial Society Monograph Series 6, CAS. Available online: https:\/\/www.casact.org\/pubs\/monographs\/papers\/06-Kunce-Chatterjee.pdf."},{"key":"ref_41","unstructured":"Noll, A., Salzmann, R., and W\u00fcthrich, M.V. (2020, December 17). Case Study: French Motor Third-Party Liability Claims. Available online: https:\/\/ssrn.com\/abstract=3164764."},{"key":"ref_42","unstructured":"Zappa, D., Clemente, G.P., Borrelli, M., and Savelli, N. (2020, December 17). Text Mining in Insurance: From Unstructured Data to Meaning. Available online: https:\/\/www.variancejournal.org\/articlespress\/articles\/Text_Mining-Zappa-Borrelli-Clemente-Savelli.pdf."},{"key":"ref_43","unstructured":"(2020, December 17). Report on the Use of Big Data by Financial Institutions. Available online: https:\/\/www.esma.europa.eu\/sites\/default\/files\/library\/jc-2016-86_discussion_paper_big_data.pdf."},{"key":"ref_44","unstructured":"(2020, December 17). P&C Insurance Trends to Watch in 2019. Available online: www.cbinsights.com\/research\/insurance-trends-2019\/."},{"key":"ref_45","unstructured":"Bellina, R., Ly, A., and Taillieu, F. (2020, December 17). A European Insurance lEader Works with Milliman to Process Raw Telematics Data and Detect Driving Behavior. Milliman White Paper, Available online: https:\/\/www.milliman.com\/en\/insight\/a-european-insurance-leader-works-with-milliman-to-process-raw-telematics-data-and-detect."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"113156","DOI":"10.1016\/j.dss.2019.113156","article-title":"Automobile insurance classification ratemaking based on telematics driving data","volume":"127","author":"Huang","year":"2019","journal-title":"Decis. Support Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.aap.2016.10.006","article-title":"Innovative motor insurance schemes: A review of current practices and emerging challenges","volume":"98","author":"Tselentis","year":"2017","journal-title":"Accid. Anal. Prev."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.dss.2017.04.009","article-title":"The value of vehicle telematics data in insurance risk selection processes","volume":"98","author":"Baecke","year":"2017","journal-title":"Decis. Support Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.dss.2013.06.001","article-title":"Evaluation and aggregation of pay-as-you-drive insurance rate factors: A classification analysis approach","volume":"56","author":"Paefgen","year":"2013","journal-title":"Decis. Support Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.tra.2013.11.010","article-title":"Multivariate exposure modeling of accident risk: Insights from Pay-as-you-drive insurance data","volume":"61","author":"Paefgen","year":"2014","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1016\/j.proeng.2015.01.436","article-title":"Telematics System in Usage Based Motor Insurance","volume":"100","author":"Husnjak","year":"2015","journal-title":"Procedia Eng."},{"key":"ref_52","unstructured":"Richman, R. (2018, January 24\u201325). AI in Actuarial Science. Presented at the Actuarial Society of South Africa\u2019s 2018 Convention, Cape Town, South Africa."},{"key":"ref_53","unstructured":"KPMG (2020, December 17). The Chaotic Middle. The Autonomous Vehicle and Disruption in Automobile Insurance, White Paper. Available online: https:\/\/assets.kpmg\/content\/dam\/kpmg\/us\/pdf\/2017\/06\/chaotic-middle-autonomous-vehicle-paper.pdf."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Alshamsi, A.S. (2014, January 9\u201311). Predicting car insurance policies using random forest. Proceedings of the 2014 10th International Conference on Innovations in Information Technology (IIT), Abu Dhabi, UAE.","DOI":"10.1109\/INNOVATIONS.2014.6987575"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.procs.2020.02.016","article-title":"Research on the Features of Car Insurance Data Based on Machine Learning","volume":"166","author":"Wang","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.dss.2017.11.001","article-title":"Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud","volume":"105","author":"Wang","year":"2018","journal-title":"Decis. Support Syst."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Subudhi, S., and Panigrahi, S. (2018, January 21\u201323). Effect of Class Imbalanceness in Detecting Automobile Insurance Fraud. Proceedings of the 2nd International Conference on Data Science and Business Analytics, Changsha, China.","DOI":"10.1109\/ICDSBA.2018.00104"},{"key":"ref_58","unstructured":"Institute and Faculty of Actuaries (IFoA) (2020, December 17). Longevity Bulletin. Big Data Health. Available online: https:\/\/www.actuaries.org.uk\/system\/files\/field\/document\/Longevity%20Bulletin%20Issue%209.pdf."},{"key":"ref_59","unstructured":"LLMA (2020, December 17). Longevity Pricing Framework, A Framework for Pricing Longevity Exposures Developed by the Life & Longevity Markets Association (LLMA). Available online: www.llma.org."},{"key":"ref_60","unstructured":"Silverman, S., and Simpson, P. (2020, December 17). Case Study: Modelling Longevity Risk for Solvency II. Available online: https:\/\/www.milliman.com\/-\/media\/products\/reveal\/modelling-longevity-risk.ashx."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1017\/S1748499500000440","article-title":"Mortality modelling and forecasting: A review of methods","volume":"3","author":"Booth","year":"2008","journal-title":"Ann. Actuar. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s13385-017-0152-4","article-title":"Machine learning techniques for mortality modeling","volume":"7","author":"Deprez","year":"2017","journal-title":"Eur. Actuar. J."},{"key":"ref_63","unstructured":"Kopinsky, M. (2020, December 17). Predicting Group Long Term Disability Recovery and Mortality Rates Using Tree Models, SOA. Available online: https:\/\/www.soa.org\/globalassets\/assets\/Files\/Research\/Projects\/2017-gltd-recovery-mortality-tree.pdf."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1017\/asb.2017.45","article-title":"A neural-network analyzer for mortality forecast","volume":"48","author":"Hainaut","year":"2018","journal-title":"Astin Bull."},{"key":"ref_65","unstructured":"Shang, K. (2017). Individual Cancer Mortality Prediction, Insurance and Social Protection Area. Available online: www.fundacionmapfre.org."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"103311","DOI":"10.1016\/j.jbi.2019.103311","article-title":"Transforming Healthcare with Big Data Analytics and Artificial Intelligence: A Systematic Mapping Study","volume":"100","author":"Mehta","year":"2019","journal-title":"J. Biomed. Inform."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"S660","DOI":"10.1007\/s11606-013-2455-8","article-title":"Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework","volume":"28","author":"Chawla","year":"2013","journal-title":"J. Gen. Intern. Med."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/2047-2501-2-3","article-title":"Big data analytics in healthcare: Promise and potential","volume":"2","author":"Raghupathi","year":"2014","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Belle, A., Thiagarajan, R., Soroushmehr, S.M.R., Navidi, F., Beard, D.A., and Najarian, K. (2015). Big Data Analytics in Healthcare. BioMed Research International, Hindawi Publishing Corporation.","DOI":"10.1155\/2015\/370194"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1038\/35057062","article-title":"Initial sequencing and analysis of the human genome","volume":"409","author":"Lander","year":"2001","journal-title":"Nature"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1126\/science.1181498","article-title":"Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays","volume":"327","author":"Drmanac","year":"2010","journal-title":"Science"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1377\/hlthaff.2014.0041","article-title":"Big Data in Health Care: Using Analytics to Identify and Manage High-Risk and High-Cost Patients","volume":"33","author":"Bates","year":"2014","journal-title":"Health Aff."},{"key":"ref_73","first-page":"122","article-title":"Big Data in Healthcare Hype and Hope","volume":"360","author":"Feldman","year":"2012","journal-title":"Comput. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Kandula, S., and Shaman, J. (2019). Reappraising the utility of Google Flu Trends. PLoS Comput. Biol., 15.","DOI":"10.1371\/journal.pcbi.1007258"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"3485","DOI":"10.1111\/evo.12534","article-title":"Evolutionary bursts inEuphorbia(Euphorbiaceae) are linked with photosynthetic pathway","volume":"68","author":"Horn","year":"2014","journal-title":"Evolution"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"e13651","DOI":"10.14814\/phy2.13651","article-title":"Exercise and exercise training-induced increase in autophagy markers in human skeletal muscle","volume":"6","author":"Brandt","year":"2018","journal-title":"Physiol. Rep."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2483","DOI":"10.1016\/j.jbiomech.2008.05.017","article-title":"An accurate estimation of bone density improves the accuracy of subject-specific finite element models","volume":"41","author":"Schileo","year":"2008","journal-title":"J. Biomech."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1590\/S0102-79722013000200018","article-title":"Child maltreatment and later cognitive functioning: A systematic review","volume":"26","author":"Irigaray","year":"2013","journal-title":"Psicol. Reflex\u00e3o Cr\u00edtica"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1037\/a0032612","article-title":"Measurement development and validation of the Family Supportive Supervisor Behavior Short-Form (FSSB-SF)","volume":"18","author":"Hammer","year":"2013","journal-title":"J. Occup. Health Psychol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1007\/s11517-012-1023-4","article-title":"Share and enjoy: Anatomical models database--generating and sharing cardiovascular model data using web services","volume":"51","author":"Kerfoot","year":"2013","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1136\/heartjnl-2015-308044","article-title":"Computational fluid dynamics modelling in cardiovascular medicine","volume":"102","author":"Morris","year":"2016","journal-title":"Heart"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1093\/bioinformatics\/btt120","article-title":"CytoHiC: A cytoscape plugin for visual comparison of Hi-C networks","volume":"29","author":"Lio","year":"2013","journal-title":"Bioinformatics"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s12528-008-9004-1","article-title":"Facilitating guided participation through mobile technologies: Designing creative learning environments for self and others","volume":"20","author":"Evans","year":"2008","journal-title":"J. Comput. High. Educ."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Csurka, G., Kraus, M., Laramee, R.S., Richard, P., and Braz, J. (2013). Fast realistic modelling of muscle fibres. Computer Vision, Imaging and Computer Graphics, Springer. Theory and Application; Communications in Computer and Information Science.","DOI":"10.1007\/978-3-642-38241-3"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1109\/TED.2011.2107557","article-title":"Single-Crystalline Si STacked ARray (STAR) NAND Flash Memory","volume":"58","author":"Jyun","year":"2011","journal-title":"IEEE Trans. Electron Devices"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"37","DOI":"10.18203\/2349-3259.ijct20161408","article-title":"In silico clinical trials: How computer simulation will transform the biomedical industry","volume":"3","author":"Viceconti","year":"2016","journal-title":"Int. J. Clin. Trials"},{"key":"ref_87","unstructured":"Diana, A., Griffin, J., Oberoi, J., and Yao, J. (2020, December 17). Machine-Learning Methods for Insurance Applications-A Survey. Available online: https:\/\/www.soa.org\/resources\/research-reports\/2019\/machine-learning-methods\/."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Toyoda, S., and Niki, N. (2012). Information Visualization for Chronic Patient\u2019s Data. ISIP, Springer.","DOI":"10.1007\/978-3-642-40140-4_9"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Kareem, S., Ahmad, R., and Sarlan, A. (2017, January 16\u201317). Framework for the identification of fraudulent health insurance claims using association rule mining. Proceedings of the 2017 IEEE Conference on Big Data and Analytics (ICBDA), Kuching, Malaysia.","DOI":"10.1109\/ICBDAA.2017.8284114"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Dhieb, N., Ghazzai, H., Besbes, H., and Massoud, Y. (2020). A Secure AI-Driven Architecture for Automated Insurance Systems: Fraud Detection and Risk Measurement. IEEE Access, 8.","DOI":"10.1109\/ACCESS.2020.2983300"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.techfore.2015.12.019","article-title":"Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations","volume":"126","author":"Wang","year":"2018","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Hartmann, B., Owen, R., and Gibbs, Z. (2020, December 17). Predicting High-Cost Health Insurance Members through Boosted Trees and Oversampling: An Application Using the HCCI Database. Available online: https:\/\/hartman.byu.edu\/docs\/files\/HartmanOwenGibbs_HighCostClaims.pdf.","DOI":"10.1080\/10920277.2020.1754242"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Boodhun, N., and Jayabalan, M. (2018). Risk Prediction in Life Insurance Industry using Supervised Learning Algorithms. Complex Intell. Syst.","DOI":"10.1007\/s40747-018-0072-1"},{"key":"ref_94","unstructured":"The International Actuarial Association (2020, December 17). Impact of Personalised Medicine and Genomics on the Insurance Industry. Available online: http:\/\/www.actuaries.org\/LIBRARY\/Papers\/HC_Personalised_Medicine_Paper_Final.pdf."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"34","DOI":"10.3389\/fmed.2019.00034","article-title":"From Big Data to Precision Medicine","volume":"6","author":"Hulsen","year":"2019","journal-title":"Front. Med."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.copbio.2019.03.004","article-title":"Big data analytics for personalized medicine","volume":"58","author":"Cirillo","year":"2019","journal-title":"Curr. Opin. Biotechnol."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","article-title":"International evaluation of an AI system for breast cancer screening","volume":"577","author":"McKinney","year":"2020","journal-title":"Nature"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1056\/NEJMoa1901183","article-title":"Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation","volume":"381","author":"Perez","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Kamilaris, A., Ant\u00f3n, A., Bonmat\u00ed, A., Torrellas, M., and Prenafeta Bold\u00fa, F. (2018). Estimating the Environmental Impact of Agriculture by Means of Geospatial and Big Data Analysis: The Case of Catalonia, Springer.","DOI":"10.1007\/978-3-319-65687-8_4"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1098\/rstb.2007.2163","article-title":"Agricultural sustainability: Concepts, principles and evidence","volume":"363","author":"Pretty","year":"2007","journal-title":"Philos. Trans. R. Soc. B Biol. Sci."},{"key":"ref_101","first-page":"257","article-title":"Big and Meta Data Management for U-Agriculture Mobile Services","volume":"10","author":"Nandyala","year":"2016","journal-title":"Int. J. Softw. Eng. Its Appl."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.is.2014.07.006","article-title":"The rise of \u201cbig data\u201d on cloud computing: Review and open research issues","volume":"47","author":"Hashem","year":"2015","journal-title":"Inf. Syst."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1006\/jaer.2001.0719","article-title":"Recognition system for pig cough based on probabilistic neural networks","volume":"79","author":"Chedad","year":"2001","journal-title":"J. Agric. Eng. Res."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s11540-018-9357-4","article-title":"Advances in Variable Rate Technology Application in Potato in The Netherlands","volume":"60","author":"Kempenaar","year":"2017","journal-title":"Potato Res."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.3390\/rs2061589","article-title":"Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project","volume":"2","author":"Justice","year":"2010","journal-title":"Remote. Sens."},{"key":"ref_106","first-page":"44","article-title":"Spatially enabling the Global Framework for Climate Services: Reviewing geospatial solutions to efficiently share and integrate climate data & information","volume":"8","author":"Giuliani","year":"2017","journal-title":"Clim. Serv."},{"key":"ref_107","unstructured":"Karmas, A., Karantzalos, K., and Athanasiou, S. (2014, January 8\u201313). Online analysis of remote sensing data for agricultural applications. Proceedings of the OSGeo\u2019s European Conference on Free and Open Source Software for Geospatial, Portland, OR, USA."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.1016\/j.cell.2018.05.056","article-title":"Visible Machine Learning for Biomedicine","volume":"173","author":"Yu","year":"2018","journal-title":"Cell"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Mucherino, A., Papajorgji, P.J., and Pardalos, P. (2009). Data Mining Agriculture, Springer.","DOI":"10.1007\/978-0-387-88615-2"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"100189","DOI":"10.1016\/j.crm.2019.100189","article-title":"Designing weather index insurance of crops for the increased satisfaction of farmers, industry and the government","volume":"25","author":"Shirsath","year":"2019","journal-title":"Clim. Risk Manag."},{"key":"ref_111","first-page":"1","article-title":"Big Data in Agriculture: Property Rights, Privacy and Competition in Ag Data Services","volume":"19","author":"Sykuta","year":"2016","journal-title":"Int. Food Agribus. Manag. Rev."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1016\/j.telpol.2014.10.002","article-title":"Big data\u05f3s impact on privacy, security and consumer welfare","volume":"38","author":"Kshetri","year":"2014","journal-title":"Telecommun. Policy"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"5395","DOI":"10.35940\/ijitee.A4695.119119","article-title":"Prediction of Crops based on Environmental Factors using IoT & Machine Learning Algorithms","volume":"9","author":"Ashok","year":"2019","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Lane, M., and Mahul, O. (2008). Catastrophe Risk Pricing\u2014An Empirical Analysis, World Bank. WPS: 4765.","DOI":"10.1596\/1813-9450-4765"},{"key":"ref_115","first-page":"10","article-title":"The role of catastrophe modeling in insurance rating","volume":"54","author":"Li","year":"2007","journal-title":"Risk Manag."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1080\/0308514032000073428","article-title":"Catastrophe risk","volume":"32","author":"Bougen","year":"2003","journal-title":"Econ. Soc."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Logic Mark and Accord (2020, December 17). Making Sense of Big Data in Insurance (White Paper). Available online: https:\/\/cdn1.marklogic.com\/wp-content\/uploads\/2018\/01\/making-sense-big-data-in-insurance-130913.pdf.","DOI":"10.3389\/fdata.2018.00005"},{"key":"ref_118","unstructured":"(2020, December 17). Big Data Analytics Is Shaking up the Insurance Business?. Available online: www.datanami.com."},{"key":"ref_119","unstructured":"Schruek, M., and Shockley, R. (2020, December 17). Analytics: Real World Use of Big Data in Insurance. Available online: m.ibm.com."},{"key":"ref_120","unstructured":"TIBCO Blog (2020, December 17). 4 Ways Big Data Is Transforming the Insurance Industry. Available online: https:\/\/www.tibco.com\/blog\/2015\/07\/20\/4-ways-big-data-is-transforming-the-insurance-industry\/."},{"key":"ref_121","unstructured":"Nguyen, L., Yang, Z., Li, J., Pan, Z., Cao, G., and Jin, F. (2019). Forecasting People\u2019s Needs in Hurricane Events from Social Network. IEEE Trans. Big Data."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Hangan, H., Refan, M., Jubayer, C., Parvu, D., and Kilpatrick, R. (2016). Big Data from Big Experiments. The WindEEE Dome. Whither Turbulence and Big Data in the 21st Century, Springer.","DOI":"10.1007\/978-3-319-41217-7_12"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Cox, T.S., Hoi, C.S.H., Leung, C.K., and Marofke, C.R. (2018, January 23\u201327). An Accurate Model for Hurricane Trajectory Prediction. Proceedings of the 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, Japan.","DOI":"10.1109\/COMPSAC.2018.10290"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"70","DOI":"10.3141\/2599-09","article-title":"Using Big Data to Study Resilience of Taxi and Subway Trips for Hurricanes Sandy and Irene","volume":"2599","author":"Zhu","year":"2016","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Camara, R.C., Cuzzocrea, A., Grasso, G.M., Leung, C.K., Powell, S.B., Souza, J., and Tang, B. (2018, January 23\u201326). Fuzzy Logic-Based Data Analytics on Predicting the Effect of Hurricanes on the Stock Market. Proceedings of the 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA.","DOI":"10.1109\/FUZZ-IEEE.2018.8491523"},{"key":"ref_126","unstructured":"Aladangady, A., Aron-Dine, S., Dunn, W.E., Feiveson, L., Lengermann, P., and Sahm, C. (2020, December 17). The Effect of Hurricane Matthew on Consumer Spending, Feds Notes. Available online: https:\/\/ssrn.com\/abstract=3056155."},{"key":"ref_127","unstructured":"Chen, Z., Sharma, P., and Sutley, E.J. (2019, January 9\u201313). Deep learning of Tornado Disaster Scenes using Unmanned-Aerial-Vehicle (UAV) Images. Proceedings of the American Geophysical Union, Fall Meeting 2019, San Francisco, CA, USA."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Elsner, J.B., Fricker, T., and Schroder, Z. (2018). Increasingly Powerful Tornadoes in the United States. Geophys. Res. Lett.","DOI":"10.31223\/OSF.IO\/WPKT9"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1080\/17538947.2017.1279235","article-title":"Funnel Cloud: A cloud-based system for exploring tornado events","volume":"10","author":"Lian","year":"2017","journal-title":"Int. J. Digit. Earth"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Srebrov, B., Kounchev, O., and Simeonov, G. (2020). Chapter 19\u2014Big Data for the Magnetic Field Variations in Solar-Terrestrial Physics and Their Wavelet Analysis. Knowledge Discovery in Big Data from Astronomy and Earth Observation, Astrogeoinformatics, Elsevier.","DOI":"10.1016\/B978-0-12-819154-5.00031-X"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Pashova, L., Srebrov, B., and Kounchev, O. (2019). Investigation of Strong Geomagnetic Storms Using Multidisciplinary Big Data Sets, IEEE.","DOI":"10.1109\/BdKCSE48644.2019.9010611"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.asr.2018.07.016","article-title":"Impact of geomagnetic storm of 7\u20138 September 2017 on ionosphere and HF propagation: A multi-instrument study","volume":"63","author":"Blagoveshchensky","year":"2019","journal-title":"Adv. Space Res."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1002\/2014SW001072","article-title":"Geomagnetic storms super-storms and their impacts on GPS-based navigation systems","volume":"12","author":"Astafyeva","year":"2014","journal-title":"Space Weather"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"18","DOI":"10.5334\/dsj-2016-018","article-title":"Automated Hardware and Software System for Monitoring the Earth\u2019s Magnetic Environment","volume":"15","author":"Gvishiani","year":"2016","journal-title":"Data Sci. J."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"A25","DOI":"10.1051\/swsc\/2015026","article-title":"The European Ionosonde Service: Nowcasting and forecasting ionospheric conditions over Europe for the ESA Space Situational Awareness services","volume":"5","author":"Belehaki","year":"2015","journal-title":"J. Space Weather Space Clim."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1057\/s41283-019-00052-0","article-title":"Modeling and pricing of space weather derivatives","volume":"21","author":"Lemmerer","year":"2019","journal-title":"Risk Manag."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1109\/JIOT.2019.2954128","article-title":"Tornado: Enabling Blockchain in Heterogeneous Internet of Things through a Space-Structured Approach","volume":"7","author":"Liu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1080\/15326349708807451","article-title":"Earthquake size distribution and earthquake insurance","volume":"13","author":"Kagan","year":"1997","journal-title":"Commun. Stat. Stoch. Models"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/env.3170040102","article-title":"Earthquake risk and insurance","volume":"4","author":"Brillinger","year":"1993","journal-title":"Environmetrics"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Mouyiannou, A., and Styles, K.E. (2017, January 15\u201317). From Structural Performance to Loss Estimation for (Re) Insurance Industry Needs: An Overview of the Vulnerability Estimation Approaches within Earthquake Catastrophe Models. Proceedings of the COMPDYN 2017 6th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Rhodes Island, Greece.","DOI":"10.7712\/120117.5536.18309"},{"key":"ref_141","first-page":"1","article-title":"Insights into seismic hazard from big data analysis of ground motion simulations","volume":"9","author":"Tiampo","year":"2019","journal-title":"Int. J. Saf. Secur. Eng."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1057\/palgrave.gpp.2510151","article-title":"Role of Insurance in Reducing Flood Risk","volume":"33","author":"Crichton","year":"2007","journal-title":"Geneva Pap. Risk Insur. Issues Pract."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.5194\/nhess-13-2003-2013","article-title":"Contribution of insurance data to cost assessment of coastal flood damage to residential buildings: Insights gained from Johanna (2008) and Xynthia (2010) storm events","volume":"13","author":"Andre","year":"2013","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1038\/507169e","article-title":"Fight floods on a global scale","volume":"507","author":"Schumann","year":"2013","journal-title":"Nature"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"e008175","DOI":"10.1136\/bmjopen-2015-008175","article-title":"Health insurance determines antenatal, delivery and postnatal care utilisation: Evidence from the Ghana Demographic and Health Surveillance data","volume":"6","author":"Browne","year":"2016","journal-title":"BMJ Open"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Gao, C., and Wang, M. (2016, January 4\u20136). Forest Fire Risk Assessment Based on Ecological and Economic Value\u2014Take Yunnan Province as an Example. Proceedings of the 7th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention, Changsha, China.","DOI":"10.2991\/rac-16.2016.152"},{"key":"ref_147","unstructured":"Tang, J. (2018). Big Data and Predictive Analytics in Fire Risk using Weather Data, State University of New York at Buffalo. ProQuest Dissertations Publishing."},{"key":"ref_148","first-page":"27","article-title":"Exploratory study on the based on big data for fire prevention of multiple shops","volume":"16","author":"Byungkwan","year":"2018","journal-title":"J. Ind. Converg."},{"key":"ref_149","unstructured":"Eckstein, D., K\u00fcnzel, V., Sch\u00e4fer, L., and Winges, M. (2020, December 17). Global Climate Risk Index, Germanwatch, Briefing Paper. Available online: https:\/\/www.germanwatch.org\/sites\/germanwatch.org\/files\/20-2-01e%20Global%20Climate%20Risk%20Index%202020_16.pdf."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1111\/j.1539-6924.2008.01035.x","article-title":"Insurance against Climate Change and Flooding in the Netherlands: Present, Future, and Comparison with Other Countries","volume":"28","author":"Botzen","year":"2008","journal-title":"Risk Anal."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1111\/dech.12367","article-title":"Insuring Climate Change: New Risks and the Financialization of Nature","volume":"49","author":"Keucheyan","year":"2018","journal-title":"Dev. Chang."},{"key":"ref_152","unstructured":"Zvedzdov, I., and Rath, S. (2020, December 17). Towards Socially Responsible (Re)Insurance Underwriting Practices: Readily Available \u2018Big Data\u2019 Contributions to Optimize Catastrophe Risk Management. Available online: https:\/\/ssrn.com\/abstract=2737508."},{"key":"ref_153","first-page":"173","article-title":"Big Data and Smallholder Farmers: Big Data Applications in the Agri-Food Supply Chain in Developing Countries","volume":"18","author":"Iuliia","year":"2016","journal-title":"Int. Food Agribus. Manag. Rev."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1057\/gpp.2016.9","article-title":"An insurance perspective on U.S. electric grid disruption costs","volume":"41","author":"Mills","year":"2016","journal-title":"Geneva Pap. Risk Insur. Issues Pract."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1057\/gpp.2014.19","article-title":"Insurability of Cyber Risk: An Empirical Analysis","volume":"40","author":"Biener","year":"2015","journal-title":"Geneva Pap. Risk Insur. Issues Pract."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1108\/JRF-09-2016-0122","article-title":"What do we know about cyber risk and cyber risk insurance?","volume":"17","author":"Eling","year":"2016","journal-title":"J. Risk Finance"},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Gai, K., Qiu, M., and Elnagdy, S.A. (2016, January 9\u201310). Security-Aware Information Classifications Using Supervised Learning for Cloud-Based Cyber Risk Management in Financial Big Data. Proceedings of the IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), New York, NY, USA.","DOI":"10.1109\/BigDataSecurity-HPSC-IDS.2016.66"},{"key":"ref_158","unstructured":"Shaw, R. (2020, November 25). The 10 Best Machine Learning Algorithms for Data Science Beginners. Available online: https:\/\/www.dataquest.io\/blog\/top-10-machine-learning-algorithms-for-beginners\/."},{"key":"ref_159","unstructured":"Shukla, P., Iriondo, R., and Chen, S. (2020, November 26). Machine Learning Algorithms for Beginners with Code Examples in Python, towards AI. Available online: https:\/\/medium.com\/towards-artificial-intelligence\/machine-learning-algorithms-for-beginners-with-python-code-examples-ml-19c6afd60daa."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/4\/4\/40\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:47:26Z","timestamp":1760179646000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/4\/4\/40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,19]]},"references-count":159,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["bdcc4040040"],"URL":"https:\/\/doi.org\/10.3390\/bdcc4040040","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,19]]}}}