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{"title":"A Framework for Data-Driven Solutions with COVID-19 Illustrations","authors":"Kassim S. Mwitondi, Raed A. Said","doi":"10.5334/dsj-2021-036","DOIUrl":null,"url":null,"abstract":"Data–driven solutions have long been keenly sought after as tools for driving the world’s fast changing business environment, with business leaders seeking to enhance decision making processes within their organisations. In the current era of Big Data, applications of data tools in addressing global, regional and national challenges have steadily grown in almost all fields across the globe. However, working in silos has continued to impede research progress, creating knowledge gaps and challenges across geographical borders, legislations, sectors and fields. There are many examples of the challenges the world faces in tackling global issues, including the complex interactions of the 17 Sustainable Development Goals (SDG) and the spatio–temporal variations of the impact of the on-going COVID–19 pandemic. Both challenges can be seen as non–orthogonal, strongly correlated and requiring an interdisciplinary approach to address. We present a generic framework for filling such gaps, based on two data-driven algorithms that combine data, machine learning and interdisciplinarity to bridge societal knowledge gaps. The novelty of the algorithms derives from their robust built–in mechanics for handling data randomness. Animation applications on structured COVID–19 related data obtained from the European Centre for Disease Prevention and Control (ECDC) and the UK Office of National Statistics exhibit great potentials for decision-support systems. Predictive findings are based on unstructured data–a large COVID–19 X–Ray data, 3181 image files, obtained from GitHub and Kaggle. Our results exhibit consistent performance across samples, resonating with cross-disciplinary discussions on novel paths for data-driven interdisciplinary research. © 2021, Ubiquity Press. All rights reserved.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5334/dsj-2021-036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 2
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带有COVID-19插图的数据驱动解决方案框架
长期以来,数据驱动的解决方案一直备受追捧,因为它是推动全球快速变化的商业环境的工具,商业领袖们也在寻求加强组织内的决策流程。在大数据时代,在全球几乎所有领域,数据工具在应对全球、区域和国家挑战方面的应用都在稳步增长。然而,竖井式的工作继续阻碍着研究进展,造成了跨越地理边界、立法、部门和领域的知识差距和挑战。世界在解决全球性问题时面临的挑战有很多例子,包括17项可持续发展目标之间复杂的相互作用,以及正在发生的COVID-19大流行影响的时空变化。这两个挑战可以被视为非正交的,强烈相关的,需要跨学科的方法来解决。我们提出了一个填补这些空白的通用框架,基于两种数据驱动的算法,将数据、机器学习和跨学科结合起来,以弥合社会知识空白。这些算法的新颖之处在于它们处理数据随机性的强大内置机制。从欧洲疾病预防控制中心(ECDC)和英国国家统计局获得的结构化COVID-19相关数据的动画应用显示出决策支持系统的巨大潜力。预测结果基于非结构化数据-从GitHub和Kaggle获得的大型COVID-19 x射线数据,3181个图像文件。我们的研究结果在不同的样本中表现出一致的表现,与数据驱动的跨学科研究的新路径的跨学科讨论产生共鸣。©2021,Ubiquity出版社。版权所有。
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