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Browsing International Business by Subject "Big Data"
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Item Dealing with randomness and concept drift in large datasets(MDPI AG, 2021-07) Mwitondi, Kassim S.; Said, Raed A.Data-driven solutions to societal challenges continue to bring new dimensions to our daily lives. For example, while good-quality education is a well-acknowledged foundation of sustainable development, innovation and creativity, variations in student attainment and general performance remain commonplace. Developing data-driven solutions hinges on two fronts-technical and appli-cation. The former relates to the modelling perspective, where two of the major challenges are the impact of data randomness and general variations in definitions, typically referred to as concept drift in machine learning. The latter relates to devising data-driven solutions to address real-life challenges such as identifying potential triggers of pedagogical performance, which aligns with the Sustainable Development Goal (SDG) #4-Quality Education. A total of 3145 pedagogical data points were obtained from the central data collection platform for the United Arab Emirates (UAE) Ministry of Education (MoE). Using simple data visualisation and machine learning techniques via a generic algorithm for sampling, measuring and assessing, the paper highlights research pathways for educa-tionists and data scientists to attain unified goals in an interdisciplinary context. Its novelty derives from embedded capacity to address data randomness and concept drift by minimising modelling variations and yielding consistent results across samples. Results show that intricate relationships among data attributes describe the invariant conditions that practitioners in the two overlapping fields of data science and education must identify. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Item A framework for data-driven solutions with covid-19 illustrations(Ubiquity Press, 2021) Mwitondi, Kassim S.; Said, Raed A.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.