Unifying telescope and microscope: A multi-lens framework with open data for modeling emerging events |
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| Institution: | 1. AGH University of Science and Technology, 30 Mickiewicza Ave, Kraków 30-059, Poland;2. VSB Technical University of Ostrava, 17. listopadu 2172/15, Ostrava-Poruba 708 00, Czech Republic;1. School of Information and Communication Engineering, Hunan Institute of Science and Technology, Hunan, China;2. Machine Vision & Artificial Intelligence Research Center, Hunan Institute of Science and Technology, Hunan, China;1. School of Economics and Management, Chang''an University, Xi''an 710064, China;2. Computer & Information Sciences Department, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia;3. Institute of IR4.0, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia;4. College of Engineering, Al Ain University, Al Ain, United Arab Emirates;5. Department of Mathematics, College of Science, Tafila Technical University, Tafila, Jordan |
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| Abstract: | Open data is becoming ubiquitous as governments, companies, and even individuals have the option to offer more or less unrestricted access to their non-sensitive data. The benefits of open data, such as accessibility and transparency, have motivated and enabled a large number of research studies and applications in both academia and industry. However, each open data only offers a single perspective, and its potential inherent limitations (e.g., demographic biases) may lead to poor decisions and misjudgments. This paper discusses how to create and use multiple digital lenses empowered by open data, including census data (macro lens), search logs (meso lens), and social data (micro lens), to investigate general real-world events. To reveal the unique angles and perspectives brought by each open lens, we summarize and compare the underpinning open data from eleven dimensions, such as utility, data volume, dynamic variability, and demographic fairness. Then, we propose an easy-to-use and generalized open data driven framework, which automatically retrieves multi-source data, extracts features, and trains machine learning models for the event specified by answering what, when, and where questions. With low labor efforts, the framework’s generalization and automation capabilities guarantee an instant investigation of general events and phenomena, such as disasters, sports events, and political activities. We also conduct two case studies, i.e., the COVID-19 pandemic and Great American Eclipse (see Appendix), to demonstrate its feasibility and effectiveness at different time granularities. |
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| Keywords: | Open data Data retrieval Data fusion Model fusion Google Trends Twitter |
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