New and emerging forms of data and technologies: literature and bibliometric review

Abstract

With the increased digitalisation of our society, new and emerging forms of data present new values and opportunities for improved data driven multimedia services, or even new solutions for managing future global pandemics (i.e., Disease X). This article conducts a literature review and bibliometric analysis of existing research records on new and emerging forms of multimedia data. The literature review engages with qualitative search of the most prominent journal and conference publications on this topic. The bibliometric analysis engages with statistical software (i.e. R) analysis of Web of Science data records. The results are somewhat unexpected. Despite the special relationship between the US and the UK, there is not much evidence of collaboration in research on this topic. Similarly, despite the negative media publicity on the current relationship between the US and China (and the US sanctions on China), the research on this topic seems to be growing strong. However, it would be interesting to repeat this exercise after a few years and compare the results. It is possible that the effect of the current US sanctions on China has not taken its full effect yet.

How containment policy and medical service impact COVID-19 transmission: A cross-national comparison among China, the USA, and Sweden

“…As the study of COVID-19 transmission patterns requires data from multiple sources, new and emerging forms of data (NEFD), such as spatiotemporal and open data, could give us data support [ 38 ]. As an open database, the program “COVID-19 Data Repository by the Center for System Science and Engineering (CSSE) at Johns Hopkins University [ 2 ]" provides daily updated infected data of COVID-19.…”

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

“…In addition, new and emerging forms of data have been witnessed, for example, social media data have been used as an alternative to traditional survey data and used to identify peoples' opinions and trends in priorities and concerns about emerging food risks. Moreover, various types of multimedia data (e.g., transaction, registration, tracking, and images) have been combined in making AI models (Radanliev & De Roure, 2023). Tao et al (2020) reviewed the data sources (mainstream news media, government websites, specialty blogs, social media platforms like Twitter, Facebook, and Instagram), computational methods, and applications of text data in food industry and showed that application of text data analysis can be beneficial for improving food safety and food fraud surveillance by checking different types of information for trends and patterns, such as food safety and fraud surveillance, dietary patterns, consumer-opinions, new-product development, and feedback to online food services.…”

“…In addition, new and emerging forms of data have been witnessed, for example, social media data have been used as an alternative to traditional survey data and used to identify peoples' opinions and trends in priorities and concerns about emerging food risks. Moreover, various types of multimedia data (e.g., transaction, registration, tracking, and images) have been combined in making AI models (Radanliev & De Roure, 2023). Tao et al.…”

A Stethoscope for Drones: Transformers-Based Methods for UAVs Acoustic Anomaly Detection

“…The existence of AI in low memory devices such as UAVs and drones is not new; that is, in [50] for instance, was introduced a survey of methodologies that combines deep learning and data science algorithms (e.g., statistics, linear regression, Bayesian methods). Another review was introduced in [51] and discusses new and emerging forms of data and technologies which seems to be a new field for future developments on AI, as well as in [52] that presented a method for the conceptualization of healthcare system that is supported by autonomous AI devices (such as drones or UAVs) that can use edge health devices with real-time data. As the AI field progresses more and more from a complex architecture standpoint, in this paper we use a Transformer-based architecture (explained thoroughly next in this section), in order to avoid the main LSTM three drawbacks.…”