Smart Homes and Families to Enable Sustainable Societies: A Data-Driven Approach for Multi-Perspective Parameter Discovery Using BERT Modelling

Abstract

Homes are the building block of cities and societies and therefore smart homes are critical to establishing smart living and are expected to play a key role in enabling smart, sustainable cities and societies. The current literature on smart homes has mainly focused on developing smart functions for homes such as security and ambiance management. Homes are composed of families and are inherently complex phenomena underlined by humans and their relationships with each other, subject to individual, intragroup, intergroup, and intercommunity goals. There is a clear need to understand, define, consolidate existing research, and actualize the overarching roles of smart homes, and the roles of smart homes that will serve the needs of future smart cities and societies. This paper introduces our data-driven parameter discovery methodology and uses it to provide, for the first time, an extensive, fairly comprehensive, analysis of the families and homes landscape seen through the eyes of academics and the public, using over a hundred thousand research papers and nearly a million tweets. We developed a methodology using deep learning, natural language processing (NLP), and big data analytics methods (BERT and other machine learning methods) and applied it to automatically discover parameters that capture a comprehensive knowledge and design space of smart families and homes comprising social, political, economic, environmental, and other dimensions. The 66 discovered parameters and the knowledge space comprising 100 s of dimensions are explained by reviewing and referencing over 300 articles from the academic literature and tweets. The knowledge and parameters discovered in this paper can be used to develop a holistic understanding of matters related to families and homes facilitating the development of better, community-specific policies, technologies, solutions, and industries for families and homes, leading to strengthening families and homes, and in turn, empowering sustainable societies across the globe.

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

“…The work presented in this paper is the beginning, many more works are needed to investigate the potential of social media for forensic and other purposes. This research is part of our broader work on data-driven parameter discovery from Twitter and other data sources applied previously to different research areas including the education sector in KSA during COVID-19 [ 35 ], the discovery of cancer-related healthcare services [ 71 ], families and smart homes [ 72 ], transportation [ 73 ], tourism [ 74 ], multi-generational labor markets [ 75 , 76 ], and COVID-19 governance measures [ 77 ].…”

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

“…The paper belongs to our extensive research on utilizing Information and Communication Technology (ICT) to tackle issues in smart cities and societies. Our work encompasses deep journalism [39,255], labor economics [256], transportation [39], smart families and homes [40], healthcare [50,257], education during COVID-19 [52], and event detection [55]. In the future, we aim to enhance the methodology in this paper through advanced deep learning techniques and apply them to enhancing tourism and other societal, economic, environmental, and cultural issues.…”

“…Bibliometric and scientometric analysis of scientific literature has been used as an approach to analyse existing research in different areas such as finance [36], construction industry [37], transportation [38,39], smart homes [40], artificial intelligence [41,42], and others. Scientists have also employed scientometric analysis in tourism.…”

“…Data from digital and social media including Twitter has been widely used with textual analytics to study various phenomena and problems in different application domains such as in healthcare [49,50], disasters [51], smart homes [40], education [52], COVID-19related studies [53,54], event detection [55,56] opinion mining about government services [57], city logistics [58], [59], and transportation [39]. Likewise, in the tourism sector, several works involving textual analysis of Twitter data have been reported.…”

Data Locality in High Performance Computing, Big Data, and Converged Systems: An Analysis of the Cutting Edge and A Future System Architecture

“…Data has grown exponentially during the last decade giving rise to the big data phenomenon [1], [2]. Big data has revolutionised science and technology leading to innovations in many sectors including urbanisation [3], transport [4], energy [5], healthcare [6], education [7], economics [8], smart societies [9], computing infrastructure [10], and more; see e.g., [1], [11], for details on big data technologies and applications. The paramount contribution of big data is the development of contemporary data-driven machine and Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).…”