Tracing the evolution of service robotics : Insights from a topic modeling approach

Acord transformatiu CRUE-CSICAltres ajuts: Helmholtz Association (HIRG-0069)Altres ajuts: Russian Science Foundation (RSF grant number 19-18-00262)Taking robotic patents between 1977 and 2017 and building upon the topic modeling technique, we extract their latent topics, analyze how important these topics are over time, and how they are related to each other looking at how often they are recombined in the same patents. This allows us to differentiate between more and less important technological trends in robotics based on their stage of diffusion and position in the space of knowledge represented by a topic graph, where some topics appear isolated while others are highly interconnected. Furthermore, utilizing external reference texts that characterize service robots from a technical perspective, we propose and apply a novel approach to match the constructed topics to service robotics. The matching procedure is based on frequency and exclusivity of words overlapping between the patents and the reference texts. We identify around 20 topics belonging to service robotics. Our results corroborate earlier findings, but also provide novel insights on the content and stage of development of application areas in service robotics. With this study we contribute to a better understanding of the highly dynamic field of robotics as well as to new practices of utilizing the topic modeling approach, matching the resulting topics to external classifications and applying to them metrics from graph theory

Tracing the evolution of service robotics : Insights from a topic modeling approach

Altres ajuts: Acord transformatiu CRUE-CSICAltres ajuts: Helmholtz Association (HIRG-0069)Altres ajuts: Russian Science Foundation (RSF grant number 19-18-00262)Taking robotic patents between 1977 and 2017 and building upon the topic modeling technique, we extract their latent topics, analyze how important these topics are over time, and how they are related to each other looking at how often they are recombined in the same patents. This allows us to differentiate between more and less important technological trends in robotics based on their stage of diffusion and position in the space of knowledge represented by a topic graph, where some topics appear isolated while others are highly interconnected. Furthermore, utilizing external reference texts that characterize service robots from a technical perspective, we propose and apply a novel approach to match the constructed topics to service robotics. The matching procedure is based on frequency and exclusivity of words overlapping between the patents and the reference texts. We identify around 20 topics belonging to service robotics. Our results corroborate earlier findings, but also provide novel insights on the content and stage of development of application areas in service robotics. With this study we contribute to a better understanding of the highly dynamic field of robotics as well as to new practices of utilizing the topic modeling approach, matching the resulting topics to external classifications and applying to them metrics from graph theory

Twenty years of technology and strategic roadmapping research: A school of thought perspective

© 2020 Two decades ago the thirtieth-anniversary special issue of Technological Forecasting and Social Change correctly anticipated the widespread adoption of technology and strategic roadmapping at firm, sectoral and national levels. In this article, we explore the evolution of roadmapping studies since that time. Drawing on a mixed-methods approach (i.e. topic modelling, genealogical analysis, content analysis and interviews), we reveal the development of seven distinctive ‘schools of thought’: the Cambridge practical school, the Seoul school, the Portland and Bangkok schools, the Cambridge phenomenological school, the Beijing school and the Moscow school. We show that the schools differ in terms of (a) the research orientation, whether it be solution- or theory-oriented; (b) the research methods and data sources being used; and (c) the nature of contributions that each school seeks to achieve. The different areas of emphasis associated with each school are not competing but complementary, and together they develop the eclectic body of knowledge on roadmapping

Novel data structure and visualization tool for studying technology evolution based on patent information: The DTFootprint and the TechSpectrogram

We introduce a new, bespoke data structure to analyze and visualize the evolution of a technology. The technology under analysis is defined by a set of patents corresponding to a technical field, owned by a company or invented by a team of research. Our data structure, the Dynamic Technology Footprint –DTFootprint–, facilitates the analysis and visualization of trends and dynamics of a given technology, and therefore the evolution of a technical field, a company, or a team of people. A graphical tool based on our data structure is defined, it is named Technology Spectrogram –TechSpectrogram–, because it is inspired by the acoustic frequency spectrograms: as the acoustic frequency spectrograms visualize the dynamics of an acoustic wave showing the evolution of its frequency components our tool shows the dynamics of a technology showing the evolution of its technological components, which are represented by the whole set of IPC-codes. Our graphical tool, the TechSpectrogram is shown for some study cases, and its application to the history of technology and technology management are disclosed

Augmented Reality and Health Informatics: A Study based on Bibliometric and Content Analysis of Scholarly Communication and Social Media

Healthcare outcomes have been shown to improve when technology is used as part of patient care. Health Informatics (HI) is a multidisciplinary study of the design, development, adoption, and application of IT-based innovations in healthcare services delivery, management, and planning. Augmented Reality (AR) is an emerging technology that enhances the user’s perception and interaction with the real world. This study aims to illuminate the intersection of the field of AR and HI. The domains of AR and HI by themselves are areas of significant research. However, there is a scarcity of research on augmented reality as it applies to health informatics. Given both scholarly research and social media communication having contributed to the domains of AR and HI, research methodologies of bibliometric and content analysis on scholarly research and social media communication were employed to investigate the salient features and research fronts of the field. The study used Scopus data (7360 scholarly publications) to identify the bibliometric features and to perform content analysis of the identified research. The Altmetric database (an aggregator of data sources) was used to determine the social media communication for this field. The findings from this study included Publication Volumes, Top Authors, Affiliations, Subject Areas and Geographical Locations from scholarly publications as well as from a social media perspective. The highest cited 200 documents were used to determine the research fronts in scholarly publications. Content Analysis techniques were employed on the publication abstracts as a secondary technique to determine the research themes of the field. The study found the research frontiers in the scholarly communication included emerging AR technologies such as tracking and computer vision along with Surgical and Learning applications. There was a commonality between social media and scholarly communication themes from an applications perspective. In addition, social media themes included applications of AR in Healthcare Delivery, Clinical Studies and Mental Disorders. Europe as a geographic region dominates the research field with 50% of the articles and North America and Asia tie for second with 20% each. Publication volumes show a steep upward slope indicating continued research. Social Media communication is still in its infancy in terms of data extraction, however aggregators like Altmetric are helping to enhance the outcomes. The findings from the study revealed that the frontier research in AR has made an impact in the surgical and learning applications of HI and has the potential for other applications as new technologies are adopted

A Method for the Detection and Characterization of Technology Fronts: Analysis of the Dynamics of Technological Change in 3D Printing Technology

This paper presents a method for the identification of the "technology fronts"-core technological solutions-underlying a certain broad technology, and the characterization of their change dynamics. We propose an approach based on the Latent Dirichlet Allocation (LDA) model combined with patent data analysis and text mining techniques for the identification and dynamic characterization of the main fronts where actual technological solutions are put into practice. 3D printing technology has been selected to put our method into practice for its market emergence and multidisciplinarity. The results show two highly relevant and specialized fronts strongly related with mechanical design that evolve gradually, in our opinion acting as enabling technologies. On the other side, we detected three fronts undergoing significant changes, namely layer-by-layer multimaterial manufacturing, data processing and stereolithograpy techniques. Laser and electron-beam based technologies take shape in the latter years and show signs of becoming enabling technologies in the future. The technology fronts and data revealed by our method have been convincing to experts and coincident with many technology trends already pointed out in technical reports and scientific literature

Forecasting the Subject Trend of International Library and Information Science Research by 2030 Using the Deep Learning Approach

This study seeks to forecast the subject trend of library and information science research until 2030 based on modeling previous research topics in this field, which has been done with a text mining and in-depth learning approach. After pre-processing and thematic classification of the studies, deep neural network algorithms were used to model previous studies and forecast future topics. The study population included 90,311 journal articles in library and information science publications indexed on the Web of Science website from 1945-2020. All research processes were implemented in the Python programming language. The findings showed that the largest number of studies in the future would be related to Internet and web studies, and the growth rate of these topics will be higher in the future. However, topics related to libraries and their work processes and other traditional disciplines such as theoretical foundations will have a lower growth rate in library and information science studies. As a result, knowledge of important future issues, while helping to plan for future research, can identify study gaps and investment opportunities in the R&D sector, thereby assisting researchers, universities, and relevant research institutes in selecting projects intelligently.https://dorl.net/dor/ 20.1001.1.20088302.2022.20.1.26.

Data science for industry 4.0 and sustainability: a survey and analysis based on open data

The last few years have been marked by the transition of companies and organizations to more efficient, productive and leaner practices in their processes and systems. In the spectrum of Industry and Engineering, the successful transition to Industry 4.0 is a clear goal for many Small and Medium Enterprises (SMEs) and bigger-sized companies. However, there are economic, social and environmental challenges for this transition that require innovative approaches to overcome them. The starting point for the development of this dissertation is exploring the importance of Data as a crucial resource and Data-science as a tool for companies, organizations and even public institutions to achieve innovative solutions through collaboration. As it will be further explained, Data is essential in decision making, but in many cases, organizations can’t access relevant information and tools because they are either proprietary or because there is a lack of collaboration between them and third parties. There is a common misconception that competition between companies within the same industry prohibits them from collaborating with each other. However, many times data-sharing and collaborative approaches can actually benefit both of them, increase the market they operate in, and accelerate innovation. Even though the adoption of Industry 4.0 has been already underway, this transition cannot be considered successful unless it improves sustainability across the economic, social and environmental areas of society. Those three sustainable pillars should always be considered a priority in the research of industrial and engineering evolution. Today, more than ever before, information about those topics is widely available but there is still a lack of interest by scientists and scholars in studying some of them. The following research aims to study Industry 4.0 and Sustainability themes through Data Science by incorporating open data and leveraging open-source tools in order to achieve Sustainable Industry 4.0. For that, studying the trends and current state of Industry 4.0, Sustainability and open data in the world, as well as identifying the industries, regions, and enterprises that benefit the most from Industry 4.0 adoption, and understanding if openness of data has a positive impact on Social Sustainability are the main objectives of the study. For that are used methods such as SLR (Sistematic Literature Review) in the bibliographic review and quantitative analysis through open-source software such as Python and R in the development of the research. The main results show a positive trend in Industry 4.0 adoption through sustainable practices, mainly on developed countries, and a growing trend of openness of data, which can be positive for transparency in both Industry and Sustainability.Os últimos anos têm sido marcados pela transição por parte de empresas e organizações para práticas mais eficientes, produtivas e de menores desperdícios nos seus processos e sistemas. No espectro da Indústria e Engenharia, a transição bem sucedida para a Indústria 4.0 é um objetivo claro por várias Pequenas e Médias Empresas (PMEs) e também por empresas maiores. No entanto, existem desafios de cariz económico, social e ambiental para esta transição, que requerem abordagens inovadoras para que os mesmos sejam ultrapassados. O ponto de partida para o desenvolvimento desta dissertação passou por explorar a importância de Dados como um recurso crucial e da Ciência de Dados como uma ferramenta para empresas, organizações e até mesmo instituições públicas atingirem soluções inovadoras através de colaboração. Como será explicado ao longo da dissertação, os dados são essenciais em tomadas de decisão, mas em muitos casos, as organizações não conseguem aceder a informação ou ferramentas relevantes porque ou são proprietárias, ou porque existe a falta de colaboração entre elas e terceiros. Existe também o conceito errado de que a competição entre empresas numa dada indústria as proíbe de colaborarem entre si. No entanto, muitas vezes a partilha de informação e abordagens colaborativas podem, na verdade, beneficiar ambas, expandindo o mercado onde operam e acelerando inovação. Apesar da adoção da Indústria 4.0 estar em progresso, esta transição não pode ser considerada bem sucedida se não melhorar a sustentabilidade nas áreas económicas, sociais e ambientais da sociedade. Esses três pilares da sustentabilidade devem ser considerados uma prioridade no estudo da evolução industrial e da engenharia. Hoje, mais do que nunca, a informação acerca desses tópicos é facilmente acedida, mas continua a existir interesse por parte de cientistas e académicos no estudo de alguns deles. A presente pesquisa tenciona estudar a Indústria 4.0 e temas de Sustentabilidade através de Ciência de Dados, incorporando dados abertos e explorando ferramentas open-source, para contribuir para uma Indústria 4.0 Sustentável. Para tal, estudar a tendência e estado atual da Indústria 4.0, Sustentabilidade e abertura de dados no mundo, assim como identificar as indústrias, regiões e empresas que mais beneficiam desta adoção, e finalmente compreender se uma maior abertura de dados pode ter um impacto positivo na Sustentabilidade Social são os principais objetivos do estudo. Assim, são usados métodos como RSL (Revisão Sistemática da Literatura) na revisão bibliográfica e análise quantitativa através de software open-source como o Python e R nos capítulos de desenvolvimento. Os principais resultados mostram uma tendência positiva na adoção da Indústria 4.0 através de praticas sustentáveis, principalmente em países desenvolvidos, e uma tendência crescente na abertura de dados, que pode ser positiva para uma indústria mais sustentável e transparente

Tracing the Evolution of Service Robotics: Insights from a Topic Modeling Approach

Taking robotic patents between 1977 and 2017 and building upon the topic modeling technique, we extract their latent topics, analyze how important these topics are over time, and how they are related to each other looking at how often they are recombined in the same patents. This allows us to differentiate between more and less important technological trends in robotics based on their stage of diffusion and position in the space of knowledge represented by a topic graph, where some topics appear isolated while others are highly interconnected. Furthermore, utilizing external reference texts that characterize service robots from a technical perspective, we propose and apply a novel approach to match the constructed topics to service robotics. The matching procedure is based on frequency and exclusivity of words overlapping between the patents and the reference texts. We identify around 20 topics belonging to service robotics. Our results corroborate earlier findings, but also provide novel insights on the content and stage of development of application areas in service robotics. With this study we contribute to a better understanding of the highly dynamic field of robotics as well as to new practices of utilizing the topic modeling approach, matching the resulting topics to external classifications and applying to them metrics from graph theory. © 2021 The Author(s).Financial support from the Helmholtz Association (HIRG-0069) is gratefully acknowledged. Ivan Savin acknowledges support from the Russian Science Foundation [RSF grant number 19-18-00262]. This work has benefited from presentations at workshops in Kiel, Strasbourg, Karlsruhe, the EMAEE conference in Brighton and the EAEPE conference in Bilbao. All remaining shortcomings are our responsibility