Visual analytics for technology and innovation management

The awareness of emerging trends is essential for strategic decision making because technological trends can affect a firm’s competitiveness and market position. The rise of artificial intelligence methods allows gathering new insights and may support these decision-making processes. However, it is essential to keep the human in the loop of these complex analytical tasks, which, often lack an appropriate interaction design. Including special interactive designs for technology and innovation management is therefore essential for successfully analyzing emerging trends and using this information for strategic decision making. A combination of information visualization, trend mining and interaction design can support human users to explore, detect, and identify such trends. This paper enhances and extends a previously published first approach for integrating, enriching, mining, analyzing, identifying, and visualizing emerging trends for technology and innovation management. We introduce a novel interaction design by investigating the main ideas from technology and innovation management and enable a more appropriate interaction approach for technology foresight and innovation detection.

Interdependence of mining industry and industrial sectors in Slovakia

Due to globalization, all industrial sectors are mutually connected, which means that the sectors are part of the global economy, influenced by a similar trend. Mining relates to various industrial sectors. The base of mineral raw materials provides in the frame of certain regions the value that is possible to evaluate with optimal use complexly, and by this way profit for the owner, state and mining company, as well as single region is created. The goal of the presented contribution is to search for a position of raw material using in industries and its trend from the view of growth rate, in connection to the growth of other industries with the aim to provide long-term prosperity and contribution of industries to the national economy. Research had been done through growth rate of sales and revenues and one-way analysis of sales trend in analyzed period 2009-2018. The research is supported by initial decline analysis, orientated to the evaluation of positive and negatives of raw material using generally. According to the internal analysis, there are selected sectors directly connected to the mining industry. The results show there is the stable or improving character of the mining industry, which is very similar to industrial production, having influence with other industrial sectors. There is a recorded trend in the growth of individual industries, which varies significantly. The results of the contribution are useful in providing long-term prosperity and contribution of industries to the national economy, providing sustainable economic growth. The contribution is limited to the evaluation of industries growth from the view of sales and revenues, and future research can be extended to the evaluation from the view of market share, etc. Future research can also be extended to other macro-economic indicators, influencing the competitiveness of the mining industry and its sustainable tendency.

Case Study of Trend Mining in Transportation Research Record Articles

This study employs two topic models to perform trend mining on an abundance of textual data to determine trends in research topics from immense collections of unstructured documents over the years. This study collected data from the titles and abstracts of the papers published in Transportation Research Record: Journal of the Transportation Research Board, since 1974. The content of these papers was ideal for examining research trends in various fields of research because it contains large textual data. In previous studies, exploratory analysis tools such as text mining were used to provide descriptive information about the data. However, this method does not provide researchers with quantifications of the topics and their correlations. Furthermore, the contents examined in this study are largely unstructured, and therefore they require faster machine learning algorithms to decipher them. For these reasons, the research team chose to employ two topic modeling tools, latent Dirichlet allocation and structural topic model, to perform trend mining. This analysis succeeded in extracting 20 main topics, identified by keywords, from the data. The research team also developed two interactive topic model visualization tools that can be used to extract topics from journal titles and abstracts, respectively. The findings from this study provide researchers with a further understanding of research patterns within ever-evolving area of transportation engineering studies.

Overcoming Language Barriers Through an Interactive Platform for Public Engagement

The article discusses the framework for a city visioning platform which can offer a public participation in energy-related actions and support the social acceptance of energy transition. The platform has a dual feature: a. the interactivity of the platform is based on crowd-sourcing tools, open linked data approach, trend mining and scenario building tools to address the gaps in urban planning for energy supply, traffic management and governance practices that have been criticized for being exclusive, top-down and short sighted; b. the platform will act as an intercultural & linguistic mediator by offering the opportunity to the community to interact with people from different cultures in all European languages, stimulation of interest and critical thinking, the opportunity to engage in constructive dialogues and projects, capitalizing on the skills and creativity of the participants.