Visual Collaboration - An Approach for Visual Analytical Collaborative Research

Blazevic, Midhad; Sina, Lennart B.; Nazemi, Kawa

doi:10.1109/iv56949.2022.00057

Cited by 5 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 4 shows an alternative version of the UI focusing on visual analytics. The calculations and backend are the same; however, the frontend that is based on our previous works [8,28] grants the user the ability to explore the results better. The user has various filtering options seen in (2) while at the same time having a listed view seen in ( 1) that shows more information than Figure 3.…”

Section: Results Presentationmentioning

confidence: 99%

See 1 more Smart Citation

Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System

et al. 2023

Self Cite

View full text Add to dashboard Cite

Studies have shown that although having more information improves the quality of decision-making, information overload causes adverse effects on decision quality. Visual analytics and recommendation systems counter this adverse effect on decision-making. Accurately identifying relevant information can reduce the noise during exploration and improve decision-making. These countermeasures also help scientists make correct decisions during research. We present a novel and intuitive approach that supports real-time collaboration. In this paper, we instantiate our approach to scientific writing and propose a system that supports scientists. The proposed system analyzes text as it is being written and recommends similar publications based on the written text through similarity algorithms. By analyzing text as it is being written, it is possible to provide targeted real-time recommendations to improve decision-making during research by finding relevant publications that might not have been otherwise found in the initial research phase. This approach allows the recommendations to evolve throughout the writing process, as recommendations begin on a paragraph-based level and progress throughout the entire written text. This approach yields various possible use cases discussed in our work. Furthermore, the recommendations are presented in a visual analytics system to further improve scientists’ decision-making capabilities.

show abstract

Section: Results Presentationmentioning

confidence: 99%

“…We briefly described using a visual system as an alternative view of the results. Visual analytics systems yield various benefits we described in our previous work [28] that impact decision-making and exploration. However, further studies must be made into our approach's impact with various frontend versions.…”

Section: Discussionmentioning

confidence: 99%

Recommendation of Scientific Publications—A Real-Time Text Analysis and Publication Recommendation System

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…The adaption process requires time and thought as it is necessary to provide meaningful recommendations that do not harm users' cognition. As described in our previous works [39,40,59], the user should not have cognition overload due to bad interaction design while being cognitively within the research process on our platform. The usability study shows deficits in our current interaction design that need to be addressed, such as a suboptimal abstract presentation, a lack of loading icons and underlying speed issues, a lack of navigation bars showing users where they are currently in the ideation process, and missing buttons and interaction functionality.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Ideation Analyzer and Information Recommender

Blazevic,

Sina,

Secco

et al. 2024

Electronics

Self Cite

View full text Add to dashboard Cite

The benefits of ideation for both industry and academia alike have been outlined by countless studies, leading to research into various approaches attempting to add new ideation methods or examine how the quality of the ideas and solutions created can be measured. Although AI-based approaches are being researched, there is no attempt to provide the ideation participants with information that inspire new ideas and solutions in real time. Our proposal presents a novel and intuitive approach that supports users in real time by providing them with relevant information as they conduct ideation. By analyzing their ideas within the respective ideation sessions, our approach recommends items of interest with high contextual similarity to the proposed ideas, allowing users to skim through, for example, publications and inspire new ideas quickly. The recommendations also evolve in real time. As more ideas are written during the ideation session, the recommendations become more precise. This real-time approach is instantiated with various ideation methods as a proof of concept, and various models are evaluated and compared to identify the best model for working with ideas.

show abstract