Adaptive Data Communication Interface: A User-Centric Visual Data Interpretation Framework
It addresses the problem of making data visualization more user-friendly for data owners, but it is incremental as it builds on existing techniques without new breakthroughs.
The paper proposes a framework for an adaptive interface that uses AI to recommend visualizations from large datasets to help users interpret their data, but it presents only ideas without concrete results or numbers.
In this position paper, we present ideas about creating a next generation framework towards an adaptive interface for data communication and visualisation systems. Our objective is to develop a system that accepts large data sets as inputs and provides user-centric, meaningful visual information to assist owners to make sense of their data collection. The proposed framework comprises four stages: (i) the knowledge base compilation, where we search and collect existing state-ofthe-art visualisation techniques per domain and user preferences; (ii) the development of the learning and inference system, where we apply artificial intelligence techniques to learn, predict and recommend new graphic interpretations (iii) results evaluation; and (iv) reinforcement and adaptation, where valid outputs are stored in our knowledge base and the system is iteratively tuned to address new demands. These stages, as well as our overall vision, limitations and possible challenges are introduced in this article. We also discuss further extensions of this framework for other knowledge discovery tasks.