Paper Accepted at the International Conference on Decision Aid Science and Applications (DASA 2020)

Our paper entitled “Visual Trend Analytics for Decision Making” is accepted as full paper at the International Conference on Decision Aid Science and Applications (DASA 2020) . The International Conference on Decision Aid Sciences and Applications is an interdisciplinary forum for the presentation of recent developments and applications in the field of Decision Aid Sciences. This Conference aims to disseminate recent models and techniques related to decision making and decision processes through researchers and practitioners from all over the world. There will be rigorous plenary talks by invited speakers as well as contributed talks. A Workshop for postgraduate students at the early stage of their dissertation research will be organized during the conference and will include a variety of panels as well as, practical sessions on developing dissertation proposals, launching academic careers, and a meet-the-editors session.

Paper: Visual Trend Analytics for Decision Making

Abstract:
Strategic foresight, corporate foresight and technology management enables firms detecting discontinuous changes in an early stage and develop future courses for a more sophisticated market positioning. The enhancements in machine learning and artificial intelligence allow a more automatic detection of early trends to create such future courses and make strategic decisions. Visual Analytics and in particular Visual Trend Analytics combine methods of automated data analysis through machine learning methods and interactive visualizations and enable a far better way to gather insights from vast amount of data to make strategic decision. While Visual Analytics got various models and approaches to enable strategic decision making, the analysis of trend is still a matter of research. The forecasting approaches and involvement of human in the visual trend analysis process require further investigation that will lead to sophisticated analytical approaches. We introduce in this paper a novel model of Visual Trend Analytics for decision making in particular for corporate foresight through early trends from scientific publications.