Personalizing data visualization and interaction

Information visualization is one of the major approach to analyse data. Though there are lot of visualization techniques, designing an adaptive visualization technique for different user and task characteristics is challenging. In this dissertation we are comparing different visualization techniques to find an optimal way for authoring, displaying datasets for two case studies - a crowd sourcing platform for people with different range of abilities and a sensor dashboard for a smart manufacturing set up. We also aspire to develop a user adaptive visualization system. A pilot study found that for numeric dataset, a Bar graph has maximum correct response and Area graph has lowest response time.