  Massive public resume data emerging on the WWW indicates individual-related
characteristics in terms of profile and career experiences. Resume Analysis
(RA) provides opportunities for many applications, such as talent seeking and
evaluation. Existing RA studies based on statistical analyzing have primarily
focused on talent recruitment by identifying explicit attributes. However, they
failed to discover the implicit semantic information, i.e., individual career
progress patterns and social-relations, which are vital to comprehensive
understanding of career development. Besides, how to visualize them for better
human cognition is also challenging. To tackle these issues, we propose a
visual analytics system ResumeVis to mine and visualize resume data. Firstly, a
text-mining based approach is presented to extract semantic information. Then,
a set of visualizations are devised to represent the semantic information in
multiple perspectives. By interactive exploration on ResumeVis performed by
domain experts, the following tasks can be accomplished: to trace individual
career evolving trajectory; to mine latent social-relations among individuals;
and to hold the full picture of massive resumes' collective mobility. Case
studies with over 2500 online officer resumes demonstrate the effectiveness of
our system. We provide a demonstration video.
