Abstract
Data crisis ubiquitously arises in solving complex scientific problems. It has motivated us to develop a Collaborative Visualization Environment (CVE), which provides the users with the “serendipity” with the aid of effective data-centric tweaking of visualization-related parameters. To this end, we have proposed the concept of Volume Data Mining (VDM), which takes full advantage of knowledge in the field of differential topology to allow the users to explore the global structures and local features of target 4D volumes. In this paper, the effectiveness of our current CVE framework with several VDM tools is illustrated with applications to practical scientific simulation datasets.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
