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Chen, Chaomei

Publications and source records attributed to Chen, Chaomei.

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Big, Deep, and Smart Data in Scanning Probe Microscopy

Scanning probe microscopy techniques open the door to nanoscience and nanotechnology by enabling imaging and manipulation of structure and functionality of matter on nanometer and atomic scales. We analyze the discovery process by SPM in terms of information flow from tip-surface junction to the knowledge adoption by scientific community. Furthermore, we discuss the challenges and opportunities offered by merging of SPM and advanced data mining, visual analytics, and knowledge discovery technologies.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2012

Top Ten Interaction Challenges in Extreme-Scale Visual Analytics

The chapter presents ten selected user interfaces and interaction challenges in extreme-scale visual analytics. The study of visual analytics is often referred to as 'the science of analytical reasoning facilitated by interactive visual interfaces' in the literature. The discussion focuses on the issues of applying visual analytics technologies to extreme-scale scientific and non-scientific data ranging from petabyte to exabyte in sizes. The ten challenges are: in situ interactive analysis, user-driven data reduction, scalability and multi-level hierarchy, representation of evidence and uncertainty, heterogeneous data fusion, data summarization and triage for interactive query, analytics of temporally evolving features, the human bottleneck, design and engineering development, and the Renaissance of conventional wisdom. The discussion addresses concerns that arise from different areas of hardware, software, computation, algorithms, and human factors. The chapter also evaluates the likelihood of success in meeting these challenges in the near future.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

Top Ten Challenges in Extreme-Scale Visual Analytics

In the current special issue of IEEE Computer Graphics and Applications (CG&A), researchers share their research and development (R&D) findings and results on applying visual analytics to extreme-scale data. Having surveyed the special issue articles and other related R&D efforts in the area, we have identified what we consider to be the top challenges of extreme-scale visual analytics. To cater to the diverse readership of CG&A, our discussion evaluates challenges in all areas of the field, including algorithms, hardware, software, engineering, and social issues.

99 GENERAL AND MISCELLANEOUS↗