Team awarded NSF Big Data grant

BIGDATA: F: DKA: Usable Big Data Analytics via Multi-Scale Visual to Parametric Interaction (MV2PI)

Gaining big insight from big data requires big analytics, which poses big usability problems. In this proposal, human-computer-interaction research is merged with complex statistical methods and fast computation to make big data analytics usable and accessible to professional and student users. Multi-scale Visual-to-Parametric Interaction (MV2PI) enables analysts to simultaneously steer multiple algorithmic models across a continuum of data scales, from large data clouds to small working sets, by interacting directly with visualized data to adjust complex model parameters.

Chris North (CS)
Yong Cao (CS)
Leanna House (Statistics)
Scotland Leman (Statistics)

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