Proactive Support for Large-Scale Data Exploration
Mark Hereld, Tanu Malik, Venkatram Vishwanath · 2013
Computational science is generating increasingly unwieldy datasets created by complex and high-resolution simulations of physical, social, and economic systems. Traditional post processing of such large datasets requires high bandwidth to large storage resources. In situ processing approaches can reduce I/O requirements but steal processing cycles from the simulation and forsake interactive data exploration. The Fusion project aims to develop a new approach for exploring large-scale scientific datasets wherein the system actively assists the user in the data exploration process. A key component of the system is a software assistant that evaluates the stated and implied analysis goals of the scientist, observes the environment, models and proposes actions to be taken, and orchestrates the generation of analysis and visualization products for the user. These products are managed and made available to the scientist through an interactive space consisting of a database and a visual interface. The scientist sifts through and explores the available analysis products while indicating preferences that are translated into goals, completing the feedback loop that steers the assistant in its future actions.