Human-in-the-loop to Fine-tune Data in Real Time
Marta Mattoso
COPPE/UFRJ, Rio de Janeiro
14 december 2017, 11h
Room 1/124, Bat.5
In long-lasting exploratory executions, it is often needed to fine-tune several parameters of complex computational models, because they may significantly impact performance. Listing all possible combinations of parameters and exhaustively trying them all is nearly impossible even in high performance computers. Because of the exploratory nature of those computations, it is hard to determine, before the execution, which parameters and which values will work best to validate the initial hypothesis, even for the most experienced users. For this reason, after the initial setups, the user starts the computation and fine-tunes specific parameters based on online intermediate data analysis. In this talk we present the challenges in supporting the user with data analysis to monitor, evaluate and adjust executions in real time. One of the problems in these executions is that, after some hours, the users can lose track of what has been tuned at early execution stages if the adaptations are not properly registered. We discuss on using techniques from provenance data management and human-in-the-loop to address the problem of adapting and tracking online parameter fine-tuning in several applications.
