Combining discovery and augmentation is important in the era of data usage when it comes to predicting the outcome of tasks. However, having to ask the user the utility function to discover the goal to achieve the optimal small rightful dataset is not an optimal solution. The existing solutions do not make good use of this combination, hence underutilizing the data. In this paper, we introduce a novel goal-oriented framework, called BOD: Blindly Optimal Data Discovery, that involves humans in the loop and comparing utility scores every time querying in the process without knowing the utility function. This establishes the promise of using BOD: Blindly Optimal Data Discovery for modern data science solutions.

翻译：在数据使用时代，结合发现与增强对于预测任务结果至关重要。然而，要求用户提供效用函数以发现目标、从而获取最优小型正确数据集并非最佳解决方案。现有方法未能充分利用这一结合，导致数据利用不足。本文提出一种新颖的面向目标的框架，称为BOD：盲优数据发现。该框架将人类纳入循环，在每次查询过程中无需知晓效用函数即可比较效用分数。这确立了使用BOD：盲优数据发现于现代数据科学解决方案的前景。