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，结合数据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最优小型合法数据集的目标并非最优解。现有方案未能充分利用这种组合，导致数据利用不足。本文提出一种名为"BOD：盲最优数据发现"的新型目标导向框架，该框架在不知晓效用函数的情况下，通过引入人工反馈机制，在每次查询过程中比较效用得分。这确立了BOD：盲最优数据发现方法在现代数据科学解决方案中的应用前景。