BOD: Blindly Optimal Data Discovery

TL;DR

BOD introduces a goal-oriented data discovery framework that involves human input and utility score comparisons without prior knowledge of the utility function, aiming to optimize data selection for predictive tasks.

Contribution

It proposes a novel framework that integrates human-in-the-loop and utility comparisons to improve data discovery without requiring utility function knowledge.

Findings

Demonstrates the effectiveness of BOD in data discovery tasks.

Shows improved data utilization over existing methods.

Validates the approach in modern data science scenarios.

Abstract

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.