Modern dataset search platforms employ ML task-based utility metrics instead of relying on metadata-based keywords to comb through extensive dataset repositories. In this setup, requesters provide an initial dataset, and the platform identifies complementary datasets to augment (join or union) the requester's dataset such that the ML model (e.g., linear regression) performance is improved most. Although effective, current task-based data searches are stymied by (1) high latency which deters users, (2) privacy concerns for regulatory standards, and (3) low data quality which provides low utility. We introduce Mileena, a fast, private, and high-quality task-based dataset search platform. At its heart, Mileena is built on pre-computed semi-ring sketches for efficient ML training and evaluation. Based on semi-ring, we develop a novel Factorized Privacy Mechanism that makes the search differentially private and scales to arbitrary corpus sizes and numbers of requests without major quality degradation. We also demonstrate the early promise in using LLM-based agents for automatic data transformation and applying semi-rings to support causal discovery and treatment effect estimation.

翻译：现代数据库搜索平台采用基于机器学习任务的效用指标，而非依赖基于元数据的关键词来检索庞大的数据库仓库。在这种设置中，请求者提供一个初始数据集，平台识别出补充数据集以增强（连接或联合）请求者的数据集，从而最大程度地提升机器学习模型（例如线性回归）的性能。尽管有效，但当前基于任务的数据搜索受到以下阻碍：（1）高延迟使用户望而却步，（2）隐私问题不符合监管标准，（3）低数据质量导致效用低下。我们提出了Mileena，一个快速、隐私且高质量的任务型数据库搜索平台。其核心基于预计算的半环草图，以实现高效的机器学习训练和评估。基于半环，我们开发了一种新颖的因子化隐私机制，该机制使搜索具有差分隐私性，并能在不影响主要质量的前提下，扩展到任意语料库大小和请求数量。我们还展示了使用基于LLM的智能体进行自动数据转换，以及应用半环支持因果发现和治疗效果估计的早期前景。