Recent dataset search platforms use ML task-based utility measures rather than metadata-based keywords, to search large dataset corpora. Requesters provide an initial dataset, and the platform seeks additional datasets that augment -- join or union -- requester's dataset to most improve the model (e.g., linear regression) performance. 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：近期，数据集搜索平台采用基于机器学习任务的效用度量，而非基于元数据的关键词，来搜索大规模数据集集合。请求者提供初始数据集，平台则寻找能够通过连接或并集操作增强请求者数据集、从而最大程度提升模型（例如线性回归）性能的额外数据集。尽管此类方法有效，但当前基于任务的数据搜索受到以下因素制约：（1）高延迟使用户望而却步；（2）隐私问题难以满足法规标准；（3）低数据质量导致效用低下。我们提出Mileena——一个快速、隐私且高质量的任务导向型数据集搜索平台。Mileena的核心构建于预计算的半环草图之上，以实现高效的机器学习训练与评估。基于半环，我们开发了一种新颖的因子化隐私机制，使搜索满足差分隐私要求，并能扩展到任意规模的语料库和请求数量，且不会显著降低质量。此外，我们还展示了将基于大语言模型的智能体用于自动数据转换，以及应用半环支持因果发现与处理效应估计的初步潜力。