Generating Skyline Datasets for Data Science Models
Authors: name: Mengying Wang, name: Hanchao Ma, name: Yiyang Bian, name: Yangxin Fan, name: Yinghui Wu

Preparing high-quality datasets required by various data-driven AI and
machine learning models has become a cornerstone task in data-driven analysis.
Conventional data discovery methods typically integrate datasets towards a
single pre-defined quality measure that may lead to bias for downstream tasks.
This paper introduces MODis, a framework that discovers datasets by optimizing
multiple user-defined, model-performance measures. Given a set of data sources
and a model, MODis selects and integrates data sources into a skyline dataset,
over which the model is expected to have the desired performance in all the
performance measures. We formulate MODis as a multi-goal finite state
transducer, and derive three feasible algorithms to generate skyline datasets.
Our first algorithm adopts a "reduce-from-universal" strategy, that starts with
a universal schema and iteratively prunes unpromising data. Our second
algorithm further reduces the cost with a bi-directional strategy that
interleaves data augmentation and reduction. We also introduce a
diversification algorithm to mitigate the bias in skyline datasets. We
experimentally verify the efficiency and effectiveness of our skyline data
discovery algorithms, and showcase their applications in optimizing data
science pipelines.