VALUE

The widespread adoption of open datasets across various domains has emphasized the significance of joining and computing their utility. However, the interplay between computation and human interaction is vital for informed decision-making. To address this issue, we first propose a utility metric to calibrate the usefulness of open datasets when joined with other such datasets. Further, we distill this utility metric through a visual analytic framework called VALUE, which empowers the researchers to identify joinable datasets, prioritize them based on their utility, and inspect the joined dataset. This transparent evaluation of the utility of the joined datasets is implemented through a human-in-the-loop approach where the researchers can adapt and refine the selection criteria according to their mental model of utility. Finally, we demonstrate the effectiveness of our approach through a usage scenario using real-world open datasets.