Probabilistic and Incomplete databases are a principled way to handle data that isn't perfect (and really, who's data is perfect). Unfortunately, pretty much every PDB and IDB developed to date is insanely slower than their deterministic counterparts (to say nothing of how complex and finicky they are to use correctly). That's why, in collaboration with IIT, for the past five years, we've been working towards a more user-friendly approach to incomplete data management. Instead of trying to give people perfect answers, we just help them keep track of what is uncertain through annotations and provenance trickery. In other words, we're developing an Uncertainty Annotated Database System (or UADB).
Thanks in large part to the heroic efforts of Su Feng, our latest UADB paper received the SIGMOD 2020 Reproducibility Award.