In view of the paradigm shift that makes science ever more data-driven, we consider deterministic scientific hypotheses as uncertain data. This vision comprises a probabilistic database (p-DB) design methodology for the systematic construction and management of U-relational hypothesis DBs, viz., @math -DBs. It introduces hypothesis management as a promising new class of applications for p-DBs. We illustrate the potential of @math -DB as a tool for deep predictive analytics.
@cite_0: Note: Chapter 6 Reference EPFL-CHAPTER-167070 Record created on 2011-06-22, modified on 2017-05-12
U-relations and p- @math WSA were developed in the influential MayBMS project. http: maybms.sourceforge.net As implied by its design principles, e.g., compositionality and the ability to introduce uncertainty, MayBMS ' query language @math @cite_0 fits well to hypothesis management. Noteworthy, the repair @math key operation gives rise to alternative worlds as maximal-subset repairs of an argument key. We shall look at it from the point of view of p-DB design, for which no methodology has yet been proposed.