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.
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.
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This paper introduces U-relations, a succinct and purely relational representation system for uncertain databases. U-relations support attribute-level uncertainty using vertical partitioning. If we consider positive relational algebra extended by an operation for computing possible answers, a query on the logical level can be translated into, and evaluated as, a single relational algebra query on the U-relational representation. The translation scheme essentially preserves the size of the query in terms of number of operations and, in particular, number of joins. Standard techniques employed in off-the-shelf relational database management systems are effective for optimizing and processing queries on U-relations. In our experiments we show that query evaluation on U-relations scales to large amounts of data with high degrees of uncertainty.