$\Upsilon$-DB: Managing scientific hypotheses as uncertain data

TL;DR

This paper introduces $$-DB, a probabilistic database framework for managing deterministic scientific hypotheses as uncertain data, enabling systematic hypothesis management and deep predictive analytics.

Contribution

It presents a novel methodology for constructing and managing hypothesis databases as uncertain data within probabilistic databases, expanding applications of p-DBs.

Findings

Systematic construction of hypothesis databases as uncertain data.

Application of $$-DB for deep predictive analytics.

Demonstration of hypothesis management as a new p-DB application.

Abstract

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., $\Upsilon$-DBs. It introduces hypothesis management as a promising new class of applications for p-DBs. We illustrate the potential of $\Upsilon$-DB as a tool for deep predictive analytics.