$\Upsilon$-DB: A system for data-driven hypothesis management and analytics

TL;DR

$psilon$-DB is a system that manages and analyzes scientific hypotheses modeled as uncertain probabilistic data, enabling hypothesis evaluation and ranking through a data-driven pipeline.

Contribution

It introduces a novel architecture for processing deterministic hypotheses as probabilistic data, including extraction, encoding, and conditioning within a unified system.

Findings

Prototype demonstrates hypothesis extraction from web repositories.

System effectively encodes hypotheses as uncertain probabilistic data.

Enables ranking and evaluation of hypotheses based on observational data.

Abstract

The vision of $\Upsilon$-DB introduces deterministic scientific hypotheses as a kind of uncertain and probabilistic data, and opens some key technical challenges for enabling data-driven hypothesis management and analytics. The $\Upsilon$-DB system addresses those challenges throughout a design-by-synthesis pipeline that defines its architecture. It processes hypotheses from their XML-based extraction to encoding as uncertain and probabilistic U-relational data, and eventually to their conditioning in the presence of observations. In this demo we present a first prototype of the $\Upsilon$-DB system. We showcase its core innovative features by means of use case scenarios in computational science in which the hypotheses are extracted from a model repository on the web and evaluated (rated/ranked) as probabilistic data.