Managing large-scale scientific hypotheses as uncertain and probabilistic data with support for predictive analytics

TL;DR

This paper introduces a probabilistic database approach for managing large-scale scientific hypotheses as uncertain data, enabling hypothesis encoding, management, and predictive analytics from simulation results.

Contribution

It presents a novel synthesis method and tool to encode and manage competing hypotheses as uncertain data within a probabilistic database framework.

Findings

Enables encoding of hypotheses as uncertain data

Supports conditioning hypotheses with observational data

Facilitates predictive analytics from simulation results

Abstract

The sheer scale of high-resolution raw data generated by simulation has motivated non-conventional approaches for data exploration referred as `immersive' and `in situ' query processing of the raw simulation data. Another step towards supporting scientific progress is to enable data-driven hypothesis management and predictive analytics out of simulation results. We present a synthesis method and tool for encoding and managing competing hypotheses as uncertain data in a probabilistic database that can be conditioned in the presence of observations.