Leveraging Time Distortion for seamless Navigation into Data Space-Time Continuum

TL;DR

This paper introduces a novel space-time continuum modeling and navigation approach that enhances continuous reasoning in intelligent systems, demonstrated through a smart grid electric load prediction case.

Contribution

It proposes a new modeling and navigation method that treats time and locality as core properties, enabling efficient, seamless model exploration for real-time reasoning.

Findings

Outperforms full sampling in reasoning tasks

Maintains near real-time performance

Successfully integrated into an open-source framework

Abstract

Intelligent software systems continuously analyze their surrounding environment and accordingly adapt their internal state. Depending on the criticality index of the situation, the system should dynamically focus or widen its analysis and reasoning scope. A naive -why have less when you can have more- approach would consist in systematically sampling the context at a very high rate and triggering the reasoning process regularly. This reasoning process would then need to mine a huge amount of data, extract a relevant view, and finally analyze this adequate view. This overall process would require some heavy resources and/or be time-consuming, conflicting with the (near) real-time response time requirements of intelligent systems. We claim that a continuous and more flexible navigation into context models, in space and in time, can significantly improve reasoning processes. This paper thus introduces a novel modeling approach together with a navigation concept, which consider time and locality as first-class properties crosscutting any model element, and enable the seamless navigation of models in this space-time continuum. In particular, we leverage a time-relative navigation (inspired by the space-time and distortion theory [7]) able to efficiently empower continuous reasoning processes. We integrate our approach into an open-source modeling framework and evaluate it on a smart grid reasoning engine for electric load prediction. We demonstrate that reasoners leveraging this distorted space-time continuum outperform the full sampling approach, and is compatible with most of (near) real-time requirements.