Leveraging Time Distortion for seamless Navigation into Data Space-Time Continuum
This work addresses the challenge of resource-intensive and time-consuming reasoning processes for intelligent systems, such as smart grids, by providing a more flexible navigation method, though it appears incremental in its application to existing frameworks.
The paper tackles the problem of inefficient data sampling and reasoning in intelligent systems by introducing a novel modeling approach that treats time and locality as first-class properties, enabling seamless navigation in a space-time continuum. The result shows that reasoners using this approach outperform full sampling methods and meet near real-time requirements in a smart grid electric load prediction evaluation.
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.