Streaming and Learning the Personal Context

TL;DR

This paper proposes a novel model and learning process for representing personal context to enhance machine understanding and human-machine interaction in real-world systems.

Contribution

It introduces a new personal context representation model, a learning process for better integration, and a system architecture tailored for real-life environments.

Findings

Improved personal context representation model

Enhanced integration with machine learning systems

Architectural design for real-world deployment

Abstract

The representation of the personal context is complex and essential to improve the help machines can give to humans for making sense of the world, and the help humans can give to machines to improve their efficiency. We aim to design a novel model representation of the personal context and design a learning process for better integration with machine learning. We aim to implement these elements into a modern system architecture focus in real-life environments. Also, we show how our proposal can improve in specifically related work papers. Finally, we are moving forward with a better personal context representation with an improved model, the implementation of the learning process, and the architectural design of these components.