# Learn-Memorize-Recall-Reduce A Robotic Cloud Computing Paradigm

## TL;DR

This paper introduces a novel robotic cloud computing paradigm that transforms unstructured data into structured formats, stores it efficiently, retrieves it effectively, and reduces data complexity to optimize limited computing resources.

## Contribution

It proposes a comprehensive learn-memorize-recall-reduce framework specifically designed for managing large volumes of unstructured robotic data in cloud environments.

## Key findings

- Efficient data transformation from unstructured to structured formats.
- Effective storage solutions for massive robotic data.
- Improved data retrieval and reduction techniques for resource-limited settings.

## Abstract

The rise of robotic applications has led to the generation of a huge volume of unstructured data, whereas the current cloud infrastructure was designed to process limited amounts of structured data. To address this problem, we propose a learn-memorize-recall-reduce paradigm for robotic cloud computing. The learning stage converts incoming unstructured data into structured data; the memorization stage provides effective storage for the massive amount of data; the recall stage provides efficient means to retrieve the raw data; while the reduction stage provides means to make sense of this massive amount of unstructured data with limited computing resources.

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Source: https://tomesphere.com/paper/1704.04712