Learn-Memorize-Recall-Reduce A Robotic Cloud Computing Paradigm
This addresses a scalability issue for robotic applications in cloud computing, but it appears incremental as it builds on existing cloud and data processing concepts.
The paper tackles the problem of processing massive unstructured data from robotic applications in cloud infrastructure designed for structured data, proposing a learn-memorize-recall-reduce paradigm that converts, stores, retrieves, and reduces data with limited computing resources.
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.