  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.
