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UNCG-CSE / Library-Computer-Usage-Analysis

The University Libraries at UNCG currently track the state of a computer, determining whether or not a particular computer is in use. This data is compiled into a database, and a web app pulls from this database to show a map and number of available computers. As of Fall 2017, the data had not been used to determine which computers are used more frequently, aside from counting the number of times a computer transitions into/away from the 'in-use' state. This project attempts to correlate the usage of these computers with various factors, including: campus scheduling, equipment configuration, placement, population in the library, and area weather. Using this data, this project also uses machine learning to determine the best placement of computers for future allocation, and possible reconfiguration of equipment and space.
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Attempt at addressing issue 19. #31

Closed smindinvern closed 6 years ago

smindinvern commented 6 years ago

Add functions for computing per-period (most likely hour) utilization for each machine, and merging that all into a single DataFrame, with columns representing individual computers.

This implementation is horribly slow.

smindinvern commented 6 years ago

Any thoughts, anyone?

brownworth commented 6 years ago

I'd like to compare your code and mine for efficiency. If yours is faster than mine, I would definitely go with yours.

smindinvern commented 6 years ago

sigh... Now that i'm actually using the same data file as you are in the brown branch, the results line up. And, unsurprisingly, the implementation in brown using pivot is about 2x faster than the implementation in this branch. The only thing I wonder about is how well this method will scale, since it's resampling the data to 1Hz, which, for some number of years of data is... a lot of seconds... Probably still fine though for this use-case.