Implementation of Hashing Algorithms in Stream Mining

Artificial intelligence in food logistics has shown
potential to substitute the First In First Out approach with much
more promising First Expiring First Out approach. However, the
shelf life prediction might be hindered by the amount of storage
needed and by the amount of processing time elapsed between an
event and appropriate system response. We use the data structure
Count-Min (CM) sketch for approximating frequencies of critical
ambient parameters (CAPs) in the cold-chain of peaches and
nectarines. The CM sketch supports fast updates, but it also
supports a trade-off between compression and error. This is
important in the cold-chain setting because of the limited storage
space in sensor nodes, but also because transmitting each sensed
value increases energy consumption and introduces delays. In
order to assess the trade-off between memory consumption and
information loss, the actual values of CAPs with their estimates in
various parameter settings are presented graphically. The
estimates are obtained by querying the point estimator with the
original dataset values. Errors are computed as differences
between actual values and their estimates, and scaled by the
number of instances.