AI News, Data Science for Internet of Things methodology - Evolving CRISP-DM - Part Two

Data Science for Internet of Things methodology - Evolving CRISP-DM - Part Two

You can find the first post describing the initial steps of the methodology on Data Science Central  This new post focus on continuous improvement, following the design and deployment of the architecture and the model.

While hardware is unchanged for a certain period of time, software gets updated often, bringing new capabilities to the devices, which can be used to solve the problem defined initially.

However, it the methodology has been implemented in an agile way, then the initial implementation is only a first step good enough, but definitely not the last step of implementation.  This continuous improvement phase is split into four activities:

Given the categories of factors listed above, the rate of review to conduct the improvement activities is very specific to those factors.

Whether we decide to revise the model on a regular period or when a given event occurs (often an unexpected result / behavior), we need to collect information on the current state.

For instance, if we are looking at an industrial analytics application that predicts pressure based on a set of diverse parameters coming from sensors, we want to collect the time series of all the data coming from the sensors, the time series of the actual pressure points and the predicted time series by the model we initially designed.

Whatever the rationale behind the continuous improvement process, collecting this data ensures that we have a sound foundation for reviewing the work done previously, and construct a new dataset.

For instance a device may have sensors that were not initially programmed in its firmware, like an industrial device initially providing only temperature information, which is upgraded to provide pressure in addition of temperature.

The implications of changing the architecture can be far reaching, and doing so should be done with care, otherwise we may not be doing continuous improvement but we have to start from scratch instead.

This list is by no means exhaustive, but for all these reasons we need to re-analyze the problem at hand and rework on the model, using the new dataset that has been created (either in the first phase or in the second phase depending on whether we have additional data in the data streams).

We won’t go through again here, just reminding that the model should be assess both from a statistical point of view (minimizing bias and variance and trading-off precision and recall) and that it can indeed solve the problem (impact on the KPIs we have defined).

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