A new form of business rule mining and validation?

I am fully emerged in a business rules automation project, as a process consultant for rules governance.

Last week I received training in the business rules management suite that is used within the project (Be Informed, a promising dutch player, see their website).
And it brought me back years ago, when I was programming (during my time @ university) in Prolog.
Part of my Prolog assignment back then, was to implement an algorithm (from Quinlan) from the Artificial Intelligence - Machine learning domain (see for instance http://en.wikipedia.org/wiki/Supervised_learning).

It worked basically as follows:

You gave it a set of examples, in which each example contained a set of variables and their data, and an associated classification.
For instance:
(Color=Red, Tiers=True, NrOfTiers=4, Engine=True, Sail=False, CanBeInWater=False) -> Car
(Color=Green, Tiers=True, NrofTiers=2, Engine=False, Sail=False, CanBeInWater=False) -> Bike
(Color=White, Tiers=False, NrOfTiers=n/a, Engine=False, Sail=True, CanBeInWater=True) -> Sailingboat
Etc.

When you ran the algorithm, it was able to analyse the examples and classifications, and come up with a minimum set of rules that matched the key determining variables and classification, e.g.:
(Tiers=True, NrOfTiers=4, Engine=True) -> Car
(Tiers=True, NrOfTiers=2, Engine=False) -> Bike

Back to the project....
Considerable time is currently spent in the project for business rule elicitation. Workshops, study of systems, analysing documents, analysing legislation, etc.
In a recent project someone in my company worked with Process Mining, in which logfiles were analysed, and based on the logfile a process diagram was constructed (+ all kinds of performance data).

Adding algorithm, effort and the concept of process mining, made me look up Rule Mining (see http://en.wikipedia.org/wiki/Business_rule_mining). Strangly enough this differs from the concept of Process mining (it does not analyse data, but code).

And this triggered the following "Wouldn't it be cool" idea:

What if we constructed a strong machine learning algorithm component in a business rules management suite, that was able to analyse great sets of (historic) production data and construct the business rule set that was apparently used?

Example: we have a large CRM database and a Billing system. We create a combined table:
(Customer data), (Order data), (Billing Data) and select certain variables that we want to explain, using business rules based on the remaining variables.
For instance: what are the rules for VAT, discount and Gold membership?

The algorithm would do it's work, and create the minimal rule set...
For instance:
(Country=Netherlands) -> (VAT=19%)
(Orderamount > 200, OrderAmount <300, country =" Germany," customerindustry =" Bank)"> (Discount = 5%)

I see a number of benefits:
- Saving time for rules elicitation
- Retroactively understand what rules where used, and compare them to the existing policies (compliance checks)

Do any of the current BRMS have this feature already? (Time to patent ;-))

For a great overview of machine learning basics:
http://www.informatica.si/PDF/31-3/11_Kotsiantis%20-%20Supervised%20Machine%20Learning%20-%20A%20Review%20of...pdf