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Population validation on a join subset constraint

Last post 02-14-2014 16:21 by koneill. 6 replies.
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  • 08-09-2013 10:39

    • koneill
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    Population validation on a join subset constraint

    Hi,

    I have a question regarding the join subset contraint in NORMA. I think that theres no population validation when using a join subset constraint. Is this true? Or am i doing someting wrong?

    I have the following subset constraint. No errors are displayed in the error list.

    Subset constraint: 

    The only examples I have in my model
     
    Examples:
    Advisor 'Adam' speaks Language 'Dutch'.
     
    Regards,
    Karl

     

     

  • 08-09-2013 17:13 In reply to

    Re: Population validation on a join subset constraint

    Hi Karl Sorry. The join subset constraint verbalizes correctly, but we do not yet check for sample population violations of that constraint. At this stage, we check sample population violations for only basic constraints. Adding population checks for advanced constraints is on our to-do list for a future release. Cheers Terry
  • 08-10-2013 3:24 In reply to

    • koneill
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    Re: Population validation on a join subset constraint

    No problem. I will keep it in mind when entering sample populations. The verbalization was indeed fine so I was doubting my understanding of the rule. Great tool by the way! Regards, Karl
  • 02-12-2014 14:11 In reply to

    Re: Population validation on a join subset constraint

    Does any tool (separate from NORMA) exist to check whether a sample population satisfies all constraints in an ORM model? Thanks, Matt
  • 02-12-2014 15:06 In reply to

    • koneill
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    Re: Population validation on a join subset constraint

    Doctool does, which is a tool that supports the cogNiam method. It has many similar features including verbalization. However it is expensive.
  • 02-14-2014 14:30 In reply to

    Re: Population validation on a join subset constraint

    I'm not in a position to comment on any recent Doctool versions as it has been close to a decade since I touched a new version, but I am familiar with the approach. There is certainly a stronger emphasis on sample population as the whole system focuses on the lexical level. The system does not use ORM object types at all. The primary display is called a 'fact type diagram', with a single fact type diagram representing multiple elementary fact types in ORM.

    Given the lack of object types, which are fundamental for forming the ORM verbalization patterns, the Doctool verbalization is generally limited to inserting sample data into the sentence patterns written by the user. This is a much different style of verbalization and validation than we use in the NORMA tool, which forms logical statements based solely on the model, not a population. Both approaches have benefits.

    Karl, if you've used the tool, please let the forum know if I've misrepresented anything here.

    (Back to NORMA) 

    To validate a sample population against all constraints effectively requires a full physical implementation of the constraints in the system. This would also likely be a different implementation than a production system with soft (deontic) constraints so that invalid data could be entered. If this happens in the tool, it will likely be based on such a mapping of the system, with data entry still done at the conceptual (fact type and object type) level.

    -Matt

  • 02-14-2014 16:21 In reply to

    • koneill
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    Re: Population validation on a join subset constraint

    Hi Matt, I've used the tool a few years back (and i am up to speed on cogniam) and you're spot on. A big difference is that the facttypes in doctool do not require a uniqueness constraint to cover the n-1 roles of the facttype. That, of course, is because that is allowed way of modellingin the niam method. However if you can analyse and model in norma, you can pick up doctool very quickly as most of the constraints (equality, subset, value, etc) are identical. My personal opinion is that both methods have the same 'core' advantages of which the following are the most powerful ; verbalizing the relationship between attributes and using sample populations. It basically forces the analyst to understand the subject matter :). Regards, Karl
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