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Re: [xml-dev] What techniques do you employ to ensure that your datais precisely and unambiguously specified?

In working on a few business document markups a matter of
high "level of importance" was the difference between an invoice
and a credit note. I saw and heard various proposals for how
to do this which illustrate good and not-so-good practices:
1) have a document which is neither invoice nor credit note
and add an attribute for documentType whose value could
be enumerated and include "Invoice" and "CreditNote"
[not good, I thought] 
2) have a document which is in essence an invoice
let the amounts and totals be negative if it is a credit note 
[not good, I thought]
3) have a separate invoice markup and credit note markup
with the top elements 'Invoice' and 'CreditNote' respectively
[good, I thought, and we did this for UBL]
Another example follows on from point 2): how to specify
a negative amount in such a document - maybe people
would prefer two separate elements for PositiveAmount
and NegativeAmount, though in UBL we just decided to let
the minus sign or lack of it in the value for the Amount be
the indicator.
Stephen D Green

On 24 September 2012 17:12, Costello, Roger L. <costello@mitre.org> wrote:
Hi Folks,

How carefully do you specify your data? Is it free from ambiguity and misinterpretation?

Although all data should be specified precisely and without ambiguity, the consequences of data being imprecise or ambiguous ranges from minor to catastrophic.  There is a name for the latter data: level 1 data.

      Level 1 (critical) data: the data must be specified
      precisely with absolutely no ambiguity. For if the data
      is misinterpreted, then there is a real
      possibility of a loss of human life or financial,
      political, or personal calamity.

When you create your XML Schemas do you identify the level of the data? For example, do your XML documents contains a <level> element:

            <level>level 1 (critical) data</data>

Has anyone created an XML Schema for the various levels?

[Key Question:] If you have level 1 data what techniques do you employ to ensure that there is virtually zero chance for the data to be misunderstood or misinterpreted?



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