Re: [xml-dev] What techniques do you employ to ensure that your data is 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] 

 

 

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. <[email protected]> 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:

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

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?

/Roger

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