I think a versioning strategy must in the initial formulation of the
strategy specify how applications that support the format should react
to new versions. This could conceivably be not just the ways outlined
but also algorithms for determining if the new version is compatible
with older versions, or lookups of new validations etc. of an external
repository for the new version.

A versioning strategy is, with this view, a contract on the relation
between different versions of the format. As such it is harder to
change the versioning strategy than it is to change the format itself,
it is at a higher level of control.

Cheers,
Bryan Rasmussen

On Dec 7, 2007 9:55 PM, Costello, Roger L. <costello@m...> wrote:
> Hi Folks,
>
> Oftentimes when discussing a "versioning strategy" I focus on how to
> design schemas in a fashion to lessen the impact of changes.  It occurs
> to me that this addresses only one aspect of the data versioning
> problem.  Below I have attempted to identify other issues to be
> addressed in a data versioning strategy.  I am interested in hearing
> your thoughts on this.
>
> EVOLVING DATA
>
> Suppose some data is regularly exchanged between machines:
>
> Machine 1 --> data --> Machine 2
> Machine 1 <-- data <-- Machine 2
>
> Periodically the data changes due to requirement changes, additional
> insights, or from innovation.
>
> A change results in a new "version" of the data.
>
>
> PROBLEM
>
> What are the categories of changes that may occur?  What categories of
> changes must be dealt with by a data versioning strategy?
>
>
> CATEGORIES OF CHANGE
>
> 1. Semantic - the meaning of the data changes.
>
> Example:
>
> version 1 data: a "distance" value means the distance from the center
> of town.
>
> version 2 data: a distance value means the distance from the town line.
>
> 2. Relationship - the relationship between the data changes.
>
> Example:
>
> version 1 data: there is a co-constraint between the start-time and the
> end-time.
>
> version 2 data: there is a three-way co-constraint between start-time,
> end-time, and mode-of-transportation.
>
> 3. Syntax - the structure of the data changes.
>
> Example:
>
> version 1 data: the employee data is listed first and the person's name
> is given by his given-name and surname.
>
> version 2 data: the department data is listed first and in the employee
> data each person's name additionally contains a middle name.
>
>
> SUPPORTING TECHNOLOGIES
>
> Suppose the data being exchanged is formatted using the XML syntax.
>
> Machine 1 --> XML --> Machine 2
> Machine 1 <-- XML <-- Machine 2
>
> What technologies support the above categories of change?
>
> 1. Semantic: A data dictionary may be used to define meaning.
>
> 2. Relationship: Schematron may be used to express relationships
> between data.
>
> 3. Syntax: XML Schema, Relax NG, or DTD may be used to express the
> structure of the data.
>
>
> REQUIREMENTS ON A VERSIONING STRATEGY
>
> A versioning strategy must take into consideration:
>
> - changes in the semantics of the data
> - changes in the relationships of the data
> - changes in the syntax of the data
>
> When data is in an XML format then a versioning strategy must
> implement:
>
> - versioning a data dictionary
> - versioning a Schematron schema
> - versioning an XML Schema, Relax NG schema, or DTD
>
>
> QUESTIONS
>
> a. Do you agree with the three categories of change?
>
> b. Do these categories represent all types of change?
>
> c. Do you agree that a versioning strategy must address semantic,
> relationship, and syntactic changes?
>
> /Roger
>
>
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