Re: Versioning

I also want to point out to the group that my question earlier today is completely unrelated to this kind of versioning, in case anyone had intended to comment on that in this thread. I was addressing the issue of versioning an API, not the data it makes available.
-Annette

--
Annette Greiner
NERSC Data and Analytics Services
Lawrence Berkeley National Laboratory
510-495-2935

On Jul 27, 2015, at 10:53 AM, Bernadette Farias Lóscio <bfl@cin.ufpe.br> wrote:

> Hi Makx,
> 
> Thanks for your message! Good to know that we have some agreement :) I have some comments:
> 
> 
> 
> I do not agree that ‘Following’ creates different ‘states’ of the same dataset. To me, this year’s budget is only related to last year’s budget because they are both budgets, but they are not versions of the same thing.
> 
> 
> I agree that they are not versions of the same thing. In this case, these two datasets (last year's budget and this year's budget) won't be explicitly related? I think we should find a way to specify this relationship.
> 
> They may have the same granularity (e.g. expressed in thousands of dollars) but the structure could be different (e.g. because of organisational or regional changes). For me, time series (and spatial series) have nothing to do with versioning.
> 
> 
> I agree that time series (and spatial series) have nothing to do with versioning. However, I don't agree that the structure could be different. If the structure is different, then there will be a new version, no?
>  
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>  
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> I do also not agree that ‘Adapting’ creates a new state (as in data at a particular moment). All adaptations are equally valid and exist in parallel. To me, adaptations are almost in the same category as the different formats that DCAT groups as Distributions of a Dataset.
> 
> 
> I agree that all adaptations are equally valid and should exist in parallel. Maybe, the term "state" is not suitable for this, but the idea is to show that there is a relation between adaptations and the "original" dataset.
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> Finally, I do I agree that ‘Modifying’ creates a different state and not a new version. In many cases, a publisher might not even bother to keep the old file but would just change the dct:modified date in the metadata.
> 
> 
> good!
> 
> 
> Cheers,
> Bernadette
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>  
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> Do others agree with limiting versioning to the ‘Superseding’ category?
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> 
> Makx.
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> From: Bernadette Farias Lóscio [mailto:bfl@cin.ufpe.br] 
> Sent: 27 July 2015 13:51
> To: Makx Dekkers <mail@makxdekkers.com>
> Cc: Data on the Web Best Practices Working Group <public-dwbp-wg@w3.org>
> Subject: Re: Versioning
> 
>  
> 
> Hi Makx,
> 
>  
> 
> Thanks for bringing this discussion and clarifying those differences. IMO this kind of distinction is important. However,  I am not sure if we should call "versioning" all types of "updates" that you presented. I created the following table to help me to visualize these updates in terms of data (or content) changes and structure changes. 
> 
>  
> 
>  
> 
> content change
> 
> structure change
> 
>  
> 
> Superseding
> 
> yes
> 
> yes
> 
> new version
> 
> Following
> 
> yes
> 
> no
> 
> different spatial/temporal granularity
> 
> Modifying
> 
> yes
> 
> no
> 
> the data may have been updated or data may have been added
> 
> Adapting
> 
> yes
> 
> no
> 
> content is the same, but in different contexts
> 
>  
> 
> I think that just in the first case (superseding) there will be a new version of the dataset. In the other cases, there will be different states of the same dataset, where a dataset state means the data in the dataset at a particular moment. 
> 
>  
> 
> Please, let me know if I understood correct and if these ideas make sense to you.
> 
>  
> 
> Cheers,
> 
> Bernadette
> 
>  
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> Superseding:
> 
>  
> 
> Content and structure might be very different but the publisher wants you to use the current resource rather than a resource that preceded it. The URL stays the same while the content changes although the broad intention of the content stays the same.
> 
>  
> 
> Examples:
> 
> •                    Today’s website (or, more general, web resource) versus last week’s (Memento);
> 
> •                    Latest version link, e.g. latest published draft of BP http://www.w3.org/TR/dwbp/.
> 
>  
> 
> Following:
> 
>  
> 
> The type of content is the same but it covers a different time period. Both the new and the old data remain valid. (NB: spatial series, e.g. the same kind of data for different regions, are similar to temporal series in many respects.)
> 
>  
> 
> Examples:
> 
> •                    Sequences of annual budgets;
> 
> •                    Daily meteorological observations;
> 
> •                    Periodical census data.
> 
>  
> 
> Modifying:
> 
>  
> 
> Content, structure and data points are the same to some extent but the data may have been updated or data may have been added.
> 
>  
> 
> Examples:
> 
> •             Correcting errors in values of data points, e.g. resulting from quality control or user feedback;
> 
> •             Adding data points, e.g. if measurements from different measuring devices come in at different times but belong together;
> 
> •             Updating values, e.g. in a Year-to-date file.
> 
>  
> 
> Adapting:
> 
>  
> 
> Content and structure are essentially the same but in different contexts.
> 
>  
> 
> Examples:
> 
> •             Translations of text fields or labels;
> 
> •             Conversion of co-ordinate system;
> 
> •             Conversions of measures, e.g. ºC to ºF, imperial units to SI;
> 
> •             Changes in granularity.
> 
>  
> 
> Should we somehow take such distinctions into account or should we lump them all together?
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> 
> --
> 
> Bernadette Farias Lóscio
> Centro de Informática
> Universidade Federal de Pernambuco - UFPE, Brazil
> ----------------------------------------------------------------------------
> 
> 
> 
> 
> -- 
> Bernadette Farias Lóscio
> Centro de Informática
> Universidade Federal de Pernambuco - UFPE, Brazil
> ----------------------------------------------------------------------------