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This question is currently tagged : Handling a regularly increasing feature set

It seems like users will tend to add this tag whenever they have some feeling about the "bigness" of their data. Of course that doesn't mean the tag is useful or relevant.

So, when is this tag useful and relevant? It's already difficult to say what "big data" even means, as discussed in one of the early and most popular questions on the site. Put another way, when someone views all questions tagged , what sorts of questions should they see?

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I think the tag is appropriate when scale is a non-trivial part of the problem. Not all data science involves huge amounts of data. While I agree that "big data" is an ill-defined term, I think it is relatively unambiguous here as a tag for questions where the challenge is partly data volume.

Whether a given question really has anything to do with scale issues is another issue. I'm willing to give the benefit of the doubt in general. Someone who tags a question this way ought to set out the problem of data size but this is just a way of flagging it as scale-related.

Here I think the question is related to data size although it's not obvious. The fact that retraining the simple model is identified as a significant obstacle suggests scale is a problem.

I would certainly suggest and edit or comment in this case.

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  • $\begingroup$ I think this discussion has been active long enough to mark this as accepted based on the votes and lack of counter-arguments, though "non-trivial" is still to some degree a judgment call. $\endgroup$ – Air Jul 10 '14 at 15:22
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Users tag "big-data", while not everyone knows if it is really the case. But this is our job to re-tag if needed ;)

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