This two-part series focuses on the value of doing small data analyses on a big data collection.  In Part 1 of the series, we describe the applications and benefits of “small data” in general terms from several different perspectives.  In Part 2 of the series, we’ll spend some quality time with one specific algorithm (Local Linear Embedding) that enables local subsets of data (i.e., small data) to be used in developing a global understanding of the full big data collection.

We often hear that small data deserves at least as much attention in our analyses as big data.  While there may be as many interpretations of that statement as there are definitions of big data (and see more here), there are at least two situations where “small data” applications are worth considering…

(continue reading here … https://www.mapr.com/blog/when-big-data-goes-local-small-data-gets-big-part-1)

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