`Sketch' data structures are simple, efficient, compact summaries of large data sets. Sketches enable approximate computations like similarity comparison and summarization of distributions. There have been a number of successes where use of sketches have been applied in the field, most notably when dealing with very large data sets. In this talk, I'll present some key sketches and their properties, and describe some successes and some non-successes in their application. I'll conclude by outlining emerging applications for sketches, and their future potential.
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