Data summaries (a.k.a., sketches) are compact data structures that can be updated flexibly and efficiently to capture certain properties of a data set. Well-known examples include set summaries (Bloom Filters) and cardinality estimators (e.g., Hyperloglog), amongst others. PODS and SIGMOD have been home to many papers on sketching, including several best paper recipients. Sketch algorithms have emerged from the theoretical research community, but have found wide impact in practice. This paper describes some of the impacts that sketches have had, from online advertising to privacy-preserving data analysis. It will consider the range of different strategies that researchers can follow to encourage the adoption of their work, and what has and has not worked for sketches as a case study.
[ bib | DOI | slides | .pdf ] Back
This file was generated by bibtex2html 1.92.