Random Sampling from Databases by Frank Olken Doctor of Philosophy in Computer Science University of California at Berkeley Professor Michael Stonebraker, Chair In this thesis I describe efficient methods of answering random sampling queries of relational databases, i.e., retrieving random samples of the results of relational queries. I begin with a discussion of the motivation for including sampling operators in the database management system (DBMS). Uses include auditing, estimation (e.g., approximate answers to aggregate queries), and query optimization. The second chapter contains a review of the basic file sampling methods used in the thesis: acceptance/rejection sampling, reservoir sampling, and partial sum (ranked) tree sampling. I describe their usage for sampling from variably blocked files, and sampling from results as they are generated. Related literature on sampling from databases is reviewed. In Chapter Three I show how acceptance/rejection sampling of B + trees can be...
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