Random sampling from B + trees
Frank Olken, Doron Rotem
Abstract
Frank Olken, Doron Rotem
Abstract
We consider the design and analysis of algorithms to retrieve simple random samples from databases. Specifically, we examine simple random sampling from B+ tree files. Existing methods of sampling from B+ trees, require the use of auxiliary rank information in the nodes of the tree. Such modified B+ tree files are called “ranked B+ trees”. We compare sampling from ranked Bt tree files, with new acceptance/rejection (A/R) sampling methods which sample directly from standard B+ trees. Our new A/R sampling algorithm can easily be retrofit to existing DBMSs, and does not require the overhead of maintaining rank information. We consider both iterative and batch sampling methods.
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We consider the design and analysis of algorithms to retrieve simple random samples from databases. Specifically, we examine simple random sampling from B+ tree files. Existing methods of sampling from B+ trees, require the use of auxiliary rank information in the nodes of the tree. Such modified B+ tree files are called “ranked B+ trees”. We compare sampling from ranked Bt tree files, with new acceptance/rejection (A/R) sampling methods which sample directly from standard B+ trees. Our new A/R sampling algorithm can easily be retrofit to existing DBMSs, and does not require the overhead of maintaining rank information. We consider both iterative and batch sampling methods.
Key concepts: Simple random sample, Sampling (signal processing), Computer science, Tree (set theory), Systematic sampling, Sampling design, B-tree, Slice sampling