Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
2007-12-11
2007-12-11
Leroux, Etienne (Department: 2161)
Data processing: database and file management or data structures
Database design
Data structure types
Reexamination Certificate
active
11212301
ABSTRACT:
Random samples without replacement are extracted from a distributed set of items by leveraging techniques for aggregating sampled subsets of the distributed set. This provides a uniform random sample without replacement representative of the distributed set, allowing statistical information to be gleaned from extremely large sets of distributed information. Subset random samples without replacement are extracted from independent subsets of the distributed set of items. The subset random samples are then aggregated to provide a uniform random sample without replacement of a fixed size that is representative of a distributed set of items of unknown size. In one instance, a multivariate hyper-geometric distribution is sampled by breaking up the multivariate hyper-geometric distribution into a set of univariate hyper-geometric distributions. Individual items of a uniform random sample without replacement are then determined utilizing a normal approximation of the univariate hyper-geometric distributions and a finite population correction factor.
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Chickering David M.
Meek Christopher A.
Roy Ashis K.
Amin Turocy & Calvin LLP
Leroux Etienne
Microsoft Corporation
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