Applied Invention, LLC

Other

Augusto Callejas

Eric Theodore Bax

W Daniel Hillis

Mathias L Kolehmainen

Harry Kao

Disclosed are a method and apparatus for limiting the number of results returned by a data store in response to a query. Upon receiving an initial query, the data store returns a page of results that includes a subset of the data items within the data store satisfying the conditions of the query. The data store also provides a marker indicating the extent of the set of data items. If a subsequent query that requests additional results which satisfy the same query conditions and that includes the marker is received, the data store returns a page of results that includes a subset of data items that are disjoint from the initial subset, and provides an updated marker which indicates the extent of the union of the initial and subsequent subsets. If still further results are desired from the data store, an additional query containing the updated marker may be submitted.

Data store with lock-free stateless paging capacity

Disclosed are a method and apparatus for limiting the number of results returned by a store in response to a query. Upon receiving an initial query, the data store returns a page of results that includes a subset of the data items within the data store satisfying the conditions of the query. The data store also provides a marker indicating the extent of the set of data items. If a subsequent query that requests additional results which satisfy the same query conditions and that includes the marker is received, the data store returns a page of results that includes a subset of data items that are disjoint from the initial subset, and provides an updated marker which indicates the extent of the union of the initial and subsequent subsets. If still further results are desired from the data store, an additional query containing the updated marker may be submitted.

Data store with lock-free stateless paging capability

Given a set of training examples&#x2014;with known inputs and outputs&#x2014;and a set of working examples&#x2014;with known inputs but unknown outputs&#x2014;train a classifier on the training examples. For each possible assignment of outputs to the working examples, determine whether assigning the outputs to the working examples results in a training and working set that are likely to have resulted from the same distribution. If so, then add the assignment to a likely set of assignments. For each assignment in the likely set, compute the error of the trained classifier on the assignment. Use the maximum of these errors as a probably approximately correct error bound for the classifier.

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Pasadena, CA, United States of America

Augusto Callejas