The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 08, 2017

Filed:

Dec. 31, 2014
Applicant:

Code 42 Software, Inc., Minneapolis, MN (US);

Inventors:

Matthew Dornquast, Minneapolis, MN (US);

Brian Bispala, Minneapolis, MN (US);

Samuel Schroeder, Minneapolis, MN (US);

Christopher Gwinn, Minneapolis, MN (US);

Justin Grammens, St. Paul, MN (US);

Thomas (Tony) Lindquist, Minneapolis, MN (US);

Peter Lindquist, St. Paul, MN (US);

Assignee:

Code 42 Software, Inc., Minneapolis, MN (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 5/00 (2006.01); G06F 1/00 (2006.01); G06F 11/14 (2006.01); G06F 17/30 (2006.01); G06F 21/55 (2013.01); G06F 21/60 (2013.01); G06N 99/00 (2010.01); H04L 29/08 (2006.01); H04L 12/911 (2013.01); H04L 29/06 (2006.01);
U.S. Cl.
CPC ...
G06F 11/1453 (2013.01); G06F 11/1435 (2013.01); G06F 11/1451 (2013.01); G06F 11/1464 (2013.01); G06F 17/3023 (2013.01); G06F 17/30156 (2013.01); G06F 17/30174 (2013.01); G06F 17/30194 (2013.01); G06F 17/30203 (2013.01); G06F 17/30215 (2013.01); G06F 17/30356 (2013.01); G06F 17/30581 (2013.01); G06F 21/552 (2013.01); G06F 21/604 (2013.01); G06N 99/005 (2013.01); H04L 47/821 (2013.01); H04L 67/1072 (2013.01); H04L 67/22 (2013.01); G06F 2221/2111 (2013.01); H04L 63/0428 (2013.01);
Abstract

Disclosed in some examples are predictive storage techniques for use in a distributed data system. The predictive storage techniques may be used to manage locally stored elements of a shared data collection, such as the storage of files on nodes of the distributed data system that are limited in local storage space. The predictive storage techniques may achieve a balance between consumption of local resources and timely access of important elements in the shared data collection. For example, the predictive storage techniques may be used for keeping or pre-caching certain items of a collection that are determined as likely to be used in local storage for convenient access, and allowing access the remaining items on request over a network.


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