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:
Dec. 06, 2022

Filed:

Dec. 23, 2019
Applicant:

Teradata Us, Inc., San Diego, CA (US);

Inventors:

Choudur K. Lakshminarayan, Austin, TX (US);

Thiagarajan Ramakrishnan, Austin, TX (US);

Awny Kayed Al-Omari, Cedar Park, TX (US);

Assignee:

Teradata US, Inc., San Diego, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/00 (2019.01); G06F 16/215 (2019.01); G06N 20/00 (2019.01); G06F 16/22 (2019.01);
U.S. Cl.
CPC ...
G06F 16/215 (2019.01); G06F 16/2264 (2019.01); G06N 20/00 (2019.01);
Abstract

Improved techniques for processing large-scale data and various large-scale data applications (e.g., large-scale Data Mining (DM), large-scale data analysis (LSDA)) in computing systems (e.g., Data Information Systems, Database Systems) are disclosed. Redundancy-reduced data (RRDS) can be provided as data that can be used more efficiently by various applications, especially, large-scale data applications. In doing so, at least one assumption about the distribution of a multi-dimensional data set (MDDS) and its corresponding set of responses (Y) can be made in order to reduce the multi-dimensional data set (MDDS). For example, a normal distribution (e.g., bell-shape, symmetric) can be assumed and Mutual information of the combination of a multi-dimensional set (X) and its corresponding responses (Y) can be optimized, for example, by using linear transformations, iterative numerical procedures, one or more constraints associated with the at least one assumption, and using one or more Lagrange multipliers to provide a constraint optimization function.


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