Ronald J Agresta

US Patent:

7747480, Jun 29, 2010

Filed:

Mar 28, 2007

Appl. No.:

11/692433

Inventors:

Ronald Jeffrey Agresta - Raleigh NC, US
Scott Aron Gidley - Cary NC, US
Brian Carl Rineer - Apex NC, US
Alexander A. Novozhilov - Wake Forest NC, US

Assignee:

SAS Institute Inc. - Cary NC

International Classification:

G06Q 10/00
G06F 15/16
G06F 15/177
G06F 7/00
G05B 15/00

US Classification:

705 28, 700 1, 709206, 709220, 707600

Abstract:

Systems and methods for managing data assets in an enterprise computing environment are provided. Data assets associated with a plurality of source systems operating within the enterprise computing environment may be registered with an asset repository hub. The asset repository hub receives a registration request from a source system for a particular data asset and determines whether the data asset is unique within the enterprise computing environment. If the data asset is unique, then the data asset is stored at the asset repository hub and a unique enterprise identifier is associated with the data asset. In determining whether the asset is unique, the asset repository hub may cleanse the data asset against a set of business rules; generate a plurality of match codes that describe the content of the data asset; and cluster the generated match codes against clusters of previously generated match codes. Also provided herein is a mechanism for searching and locating data assets stored within the enterprise computing environment by submitting queries to the asset repository hub.

US Patent:

20170153914, Jun 1, 2017

Filed:

Feb 13, 2017

Appl. No.:

15/431573

Inventors:

- Cary NC, US
Ronald Agresta - Raleigh NC, US
Roger Jay Barney - Fuquay-Varina NC, US
Willem Abraham Hazejager - Cary NC, US

Assignee:

SAS Institute Inc. - Cary NC

International Classification:

G06F 9/48
G06F 17/30

Abstract:

An apparatus may include a processor and storage to store instructions that cause the processor to perform operations including: generate a current data set model descriptive of a characteristic of a current data set; compare the current data set model to at least one previously generated data set model descriptive of a characteristic of a previously analyzed data set; in response to detection of a match within a similarity threshold: retrieve an indication from a correlation database of an action previously performed on a previously analyzed data set; select a computer language based on node data descriptive of characteristics of a node device execution environment; generate node instructions in the selected computer language and based on the current data set model to cause the node device to perform the previously performed action on a portion of the current data set; and transmit the node instructions to the node device.