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Joseph Visgitus Phones & Addresses

  • Vestal, NY
  • 63 Glann Rd, Apalachin, NY 13732
  • Endwell, NY
  • Windsor, NY
  • Tioga, NY
  • 3690 Struble Rd, Endicott, NY 13760

Work

Position: Professional/Technical

Education

Degree: Graduate or professional degree

Publications

Us Patents

Framework For Evaluating Data Cleansing Applications

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US Patent:
7370057, May 6, 2008
Filed:
Dec 3, 2002
Appl. No.:
10/308760
Inventors:
Douglas R. Burdick - Ithaca NY, US
Robert J. Szczerba - Endicott NY, US
Joseph H. Visgitus - Endwell NY, US
Assignee:
Lockheed Martin Corporation - Bethesda MD
International Classification:
G06F 17/30
US Classification:
707102, 707 6
Abstract:
A system evaluates a first data cleansing application and a second data cleansing application. The system includes a test data generator, an application execution module, and a results reporting module. The test data generator creates a dirty set of sample data from a clean set of data. The application execution module cleanses the dirty set of sample data. The application execution module utilizes the first data cleansing application to cleanse the dirty set of sample data and create a first cleansed output. The application execution module further utilizes the second data cleansing application to cleanse the dirty set of sample data and create a second cleansed output. The results reporting module evaluates the first and second cleansed output. The results reporting module produces an output of scores and statistics for each of the first and second data cleansing applications.

Architecture For A Data Cleansing Application

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US Patent:
20040107203, Jun 3, 2004
Filed:
Dec 3, 2002
Appl. No.:
10/308788
Inventors:
Douglas Burdick - Ithaca NY, US
Robert Szczerba - Endicott NY, US
Joseph Visgitus - Endwell NY, US
Assignee:
Lockheed Martin Corporation
International Classification:
G06F017/00
G06F007/00
US Classification:
707/101000
Abstract:
A system cleanses data. The system includes an input component, a pre-process component, an automated learning component, and a post-process component. The input component receives a collection of records. The pre-process component formats the collection of records and creates a plan for cleansing the collection of records. The automated learning component performs the plan and modifies the plan based on feedback from intermediate steps within the plan. The post-process evaluation component evaluates the result of the automated learning component. The post-process component determines whether to accept the result or to feed back the result to the automated learning component.
Joseph M Visgitus from Vestal, NY, age ~35 Get Report