Map intuition system and method

• US 8,850,071 B2
• Filed: 05/10/2011
• Issued: 09/30/2014
• Est. Priority Date: 05/10/2010
• Status: Active Grant

• Current Assignee: Open Text Holdings, Inc. (Open Text Corporation)
• Original Assignee: Liaison Technologies Incorporated (Open Text Corporation)
• Inventors: Lindsay, Walter Hughes, Shellman, Joel Timothy
• Primary Examiner(s): RECEK, JASON D
• Application Number:
  US13/104,852
• Publication Number:
  US 20120023261A1
• Time in Patent Office: 1,239 Days
• Field of Search: 709/224, 709/223, 709/246
• US Class Current: 709/246
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models

Abstract

A map intuition system and method that involves machine learning techniques to analyze data sets and identify mappings and transformation rules as well as machine-human interactions to leverage human intuition and intelligence to rapidly complete a map.

Claims

1. An inference engine method for ranking and highlighting candidates in a map intuition system, comprising:

receiving at a processor a source data set having data with input field values, each source data set having a source data set definition describing the data in the source data set;

receiving at the processor a target data set having data with output field values Vo, each target data set having a target data set definition describing the data in the target data set;

said processor comparing each input field value with each output field value, identifying a degree of correspondence between each pair of values, ranking the source and output field values into clusters, and merging the rankings from a data level to a data definition level;

wherein the step of ranking the source and output field values into clusters comprises;

identifying, for each output field value Vo, a set No of other output field values within a predetermined distance from Vo;

identifying a set S of input items Vs related to No;

identifying, for each item Vs in S, a set Ni of other input field values within a second predetermined distance from Vs;

ranking at least one of the size, quality of match, and distinctiveness of match of the set Ni compared to No; and

determining, based upon a predetermined threshold, whether to increase the value of the degree of correspondence between items in Ni to items in No.

2. The inference engine method of claim 1, wherein the step of merging the rankings from a data level to a data definition level comprises:

examining each target data format definition in No;

identifying a set Co of correlations directly involving No, any descendants in the data format definition of No, and all mandatory descendants of an ancestor of No;

examining each source data format definition in Ni that strongly relates to a correlation in Co;

identifying the set Ci of correlations directly involving Ni, all descendants in the data format definition of Ni, and all mandatory descendants of an ancestor of Ni;

evaluating the quality of matches between Co and Ci;

identifying a numerical rating for the correlation; and

recording the match between Co and Ci as part of an output of the inference engine.

3. The inference engine method of claim 2, wherein the step of examining the target data format definition comprises performing a post-order traversal.

4. The inference engine method of claim 2, wherein the step of examining the source data format definition comprises performing a post-order traversal.