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Context-blind data transformation using index string matching

A context and data technology, applied in the direction of digital data processing, natural language data processing, special data processing applications, etc., can solve problems such as impossible and difficult to identify data

Active Publication Date: 2018-09-07
ORACLE INT CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

That is, the data may be context indeterminate, making the context of the data not easily discernible
Furthermore, the source data may include multiple different contexts such that contexts for different subsets of the source data may appear in the data
In this regard, context-sensitive transformations may not be possible using traditional methods of identifying the context used in context-sensitive data transformations

Method used

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  • Context-blind data transformation using index string matching
  • Context-blind data transformation using index string matching
  • Context-blind data transformation using index string matching

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Embodiment Construction

[0050] While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and described in detail herein. It should be understood, however, that there is no intention to limit the invention to the particular forms disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the invention as defined by the appended claims.

[0051] The present invention relates to transforming data from a first or source form to a second or target form. As noted above, such transformations may be desirable in a variety of applications involving, for example, importing or otherwise populating data into information systems, processing search queries, exchanging information between information systems, and translate. Furthermore, such transformations may depend, at least in part, on the context of the data in the source form. For...

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Abstract

It is used to convert data from the first form to the second form without referring to the computer -based tools and methods to context to context to be converted.This conversion can be promoted by matching the source data with external information (for example, the rules of the context) included in the data conversion (e.g., the rules specific in the context).The matching can be performed based on the optimized indexing string matching technology. This technology can operate to match the source data to the context -related external information without requiring the context of the external information that is identified or identified.Correspondingly, the data conversion can be performed in an unsupervised machine learning environment.

Description

[0001] Cross References to Related Applications [0002] This application is a continuation-in-part of U.S. Patent Application No. 13 / 596,844, entitled "SCALABLE STRING MATCHING AS ACOMPONENT FOR UNSUPERVISED LEARNING IN SEMANTIC META-MODEL DEVELOPMENT," filed August 28, 2012, which is incorporated by reference in its entirety fully incorporated here. technical field [0003] The present disclosure relates generally to the use of computer-based tools to transform data from a first form to a second form, and more particularly to index string matching that can be utilized in conjunction with an unsupervised learning process to at least partially utilize A computer-based tool for transforming context-agnostic data based on patterns selected by matching the context-sensitive patterns to index strings of source data used when transforming the source data. Background technique [0004] In various applications, it may be desirable to transform data from a first (or input) form to ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/22G06N20/00
CPCG06F16/258G06F40/151G06F16/90344G06N20/00G06F18/214
Inventor P·戈伦L·里瓦斯E·A·格林
Owner ORACLE INT CORP
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