METHOD AND SYSTEM FOR REFINING COLUMN MAPPINGS USING BYTE LEVEL ATTENTION BASED NEURAL MODEL

1 . A method for refining column mappings, the method comprising:

configuring a processing unit, the processing unit executing a plurality of computer instructions stored in a memory for:

configuring a synthetic data generator for generating synthetic data based on pre-existing mapping data;

receiving, in an encoder, the synthetic data and a plurality of input column names, each of the plurality of column names being a group of one or more bytes;

generating, by the encoder, an encoded data for each of the one or more bytes;

deploying the generated encoded data to train a deep learning (DL) model having a word level auto regressive decoder for identifying at least one meaning for each byte of each of the received plurality of input column names;

configuring a mapping output generator based on a plurality of pre-existing column descriptions;

using the mapping output generator to determine whether or not the identified at-least one meaning matches with at least one description of the plurality of column descriptions, and thereby obtain an error score; and

using the error score to fine tune the DL model for thereby providing refined meanings for a given column name and obtain corresponding mapping prediction output.

2 . The method of claim 1 , wherein the encoder is a byte level encoder.

3 . The method of claim 1 , wherein the encoder is a byte pair encoder.

4 . The method of claim 1 , wherein the DL model is an attention based neural model.

5 . The method of claim 1 , wherein the pre-existing mapping data includes one or more sample data received from one or more data sources.

6 . The method of claim 1 , wherein the DL model identifies the context of all columns of a given source table while mapping a current column name.

7 . The method of claim 1 , further comprising performing a quality check on the obtained mapping prediction output.

8 . A system for refining column mappings, the system comprising:

a processing unit executing a plurality of computer instructions stored in a memory to:

configure a synthetic data generator for generating synthetic data based on pre-existing mapping data;

receive, in an encoder, the synthetic data and a plurality of input column names, each of the plurality of column names being a group of one or more bytes;

generate, by using the encoder, an encoded data for each of the one or more bytes;

deploy the generated encoded data to train a deep learning (DL) model having a word level auto regressive decoder for identifying at least one meaning for each byte of each of the received plurality of input column names;

configure a mapping output generator based on a plurality of pre-existing column descriptions;

use the mapping output generator to determine whether or not the identified at-least one meaning matches with at least one description of the plurality of column descriptions, and thereby obtain an error score; and

use the error score to fine tune the DL model for thereby providing refined meanings for a given column name and obtain corresponding mapping prediction output.

9 . The system of claim 8 , wherein the encoder is a byte level encoder.

10 . The system of claim 8 , wherein the encoder is a byte pair encoder.

11 . The system of claim 8 , wherein the DL model is an attention based neural model.

12 . The system of claim 8 , wherein the pre-existing mapping data includes one or more sample data received from one or more data sources.

13 . The system of claim 8 , wherein the DL model identifies the context of all columns of a given source table while mapping a current column name.

14 . The system of claim 8 , further comprising performing a quality check on the obtained mapping prediction output.