Data integration demand management using artificial intelligence

Patent metadata

Field	Value
Publication number	US-11599793-B2
Application number	US-202016776900-A
Country	US
Kind code	B2
Filing date	Jan 30, 2020
Priority date	Jan 30, 2020
Publication date	Mar 7, 2023
Grant date	Mar 7, 2023

Abstract

Methods, apparatus, and processor-readable storage media for data integration demand management using artificial intelligence are provided herein. An example computer-implemented method includes obtaining at least one data integration demand, wherein the at least one data integration demand comprises textual information provided by at least one user; determining multiple parameters of the at least one data integration demand by applying one or more machine learning natural language processing techniques to at least a portion of the textual information provided by the at least one user; generating at least one delivery date prediction for the at least one data integration demand by applying one or more artificial intelligence techniques to the multiple determined parameters of the at least one data integration demand; and performing one or more automated actions based at least in part on the at least one generated delivery date prediction.

First claim

What is claimed is:

1. A computer-implemented method comprising:
obtaining at least one data integration demand, wherein the at least one data integration demand comprises textual information provided by at least one user;
determining multiple parameters of the at least one data integration demand by applying one or more machine learning natural language processing techniques to at least a portion of the textual information provided by the at least one user;
generating at least one delivery date prediction for the at least one data integration demand by applying one or more artificial intelligence techniques to the multiple determined parameters of the at least one data integration demand, wherein applying the one or more artificial intelligence techniques comprises processing at least a portion of the multiple determined parameters using at least one artificial neural network, and wherein one or more neurons of at least one input layer of the at least one artificial neural network correspond to one or more delay sources associated with the at least one generated delivery date prediction; and
performing one or more automated actions based at least in part on the at least one generated delivery date prediction, wherein performing the one or more automated actions comprises automatically modifying one or more resource allocations, within at least one enterprise associated with resolving the at least one data integration demand, in accordance with the at least one generated delivery date prediction;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.

2. The computer-implemented method of claim 1 , wherein the at least one artificial neural network comprises at least one artificial neural network multilayer perceptron.

3. The computer-implemented method of claim 2 , wherein one or more neurons of at least one sub-layer of an input layer of the at least one artificial neural network multilayer perceptron correspond to one or more delay sources associated with the at least one generated delivery date prediction.

4. The computer-implemented method of claim 3 , wherein activation of one of the one or more neurons represents plausibility of a respective one of the one or more delay sources being a source of delay, of an amount above a given threshold level, associated with the at least one generated delivery date prediction.

5. The computer-implemented method of claim 1 , wherein performing the one or more automated actions comprises computing a confidence value attributed to the at least one generated delivery data prediction based at least in part on a level of complexity associated with the at least one data integration demand.

6. The computer-implemented method of claim 1 , wherein performing the one or more automated actions comprises outputting, to at least one integration repository, the at least one generated delivery date prediction and the multiple determined parameters of the at least one data integration demand.

7. The computer-implemented method of claim 1 , wherein performing the one or more automated actions comprises training the one or more artificial intelligence techniques using the at least one generated delivery date prediction and the multiple determined parameters of the at least one data integration demand.

8. The computer-implemented method of claim 1 , wherein performing the one or more automated actions comprises outputting, to the at least one user, the at least one generated delivery date prediction.

9. The computer-implemented method of claim 8 , further comprising:
modifying at least one of the multiple determined parameters of the at least one data integration demand in response to input from the at least one user obtained in connection with the at least one generated delivery date prediction.

10. The computer-implemented method of claim 1 , wherein the multiple parameters comprise information pertaining to two or more of: digital segment, initial delivery date, application status, type of engagement, type of data, external integration, average volume, maximum volume, average payload size, maximum payload size, parallelism, message service level agreements, message orchestration, message enrichment, one or more necessary security levels, one or more integration products, at least one data sender, at least one data receiver, product stability, and integration complexity.

11. A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:
to obtain at least one data integration demand, wherein the at least one data integration demand comprises textual information provided by at least one user;
to determine multiple parameters of the at least one data integration demand by applying one or more machine learning natural language processing techniques to at least a portion of the textual information provided by the at least one user;
to generate at least one delivery date prediction for the at least one data integration demand by applying one or more artificial intelligence techniques to the multiple determined parameters of the at least one data integration demand, wherein applying the one or more artificial intelligence techniques comprises processing at least a portion of the multiple determined parameters using at least one artificial neural network, and wherein one or more neurons of at least one input layer of the at least one artificial neural network correspond to one or more delay sources associated with the at least one generated delivery date prediction; and
to perform one or more automated actions based at least in part on the at least one generated delivery date prediction, wherein performing the one or more automated actions comprises automatically modifying one or more resource allocations, within at least one enterprise associated with resolving the at least one data integration demand, in accordance with the at least one generated delivery date prediction.

12. The non-transitory processor-readable storage medium of claim 11 , wherein the at least one artificial neural network comprises at least one artificial neural network multilayer perceptron.

13. The non-transitory processor-readable storage medium of claim 11 , wherein performing the one or more automated actions comprises computing a confidence value attributed to the at least one generated delivery data prediction based at least in part on a level of complexity associated with the at least one data integration demand.

14. An apparatus comprising:
at least one processing device comprising a processor coupled to a memory;
the at least one processing device being configured:
to obtain at least one data integration demand, wherein the at least one data integration demand comprises textual information provided by at least one user;
to determine multiple parameters of the at least one data integration demand by applying one or more machine learning natural language processing techniques to at least a portion of the textual information provided by the at least one user;
to generate at least one delivery date prediction for the at least one data integration demand by applying one or more artificial intelligence techniques to the multiple determined parameters of the at least one data integration demand, wherein applying the one or more artificial intelligence techniques comprises processing at least a portion of the multiple determined parameters using at least one artificial neural network, and wherein one or more neurons of at l

Assignees

• Dell Products Lp

Inventors

• Dinh Hung T
• Govindraj Sandeep
• Venkata Ranjani Muthyam
• Syed Sabu K
• Ramu Kannappan

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/0985

  Hyperparameter optimisation; Meta-learning; Learning-to-learn · CPC title

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title

• G06N20/00

  Machine learning · CPC title