View source for 18545290. Big Data Based Predictive Graph Generation System simplified abstract (Bank of America Corporation)

=Big Data Based Predictive Graph Generation System=


==Organization Name==

[[:Category:Bank of America Corporation|Bank of America Corporation]]


[[Category:Bank of America Corporation]]

==Inventor(s)==

[[:Category:Harish Ragavan of W. Windsor NJ (US)|Harish Ragavan of W. Windsor NJ (US)]][[Category:Harish Ragavan of W. Windsor NJ (US)]]

[[:Category:Srinivasan Shanmugam of Highland Park NJ (US)|Srinivasan Shanmugam of Highland Park NJ (US)]][[Category:Srinivasan Shanmugam of Highland Park NJ (US)]]

==Big Data Based Predictive Graph Generation System - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18545290 titled 'Big Data Based Predictive Graph Generation System

==Simplified Explanation==

The patent application describes a big data analysis system that includes a big data repository, a data accumulation server, and a predictive graph processing system. The data accumulation server receives information from various data sources related to user interactions with computing devices, stores this information in the big data repository, and monitors the data sources to update the stored information. The predictive graph processing system transforms the information from the big data repository into a predictive graph data set based on a predictive model and stores it in a visualization data repository.

* The system includes a big data repository, a data accumulation server, and a predictive graph processing system.
* The data accumulation server receives information from multiple data sources, stores it in the big data repository, and updates the stored data.
* The predictive graph processing system transforms the information from the big data repository into a predictive graph data set based on a predictive model and stores it in a visualization data repository.

== Potential Applications ==
The technology described in this patent application could be applied in various industries such as finance, healthcare, marketing, and cybersecurity for predictive analytics, trend analysis, and decision-making support.

== Problems Solved ==
This technology helps organizations efficiently analyze and process large volumes of data from multiple sources to generate predictive insights and improve decision-making processes.

== Benefits ==
The system offers real-time monitoring, predictive analytics, and visualization capabilities, enabling organizations to make data-driven decisions, identify trends, and optimize operations.

== Potential Commercial Applications ==
The technology could be used in industries such as e-commerce, telecommunications, and manufacturing for customer behavior analysis, predictive maintenance, and fraud detection.

== Possible Prior Art ==
One possible prior art for this technology could be existing big data analytics platforms that offer similar functionalities for data processing, analysis, and visualization.

=== What are the security measures in place to protect the data stored in the big data repository? ===

The security measures in place to protect the data stored in the big data repository may include encryption, access control mechanisms, data masking techniques, and regular security audits to ensure data integrity and confidentiality.

=== How does the predictive graph processing system handle scalability and performance issues when processing large volumes of data? ===

The predictive graph processing system may employ distributed computing techniques, parallel processing algorithms, and optimized data storage mechanisms to handle scalability and performance issues when processing large volumes of data efficiently.


==Original Abstract Submitted==

A big data analysis system may include a big data repository communicatively coupled to a data accumulation server and a predictive graph processing system. The data accumulation server may be configured to receive information from a plurality of data sources, the information corresponding to user interaction with one or more computing devices associated with an organization via a networked computing system, store the information received from the plurality of sources in the big data repository; and monitor the plurality of data sources to update the data stored in the big data repository. The predictive graph processing system is configured to receive information stored in the big data repository, transform the information received from the big data repository into a predictive graph data set based on a predictive model, and store the predictive graph data set to a visualization data repository.



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