View source for 18468363. GRAPH DATA PROCESSING METHODS AND SYSTEMS simplified abstract (Alipay (Hangzhou) Information Technology Co., Ltd.)

=GRAPH DATA PROCESSING METHODS AND SYSTEMS=


==Organization Name==

[[:Category:Alipay (Hangzhou) Information Technology Co., Ltd.|Alipay (Hangzhou) Information Technology Co., Ltd.]]


[[Category:Alipay (Hangzhou) Information Technology Co., Ltd.]]

==Inventor(s)==

[[:Category:Zhiwei Peng of Hangzhou (CN)|Zhiwei Peng of Hangzhou (CN)]][[Category:Zhiwei Peng of Hangzhou (CN)]]

[[:Category:Lun Gao of Hangzhou (CN)|Lun Gao of Hangzhou (CN)]][[Category:Lun Gao of Hangzhou (CN)]]

[[:Category:Zhenxuan Pan of Hangzhou (CN)|Zhenxuan Pan of Hangzhou (CN)]][[Category:Zhenxuan Pan of Hangzhou (CN)]]

==GRAPH DATA PROCESSING METHODS AND SYSTEMS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18468363 titled 'GRAPH DATA PROCESSING METHODS AND SYSTEMS

==Simplified Explanation==

The abstract describes a patent application for a graph data processing system that includes a data storage system and a graph computing engine. The system is designed to store and process graph data generated from service data in consecutive time intervals.

* Data storage system stores graph data from service data in consecutive time intervals
* Graph computing engine processes graph data and computes graph features
* Graph computing engine searches and computes target graph data based on target versions

== Potential Applications ==
The technology can be applied in various industries such as social media analytics, network analysis, fraud detection, and recommendation systems.

== Problems Solved ==
1. Efficient processing of large-scale graph data
2. Real-time analysis of dynamic graph structures

== Benefits ==
1. Improved data processing speed and efficiency
2. Enhanced accuracy in computing graph features
3. Scalability to handle large volumes of graph data

== Potential Commercial Applications ==
Optimizing advertising campaigns, enhancing cybersecurity measures, improving personalized recommendations in e-commerce platforms.

== Possible Prior Art ==
One possible prior art could be existing graph data processing systems used in social media platforms or network analysis tools.

=== Unanswered Questions ===

=== How does this technology compare to existing graph data processing systems in terms of performance and scalability? ===
The article does not provide a direct comparison with existing systems in terms of performance and scalability. Further research or testing would be needed to determine the specific advantages of this technology over others.

=== What are the potential limitations or challenges in implementing this technology in real-world applications? ===
The article does not address potential limitations or challenges in implementing this technology. Factors such as data privacy concerns, integration with existing systems, and computational resource requirements could be important considerations in practical applications.


==Original Abstract Submitted==

Implementations of this specification provide graph data processing systems and methods. An example graph data processing system includes a data storage system and a graph computing engine. The data storage system is configured to store a plurality of pieces of graph data corresponding to a plurality of versions, where the plurality of pieces of graph data are generated based on a plurality of groups of service data generated by a target service system in a plurality of consecutive time intervals. The graph computing engine is configured to receive a graph feature computing request. The graph computing engine is further configured to search the plurality of pieces of graph data stored in the data storage system for a plurality of pieces of target graph data corresponding to the plurality of target versions, and compute graph features of the plurality of pieces of target graph data.



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