System and method for generating highly scalable temporal graph database

Patent metadata

Field	Value
Publication number	US-2021334312-A1
Application number	US-201916627331-A
Country	US
Kind code	A1
Filing date	Dec 17, 2019
Priority date	Dec 17, 2019
Publication date	Oct 28, 2021
Grant date	—

Abstract

Aspects of the present disclosure involve systems, methods, devices, and the like for generating highly scalable temporal graph databases. In one embodiment, a novel architecture is presented that enables the identification of mutation or changes in graphs. For the identification a combination of graph-based modeling and journal entry is used. Events occurring are consumed and changes are ingested, transformed for use by a graph simulation system. The changes are journaled using a vertex centric temporal journaling schema.

First claim

What is claimed is:

1 . A system comprising:
a non-transitory memory storing instructions; and a processor configured to execute instructions to cause the system to:
receiving, via a wireless network communication, an event update;
determining, by an instance graph, a graph mutation on a graph-based model, the graph mutation occurring based in part on the event update;
ingesting, by a graph simulation system, an event log for the graph mutation;
transforming the ingested event log to a temporal based journal entry; and
presenting model results based in part on the temporal based journal entry.

2 . The system of claim 1 , executing instructions further causes the system to:
documenting, by an event logging component, the graph mutation in an event log; and storing, by a logging database, the event log for the ingesting by the graph simulation system.

3 . The system of claim 1 , executing instructions further causes the system to:
ingesting, by a graph ingestion module, a simulated graph mutation for performing analysis by a graph reader.

4 . The system of claim 3 , wherein the simulated graph mutation is created by an external graph builder.

5 . The system of claim 1 , executing instructions further causes the system to:
deploying, by an orchestrator, queries simulation using the transformed temporal-based journal entry for the presenting.

6 . The system of claim 1 , wherein the temporal-based journal entry is vertex centric.

7 . The system of claim 1 , wherein the graph mutation includes a change on a graph of a vertex and includes a timestamp.

8 . A method comprising:
receiving an event log for a graph mutation; transforming the event log into a temporal graph-based journal entry, the temporal graph-based journal entry comprising:
receiving an indication of a change of a first node of a graph, the indication included in the event log;
determining a timestamp and property for the change based on a relationship with a second node;
journaling the change of the first node as associated with the second node, the timestamp, and the property; and
storing the temporal graph-based journal entry at a physical datastore.

9 . The method of claim 8 , further comprising:
ingesting, the event log received at a graph simulation system for the transforming.

10 . The method of claim 8 , further comprising:
accessing by the graph reader the status of a data for a given query and timestamp from the stored temporal graph-based journal entry at the physical datastore.

11 . The method of claim 8 , further comprising:
generating, a journal entry snapshot for the transformed event log.

12 . The method of claim 8 , further comprising:
receiving a simulated graph mutation; and accessing by the graph reader the status of a data for a given query and timestamp from the stored temporal graph-based journal entry at the physical datastore and the simulated graph mutation.

13 . The method of claim 8 , wherein the temporal graph-based journal entry is node centric and based on the second node.

14 . The method of claim 8 , wherein the change of the first node includes the removal of the first node.

15 . A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:
receiving an event update; determining a graph mutation on a graph-based model, the graph mutation occurring based in part on the event update; ingesting an event log for the graph mutation; transforming the ingested event log to a temporal based journal entry; and presenting model results based in part on the temporal based journal entry.

16 . The non-transitory medium of claim 15 , further comprising:
ingesting, by a graph ingestion module, a simulated graph mutation for performing analysis by a graph reader.

17 . The non-transitory medium of claim 15 , wherein the simulated graph mutation is created by an external graph builder.

18 . The non-transitory medium of claim 15 , further comprising:
deploying, by an orchestrator, queries simulation using the transformed temporal-based journal entry for the presenting.

19 . The non-transitory medium of claim 15 , wherein the temporal-based journal entry is vertex centric.

20 . The non-transitory medium of claim 15 , wherein the graph mutation includes a change on a graph of a vertex and includes a timestamp.

Assignees

• Paypal Inc

Inventors

• Neo Rey
• Ho Chun Kiat
• Song Lin
• Xie Kai
• Zuo Quin
• Li Xin
• Zhang Pengshan
• Zang Meng
• Yun Xiaohan
• Hennig Karl Anton
• Zhang Jun
• Doran Thomas
• Tso Jerry

Classifications

• G06F16/258

  Data format conversion from or to a database · CPC title

• H04L43/045

  for graphical visualisation of monitoring data · CPC title

• G06F16/2358

  Change logging, detection, and notification (replication G06F16/27) · CPC title

• H04L41/069

  using logs of notifications; Post-processing of notifications · CPC title

• H04L41/145 Primary

  involving simulating, designing, planning or modelling of a network · CPC title