公开(公告)号：US20250111189A1

公开(公告)日：2025-04-03

申请号：US18375073

申请日：2023-09-29

Applicant: Oracle International Corporation

Inventor： Jonas Schweizer ,  Damien Hilloulin ,  Marouane Maatouk ,  Benedikt Schesch

IPC: G06N3/04

Abstract: In an embodiment, a computer hosts and operates an input neural layer of an artificial neural network that generates, based on all of the features of a first vertex of a first vertex type in a graph, an embedding of the first vertex. The embedding of the first vertex has a predefined size that does not depend on the first vertex type. The input neural layer generates, based on all of the features of a first edge of a first edge type in the graph, an embedding of the first edge. A subsequent neural layer of the artificial neural network generates an embedding of a second vertex of a second vertex type in the graph, and this generating is based on: the embedding of the first vertex and all of the features of the second vertex, including a particular feature that is not a feature of the first vertex type.

公开(公告)号：US20250139163A1

公开(公告)日：2025-05-01

申请号：US18384248

申请日：2023-10-26

Applicant: Oracle International Corporation

Inventor： Jonas Schweizer ,  Arnaud Delamare ,  Jinsu Lee ,  Sungpack Hong ,  Hassan Chafi ,  Vasileios Trigonakis

IPC: G06F16/901 ,  G06F11/32

Abstract: An estimator is provided that can be used to get an estimate of final graph size and peak memory usage of the graph during loading, based on sampling of the graph data and using machine learning (ML) techniques. A data sampler samples the data from files or databases and estimates some statistics about the final graph. The sampler also samples some information about property data. Given the sampled statistics gathered and estimated by the data sampler, a graph size estimator estimates how much memory is required by the graph processing engine to load the graph. The final graph size represents how much memory will be used to keep the final graph structures in memory once loading is completed. The peak memory usage represents the memory usage upper bound that is reached by the graph processing engine during loading.