公开(公告)号：US20240126798A1

公开(公告)日：2024-04-18

申请号：US18203195

申请日：2023-05-30

Applicant: Oracle International Corporation

Inventor： Arno Schneuwly ,  Desislava Wagenknecht-Dimitrova ,  Felix Schmidt ,  Marija Nikolic ,  Matteo Casserini ,  Milos Vasic ,  Renata Khasanova

IPC: G06F16/34 ,  G06F16/335 ,  G06F40/186

CPC classification number: G06F16/345 ,  G06F16/335 ,  G06F40/186

Abstract: In an embodiment, a computer stores, in memory or storage, many explanation profiles, many log entries, and definitions of many features that log entries contain. Some features may contain a logic statement such as a database query, and these are specially aggregated based on similarity. Based on the entity specified by an explanation profile, statistics are materialized for some or all features. Statistics calculation may be based on scheduled batches of log entries or a stream of live log entries. At runtime, an inference that is based on a new log entry is received. Based on an entity specified in the new log entry, a particular explanation profile is dynamically selected. Based on the new log entry and statistics of features for the selected explanation profile, a local explanation of the inference is generated. In an embodiment, an explanation text template is used to generate the local explanation.

公开(公告)号：US20230376743A1

公开(公告)日：2023-11-23

申请号：US17748226

申请日：2022-05-19

Applicant: Oracle International Corporation

Inventor： Marija Nikolic ,  Nikola Milojkovic ,  Arno Schneuwly ,  Matteo Casserini ,  Milos Vasic ,  Renata Khasanova ,  Felix Schmidt

IPC: G06N3/08 ,  G06N20/00

CPC classification number: G06N3/08 ,  G06N3/088 ,  G06N20/00

Abstract: The present invention avoids overfitting in deep neural network (DNN) training by using multitask learning (MTL) and self-supervised learning (SSL) techniques when training a multi-branch DNN to encode a sequence. In an embodiment, a computer first trains the DNN to perform a first task. The DNN contains: a first encoder in a first branch, a second encoder in a second branch, and an interpreter layer that combines data from the first branch and the second branch. The DNN second trains to perform a second task. After the first and second trainings, production encoding and inferencing occur. The first encoder encodes a sparse feature vector into a dense feature vector from which an inference is inferred. In an embodiment, a sequence of log messages is encoded into an encoded trace. An anomaly detector infers whether the sequence is anomalous. In an embodiment, the log messages are database commands.

公开(公告)号：US20230362180A1

公开(公告)日：2023-11-09

申请号：US17739968

申请日：2022-05-09

Applicant: Oracle International Corporation

Inventor： Milos Vasic ,  Saeid Allahdadian ,  Matteo Casserini ,  Felix Schmidt ,  Andrew Brownsword

IPC: H04L9/40 ,  G06N20/20

CPC classification number: H04L63/1425 ,  G06N20/20

Abstract: Techniques for implementing a semi-supervised framework for purpose-oriented anomaly detection are provided. In one technique, a data item in inputted into an unsupervised anomaly detection model, which generates first output. Based on the first output, it is determined whether the data item represents an anomaly. In response to determining that the data item represents an anomaly, the data item is inputted into a supervised classification model, which generates second output that indicates whether the data item is unknown. In response to determining that the data item is unknown, a training instance is generated based on the data item. The supervised classification model is updated based on the training instance.