公开(公告)号：US12242332B2

公开(公告)日：2025-03-04

申请号：US17962869

申请日：2022-10-10

Applicant: Oracle International Corporation

Inventor： Shwan Ashrafi ,  Michal Piotr Prussak ,  Hariharan Balasubramanian ,  Vijayalakshmi Krishnamurthy

IPC: G06F11/07

Abstract: Techniques are described for identifying root cause anomalies in time series. Information to be used for root cause analysis (RCA) is obtained from a graph neural network (GNN) and is used to construct a dependency graph having nodes corresponding to each time series and directed edges corresponding to dependencies between the time series. Nodes corresponding to time series that do not contain anomalies may be removed from this dependency graph, as well as edges connected to these nodes. This edge and node removal may result in the creation of one or more sub-graphs from the dependency graph. A root cause analysis algorithm may be run on these one or more sub-graphs to create a root cause graph for each sub-graph. These root cause graphs may then be used to identify root cause anomalies within the multiple time series, as well as sequences of anomalies within the multiple time series.

公开(公告)号：US20230297861A1

公开(公告)日：2023-09-21

申请号：US17696685

申请日：2022-03-16

Applicant: Oracle International Corporation

Inventor： Chirag Ahuja ,  Vikas Rakesh Upadhyay ,  Syed Fahad Allam Shah ,  Samik Raychaudhuri ,  Hariharan Balasubramanian ,  Michal Piotr Prussak ,  Shwan Ashrafi

IPC: G06N5/04 ,  G06F16/901

CPC classification number: G06N5/046 ,  G06F16/9024 ,  G06N20/00

Abstract: A computing device may access a graph comprising one or more model nodes, one or more dataset nodes, and one or more edges, the model nodes having a plurality of features. The device may add one or more test dataset nodes and test edges to the graph. The device may perform a series of iterative steps until a threshold is reached. For each iterative step: a selection probability is determined, the selection probability being based at least in part on a plurality of selection criteria; a particular model node is selected, the particular model node being selected based at least in part on the selection probability; the selection criteria is updated based at least in part on the particular model; and the plurality of features are updated based at least in part on the particular model. The device may provide the particular model node selected in the last iterative step.

公开(公告)号：US20240118965A1

公开(公告)日：2024-04-11

申请号：US17962869

申请日：2022-10-10

Applicant: Oracle International Corporation

Inventor： Shwan Ashrafi ,  Michal Piotr Prussak ,  Hariharan Balasubramanian ,  Vijayalakshmi Krishnamurthy

IPC: G06F11/07

CPC classification number: G06F11/079 ,  G06F11/0712

Abstract: Techniques are described for identifying root cause anomalies in time series. Information to be used for root cause analysis (RCA) is obtained from a graph neural network (GNN) and is used to construct a dependency graph having nodes corresponding to each time series and directed edges corresponding to dependencies between the time series. Nodes corresponding to time series that do not contain anomalies may be removed from this dependency graph, as well as edges connected to these nodes. This edge and node removal may result in the creation of one or more sub-graphs from the dependency graph. A root cause analysis algorithm may be run on these one or more sub-graphs to create a root cause graph for each sub-graph. These root cause graphs may then be used to identify root cause anomalies within the multiple time series, as well as sequences of anomalies within the multiple time series.