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公开(公告)号：US11829419B1
公开(公告)日：2023-11-28
申请号：US17744653
申请日：2022-05-14
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Iraklis Psaroudakis , Mhd Yamen Haddad , Martin Sevenich
IPC: G06F16/23 , G06F16/901 , G06F16/903
CPC classification number: G06F16/9024 , G06F16/23 , G06F16/90335
Abstract: A system for loading graph data from an external store in response to a graph query is disclosed. In some embodiment, given a graph database where all vertices are stored in memory and some but not all edges are stored in the external store, the system performs one of two methods. In the first method, the system iteratively expands a set of vertices that is initially specified in the graph query and collects all edges connected to the set of vertices, including edges stored in the external store, that satisfy a vertex constraint also specified in the query. In the second method, the system finds a set of vertices that satisfy the vertex constraint and collects all edges connected to the set of vertices, including edges stored in an external store.
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公开(公告)号：US20240330130A1
公开(公告)日：2024-10-03
申请号：US18740689
申请日：2024-06-12
Applicant: Oracle International Corporation
Inventor： Miroslav Cepek , Iraklis Psaroudakis , Rhicheek Patra , Timothy Trovatelli
CPC classification number: G06F11/1476 , G06N3/04 , G06V30/18181
Abstract: Herein is machine learning for anomalous graph detection based on graph embedding, shuffling, comparison, and unsupervised training techniques that can characterize an unfamiliar graph. In an embodiment, a computer obtains many known vectors that respectively represent known graphs. A new vector is generated that represents a new graph that contains multiple vertices. The new vector may contain an arithmetic aggregation of vertex vectors that respectively represent multiple vertices and/or a vector that represents a virtual vertex that is connected to the multiple vertices by respective virtual edges. In the many known vectors, some similar vectors that are similar to the new vector are identified. The new graph is automatically characterized based on a subset of the known graphs that the similar vectors represent.
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公开(公告)号：US20240370500A1
公开(公告)日：2024-11-07
申请号：US18773452
申请日：2024-07-15
Applicant: Oracle International Corporation
Inventor： Aras Mumcuyan , Iraklis Psaroudakis , Miroslav Cepek , Rhicheek Patra
IPC: G06F16/903 , G06F18/2113 , G06F18/214 , G06F18/22 , G06F40/30 , G06N3/045 , G06N3/08 , G06N5/04
Abstract: Techniques are described herein for a Name Matching Engine that integrates two Machine Learning (ML) module options. The first ML module is a feature-engineered classifier that boosts text-based name matching techniques with a binary classifier ML model. The feature-engineered classifier comprises a first stage of text-based candidate finding, and a second stage in which a binary classifier model predicts whether each string, of the candidate match list, is a match or not. The binary classifier model is based on features from two or more of: a name feature level, a word feature level, a character feature level, and an initial feature level. The second ML module of the Name Matching Engine comprises an end-to-end Recurrent Neural Network (RNN) model that directly accepts name strings as a sequence of n-grams and generates learned text embeddings. The text embeddings of matching name strings are close to each other in the feature space.
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公开(公告)号：US20230214407A1
公开(公告)日：2023-07-06
申请号：US18174535
申请日：2023-02-24
Applicant: Oracle International Corporation
Inventor： Iraklis Psaroudakis , Stefan Kaestle , Daniel J. Goodman , Jean-Pierre Lozi , Matthias Grimmer , Timothy L. Harris
CPC classification number: G06F16/27 , G06F9/54 , G06F9/45558 , G06F9/45516 , G06F2009/45583 , G06F2009/4557 , G06F2009/45595
Abstract: Adaptive data collections may include various type of data arrays, sets, bags, maps, and other data structures. A simple interface for each adaptive collection may provide access via a unified API to adaptive implementations of the collection. A single adaptive data collection may include multiple, different adaptive implementations. A system configured to implement adaptive data collections may include the ability to adaptively select between various implementations, either manually or automatically, and to map a given workload to differing hardware configurations. Additionally, hardware resource needs of different configurations may be predicted from a small number of workload measurements. Adaptive data collections may provide language interoperability, such as by leveraging runtime compilation to build adaptive data collections and to compile and optimize implementation code and user code together. Adaptive data collections may also provide language-independent such that implementation code may be written once and subsequently used from multiple programming languages.
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公开(公告)号：US11461130B2
公开(公告)日：2022-10-04
申请号：US16883317
申请日：2020-05-26
Applicant: Oracle International Corporation
Inventor： Petr Koupy , Vasileios Trigonakis , Iraklis Psaroudakis , Jinsoo Lee , Sungpack Hong , Hassan Chafi
IPC: G06F9/48 , G06F9/448 , G06F16/901 , G06F11/07 , G06F9/38
Abstract: In an embodiment, a computer of a cluster of computers receives graph logic that specifies a sequence of invocations, including a current invocation and a next invocation, of parallelism operations that can detect whether the graph logic should prematurely terminate. The computer initiates, on the computers of the cluster, execution of the graph logic to process a distributed graph. Before the current invocation, the graph logic registers reversion logic for a modification of the distributed graph that execution of the graph logic has caused. During the current invocation, it is detected that the graph logic should prematurely terminate. Execution of the graph logic on the cluster is terminated without performing the next invocation in the sequence of invocations. The reversion logic reverses the modification of the distributed graph to restore consistency. The distributed graph is retained in volatile memory of the cluster for reuse such as relaunch of the graph logic.
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Patent Agency Ranking
公开(公告)号：US20210287069A1
公开(公告)日：2021-09-16
申请号：US16989306
申请日：2020-08-10
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Aras Mumcuyan , Iraklis Psaroudakis , Miroslav Cepek , Rhicheek Patra
IPC: G06N3/04 , G06F16/903 , G06K9/62 , G06N5/04 , G06F40/30
Abstract: Techniques are described herein for a Name Matching Engine that integrates two Machine Learning (ML) module options. The first ML module is a feature-engineered classifier that boosts text-based name matching techniques with a binary classifier ML model. The feature-engineered classifier comprises a first stage of text-based candidate finding, and a second stage in which a binary classifier model predicts whether each string, of the candidate match list, is a match or not. The binary classifier model is based on features from two or more of: a name feature level, a word feature level, a character feature level, and an initial feature level. The second ML module of the Name Matching Engine comprises an end-to-end Recurrent Neural Network (45RNN) model that directly accepts name strings as a sequence of n-grams and generates learned text embeddings. The text embeddings of matching name strings are close to each other in the feature space.
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公开(公告)号：US12189652B2
公开(公告)日：2025-01-07
申请号：US18174535
申请日：2023-02-24
Applicant: Oracle International Corporation
Inventor： Iraklis Psaroudakis , Stefan Kaestle , Daniel J. Goodman , Jean-Pierre Lozi , Matthias Grimmer , Timothy L. Harris
Abstract: Adaptive data collections may include various type of data arrays, sets, bags, maps, and other data structures. A simple interface for each adaptive collection may provide access via a unified API to adaptive implementations of the collection. A single adaptive data collection may include multiple, different adaptive implementations. A system configured to implement adaptive data collections may include the ability to adaptively select between various implementations, either manually or automatically, and to map a given workload to differing hardware configurations. Additionally, hardware resource needs of different configurations may be predicted from a small number of workload measurements. Adaptive data collections may provide language interoperability, such as by leveraging runtime compilation to build adaptive data collections and to compile and optimize implementation code and user code together. Adaptive data collections may also provide language-independent such that implementation code may be written once and subsequently used from multiple programming languages.
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公开(公告)号：US20230004977A1
公开(公告)日：2023-01-05
申请号：US17363515
申请日：2021-06-30
Applicant: Oracle International Corporation
Inventor： Miroslav Cepek , Iraklis Psaroudakis , Nina Corvelo Benz
Abstract: In an embodiment, a computer stores a bipartite graph that consists of a source subgraph and a target subgraph. Each vertex in the bipartite graph represents an entity. The source subgraph and the target subgraph are connected by many similarity edges. Each similarity edge indicates an original amount of similarity between the entity of a source vertex in the source subgraph and the entity of a target vertex in the target subgraph. For each similarity edge, the computer determines: a set of neighbor source vertices that are reachable from the source vertex of the similarity edge by traversing at most a source radius count of source edges in the source subgraph, a set of neighbor target vertices that are reachable from the target vertex of the similarity edge by traversing at most a target radius count of target edges in the target subgraph, and various amounts based on graph topology. For each similarity edge, the computer calculates a new amount of similarity based on those various amounts.
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公开(公告)号：US20210373938A1
公开(公告)日：2021-12-02
申请号：US16883317
申请日：2020-05-26
Applicant: Oracle International Corporation
Inventor： Petr Koupy , Vasileios Trigonakis , Iraklis Psaroudakis , Jinsoo Lee , Sungpack Hong , Hassan Chafi
IPC: G06F9/48 , G06F9/448 , G06F9/38 , G06F11/07 , G06F16/901
Abstract: In an embodiment, a computer of a cluster of computers receives graph logic that specifies a sequence of invocations, including a current invocation and a next invocation, of parallelism operations that can detect whether the graph logic should prematurely terminate. The computer initiates, on the computers of the cluster, execution of the graph logic to process a distributed graph. Before the current invocation, the graph logic registers reversion logic for a modification of the distributed graph that execution of the graph logic has caused. During the current invocation, it is detected that the graph logic should prematurely terminate. Execution of the graph logic on the cluster is terminated without performing the next invocation in the sequence of invocations. The reversion logic reverses the modification of the distributed graph to restore consistency. The distributed graph is retained in volatile memory of the cluster for reuse such as relaunch of the graph logic.
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公开(公告)号：US20210042323A1
公开(公告)日：2021-02-11
申请号：US17067479
申请日：2020-10-09
Applicant: Oracle International Corporation
Inventor： Iraklis Psaroudakis , Stefan Kaestle , Daniel J. Goodman , Jean-Pierre Lozi , Matthias Grimmer , Timothy L. Harris
Abstract: Adaptive data collections may include various type of data arrays, sets, bags, maps, and other data structures. A simple interface for each adaptive collection may provide access via a unified API to adaptive implementations of the collection. A single adaptive data collection may include multiple, different adaptive implementations. A system configured to implement adaptive data collections may include the ability to adaptively select between various implementations, either manually or automatically, and to map a given workload to differing hardware configurations. Additionally, hardware resource needs of different configurations may be predicted from a small number of workload measurements. Adaptive data collections may provide language interoperability, such as by leveraging runtime compilation to build adaptive data collections and to compile and optimize implementation code and user code together. Adaptive data collections may also provide language-independent such that implementation code may be written once and subsequently used from multiple programming languages.
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