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    Automatic Selection Of An Abstract Data Type
    31.
    发明申请
    Automatic Selection Of An Abstract Data Type 审中-公开
    自动选择抽象数据类型

    公开(公告)号：US20150331683A1

    公开(公告)日：2015-11-19

    申请号：US14276895

    申请日：2014-05-13

    Applicant: Oracle International Corporation

    Inventor： Martin Sevenich ,  Sungpack Hong ,  Hassan Chafi

    IPC: G06F9/45

    CPC classification number: G06F8/443 ,  G06F8/437

    Abstract: An implementation of an abstract data type is automatically selected by a compiler. The compiler chooses an implementation for each instance of an abstract data type in a program based on operations performed in the instance within the program.

    Abstract translation: 抽象数据类型的实现由编译器自动选择。 编译器根据程序中的实例执行的操作，在程序中为抽象数据类型的每个实例选择一个实现。

    System For Applying Transformation To Improve Graph Analysis
    32.
    发明申请
    System For Applying Transformation To Improve Graph Analysis 有权
    应用变换的系统来改善图形分析

    公开(公告)号：US20140189665A1

    公开(公告)日：2014-07-03

    申请号：US13733424

    申请日：2013-01-03

    Applicant: ORACLE INTERNATIONAL CORPORATION

    Inventor： Sungpack Hong ,  Hassan Chafi ,  Eric Sedlar

    IPC: G06F9/45

    CPC classification number: G06F8/443

    Abstract: A method for performing a neighbor-flipping transformation is provided. In one embodiment, a graph analysis program for computing a function relating to nodes in a directed graph is obtained and analyzed for neighborhood iterating operations, in which a function is computed over sets of nodes in the graph. For any detected neighborhood iterating operation, the method transforms the iterating operation by reversing the neighbor node relationship between the nodes in the operation. The transformed operation computes the same value for the function as the operation prior to transformation. The method alters the neighbor node relationship automatically, so that a user does not have to recode the graph analysis program. In some cases, the method includes construction of edges in the reverse direction while retaining the original edges in addition to performing the transformation.

    Abstract translation: 提供了一种执行相邻翻转变换的方法。 在一个实施例中，获得用于计算与有向图中的节点有关的功能的图分析程序，并对其进行邻域迭代操作进行分析，其中在图中的节点集合上计算函数。 对于任何检测到的邻域迭代操作，该方法通过在操作中颠倒节点之间的邻居节点关系来转换迭代操作。 转换的操作计算与变换之前的操作相同的函数值。 该方法自动更改邻居节点关系，使用户不必重新编码图形分析程序。 在某些情况下，除了执行变换之外，该方法还包括沿相反方向构造边缘，同时保留原始边缘。

    TRANSFORMER-BASED HYBRID RECOMMENDATION MODEL WITH CONTEXTUAL FEATURE SUPPORT
    33.
    发明申请
    TRANSFORMER-BASED HYBRID RECOMMENDATION MODEL WITH CONTEXTUAL FEATURE SUPPORT 有权

    公开(公告)号：US20250156637A1

    公开(公告)日：2025-05-15

    申请号：US18505293

    申请日：2023-11-09

    Applicant: Oracle International Corporation

    Inventor： Cesare Bernardis ,  Damien Hilloulin ,  Rhicheek Patra ,  Sungpack Hong ,  Hassan Chafi

    IPC: G06F40/284 ,  G06F16/9535

    Abstract: In a computer-implemented embodiment, an interaction machine learning model is trained based on many interactions on many resources. A context lexical token is inferred that represents a current operational context of a user. The context lexical token is inserted into a sequence of other inferred lexical tokens. From the context lexical token within the sequence of tokens, the interaction machine learning model infers a predicted resource that will be accessed next. In an embodiment, accelerated matchmaking entails suitability measurement by a dot product of a) a dynamically inferred user embedding that is based on the context lexical token and b) a statically inferred item embedding.

    Invalid traffic detection using explainable unsupervised graph ML
    34.
    发明授权
    Invalid traffic detection using explainable unsupervised graph ML 有权

    公开(公告)号：US12184692B2

    公开(公告)日：2024-12-31

    申请号：US17558342

    申请日：2021-12-21

    Applicant: Oracle International Corporation

    Inventor： Valentin Venzin ,  Rhicheek Patra ,  Sungpack Hong ,  Hassan Chafi

    IPC: H04L9/40 ,  G06N20/00

    Abstract: Herein are graph machine learning explainability (MLX) techniques for invalid traffic detection. In an embodiment, a computer generates a graph that contains: a) domain vertices that represent network domains that received requests and b) address vertices that respectively represent network addresses from which the requests originated. Based on the graph, domain embeddings are generated that respectively encode the domain vertices. Based on the domain embeddings, multidomain embeddings are generated that respectively encode the network addresses. The multidomain embeddings are organized into multiple clusters of multidomain embeddings. A particular cluster is detected as suspicious. In an embodiment, an unsupervised trained graph model generates the multidomain embeddings. Based on the clusters of multidomain embeddings, feature importances are unsupervised trained. Based on the feature importances, an explanation is automatically generated for why an object is or is not suspicious. The explained object may be a cluster or other batch of network addresses or a single network address.

    SUBQUERIES IN DISTRIBUTED ASYNCHRONOUS GRAPH QUERIES
    36.
    发明公开
    SUBQUERIES IN DISTRIBUTED ASYNCHRONOUS GRAPH QUERIES 审中-公开

    公开(公告)号：US20240220495A1

    公开(公告)日：2024-07-04

    申请号：US18091242

    申请日：2022-12-29

    Applicant: Oracle International Corporation

    Inventor： Vasileios Trigonakis ,  Anton Ragot ,  Yahya Ez-zainabi ,  Tomas Faltin ,  Sungpack Hong ,  Hassan Chafi

    IPC: G06F16/2453 ,  G06F16/901

    CPC classification number: G06F16/24535 ,  G06F16/24537 ,  G06F16/9024

    Abstract: A graph processing engine is provided for executing a graph query comprising a parent query and a subquery nested within the parent query. The subquery uses a reference to one or more correlated variables from the parent query. Executing the graph query comprises initiating execution of the parent query, pausing the execution of the parent query responsive to the parent query matching the one or more correlated variables in an intermediate result set, generating a subquery identifier for each match of the one or more correlated variables, modifying the subquery to include a subquery aggregate function and a clause to group results by subquery identifier, executing the modified subquery using the intermediate result set and collecting subquery results into a subquery results table responsive to pausing execution of the parent query, and resuming execution of the parent query using the subquery results table.

    Duplication elimination in depth based searches for distributed systems
    37.
    发明授权
    Duplication elimination in depth based searches for distributed systems 有权

    公开(公告)号：US12001425B2

    公开(公告)日：2024-06-04

    申请号：US17116831

    申请日：2020-12-09

    Applicant: Oracle International Corporation

    Inventor： Tomas Faltin ,  Vasileios Trigonakis ,  Jean-Pierre Lozi ,  Sungpack Hong ,  Hassan Chafi

    IPC: G06F16/2452 ,  G06F16/2455 ,  G06F16/248

    CPC classification number: G06F16/24526 ,  G06F16/24556 ,  G06F16/248

    Abstract: Systems and methods for improving evaluation of graph queries through depth first traversals are described herein. In an embodiment, a multi-node system evaluates against graph data a graph query that specifies a particular pattern to match by determining, at a first node of the multi-node system, in a particular instance of evaluating the graph query, that one or more first vertices on the first node match a first portion of the graph query and that a second vertex that is to be evaluated next is stored on a second node separate from the first node. In response to determining that the next vertex to be evaluated is stored on the second node separate from the first node, the first node generates a message to the second node comprising one or more results of the first portion of the graph query based on the one or more first vertices, an identifier of the next vertex, and a current stage of evaluating the graph query. In response to generating the message from the first node to the second node, the first node ceases the particular instance of evaluating the graph query.

    LEARNING HYPER-PARAMETER SCALING MODELS FOR UNSUPERVISED ANOMALY DETECTION
    38.
    发明公开
    LEARNING HYPER-PARAMETER SCALING MODELS FOR UNSUPERVISED ANOMALY DETECTION 审中-公开

    公开(公告)号：US20240095604A1

    公开(公告)日：2024-03-21

    申请号：US18075784

    申请日：2022-12-06

    Applicant: Oracle International Corporation

    Inventor： Fatjon Zogaj ,  Yasha Pushak ,  Hesam Fathi Moghadam ,  Sungpack Hong ,  Hassan Chafi

    IPC: G06N20/20

    CPC classification number: G06N20/20

    Abstract: A computer sorts empirical validation scores of validated training scenarios of an anomaly detector. Each training scenario has a dataset to train an instance of the anomaly detector that is configured with values for hyperparameters. Each dataset has values for metafeatures. For each predefined ranking percentage, a subset of best training scenarios is selected that consists of the ranking percentage of validated training scenarios having the highest empirical validation scores. Linear optimizers train to infer a value for a hyperparameter. Into many distinct unvalidated training scenarios, a scenario is generated that has metafeatures values and hyperparameters values that contains the value inferred for that hyperparameter by a linear optimizer. For each unvalidated training scenario, a validation score is inferred. A best linear optimizer is selected having a highest combined inferred validation score. For a new dataset, the best linear optimizer infers a value of that hyperparameter.

    CHROMOSOME REPRESENTATION LEARNING IN EVOLUTIONARY OPTIMIZATION TO EXPLOIT THE STRUCTURE OF ALGORITHM CONFIGURATION
    39.
    发明公开
    CHROMOSOME REPRESENTATION LEARNING IN EVOLUTIONARY OPTIMIZATION TO EXPLOIT THE STRUCTURE OF ALGORITHM CONFIGURATION 审中-公开

    公开(公告)号：US20240070471A1

    公开(公告)日：2024-02-29

    申请号：US17900779

    申请日：2022-08-31

    Applicant: Oracle International Corporation

    Inventor： Yasha Pushak ,  Moein Owhadi Kareshk ,  Hesam Fathi Moghadam ,  Sungpack Hong ,  Hassan Chafi

    IPC: G06N3/12

    CPC classification number: G06N3/126

    Abstract: Principal component analysis (PCA) accelerates and increases accuracy of genetic algorithms. In an embodiment, a computer generates many original chromosomes. Each original chromosome contains a sequence of original values. Each position in the sequences in the original chromosomes corresponds to only one respective distinct parameter in a set of parameters to be optimized. Based on the original chromosomes, many virtual chromosomes are generated. Each virtual chromosome contains a sequence of numeric values. Positions in the sequences in the virtual chromosomes do not correspond to only one respective distinct parameter in the set of parameters to be optimized. Based on the virtual chromosomes, many new chromosomes are generated. Each new chromosome contains a sequence of values. Each position in the sequences in the new chromosomes corresponds to only one respective distinct parameter in the set of parameters to be optimized. The computer may be configured based on a best new chromosome.

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