Host, storage system including the host, and operating method of the host

    公开(公告)号:US12117985B2

    公开(公告)日:2024-10-15

    申请号:US17879511

    申请日:2022-08-02

    CPC classification number: G06F16/2246 G06F16/9027

    Abstract: A host, a storage system, and an operating method of the host are provided. The host includes a host memory configured to store a tree structure including a leaf node and an index node, an index management module configured to manage an index based on the tree structure and generate a first log corresponding to the leaf node based on a first update request corresponding to a first key-value entry included in the leaf node, and a device driver configured to generate a first write command corresponding to the first log and transmit the generated first write command to a key-value storage device, so as to store the first log in the key-value storage device. The index management module is configured to generate a first new key-value entry, the first new-key value entry including a first value updated based on the first update request, as the first log.

    MACHINE LEARNING RISK DETERMINATION SYSTEM FOR TREE BASED MODELS

    公开(公告)号:US20240273390A1

    公开(公告)日:2024-08-15

    申请号:US18471659

    申请日:2023-09-21

    CPC classification number: G06N5/045 G06F16/9027 G06N7/01 G06N20/00

    Abstract: The present disclosure describes systems and methods for determining correlation codes for tree-based decisioning models. In one embodiment, a method for determining correlation codes in a tree-based decision model includes: assigning each decision node in a tree-based decision model to a correlation code; initializing a risk sum for each correlation code; calculating, for all decision nodes in the tree-based decision model, a difference in risk between child nodes and respective parent nodes; updating the risk sum for each correlation code associated with the decision node used in the decision for the node; determining the feature with the highest risk sum; and determining the correlation code associated with the determined decision node.

    Dynamically-qualified aggregate relationship system in genealogical databases

    公开(公告)号:US12045287B2

    公开(公告)日:2024-07-23

    申请号:US17730480

    申请日:2022-04-27

    Inventor: Jeff Phillips

    CPC classification number: G06F16/9027 G06F16/285

    Abstract: Methods and systems for creating a cluster view person for genealogical studies. Methods may include obtaining a plurality of genealogical trees. Each of the genealogical trees may include a plurality of interconnected nodes representing individuals that are related to each other. Methods may also include identifying one or more of the genealogical trees that contain a similar individual. Whether two individuals are grouped may depend on similarity and/or quality thresholds. Methods may include creating an aggregate individual including each of the similar individuals in each of the identified genealogical trees. The aggregate individual may combine information from each of the similar individuals.

    Intelligent clustering systems and methods useful for domain protection

    公开(公告)号:US12038983B2

    公开(公告)日:2024-07-16

    申请号:US18179912

    申请日:2023-03-07

    CPC classification number: G06F16/906 G06F16/9027

    Abstract: An intelligent clustering system has a dual-mode clustering engine for mass-processing and stream-processing. A tree data model is utilized to describe heterogenous data elements in an accurate and uniform way and to calculate a tree distance between each data element and a cluster representative. The clustering engine performs element clustering, through sequential or parallel stages, to cluster the data elements based at least in part on calculated tree distances and parameter values reflecting user-provided domain knowledge on a given objective. The initial clusters thus generated are fine-tuned by undergoing an iterative self-tuning process, which continues when new data is streamed from data source(s). The clustering engine incorporates stage-specific domain knowledge through stage-specific configurations. This hybrid approach combines strengths of user domain knowledge and machine learning power. Optimized clusters can be used by a prediction engine to increase prediction performance and/or by a network security specialist to identify hidden patterns.

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