ACCESS-FREQUENCY-BASED ENTITY REPLICATION TECHNIQUES FOR DISTRIBUTED PROPERTY GRAPHS WITH SCHEMA

    公开(公告)号:US20230281219A1

    公开(公告)日:2023-09-07

    申请号:US17686938

    申请日:2022-03-04

    CPC classification number: G06F16/27 G06F16/284 G06F16/2282

    Abstract: In an embodiment, multiple computers cooperate to retrieve content from tables in a relational database. Each table contains respective rows. Each row contains a vertex of a graph. Many high-degree vertices are identified. Each high-degree vertex is connected to respective edges in the graph. A count of the edges of each high-degree vertex exceeds a degree threshold. A central computer detects that all vertices in a high-degree subset of tables are high-degree vertices. Based on detecting the high-degree subset of tables, multiple vertices of the graph that are not in the high-degree subset of tables are replicated. Within local storage capacity limits of the computers, this degree-based replication may be supplemented with other vertex replication strategies that are schema based, content based, or workload based. This intelligent selective replication maximizes system throughput by minimizing graph data access latency based on data locality.

    FAST AND MEMORY-EFFICIENT DISTRIBUTED GRAPH MUTATIONS

    公开(公告)号:US20230237047A1

    公开(公告)日:2023-07-27

    申请号:US17585117

    申请日:2022-01-26

    CPC classification number: G06F16/2379 G06F16/9024

    Abstract: Data structures and methods are described for applying mutations on a distributed graph in a fast and memory-efficient manner. Nodes in a distributed graph processing system may store graph information such as vertices, edges, properties, vertex keys, vertex degree counts, and other information in graph arrays, which are divided into shared arrays and delta logs. The shared arrays on a local node remain immutable and are the starting point of a graph, on top of which mutations build new snapshots. Mutations may be supported at both the entity and table levels. Periodic delta log consolidation may occur at multiple levels to prevent excessive delta log buildup. Consolidation at the table level may also trigger rebalancing of vertices across the nodes.

    AUTOMATED DATASET DRIFT DETECTION
    124.
    发明申请

    公开(公告)号:US20230139718A1

    公开(公告)日:2023-05-04

    申请号:US17513760

    申请日:2021-10-28

    Abstract: Herein are acceleration and increased reliability based on classification and scoring techniques for machine learning that compare two similar datasets of different ages to detect data drift without a predefined drift threshold. Various subsets are randomly sampled from the datasets. The subsets are combined in various ways to generate subsets of various age mixtures. In an embodiment, ages are permuted and drift is detected based on whether or not fitness scores indicate that an age binary classifier is confused. In an embodiment, an anomaly detector measures outlier scores of two subsets of different age mixtures. Drift is detected when the outlier scores diverge. In a two-arm bandit embodiment, iterations randomly alternate between both datasets based on respective probabilities that are adjusted by a bandit reward based on outlier scores from an anomaly detector. Drift is detected based on the probability of the younger dataset.

    Vectorized queues for shortest-path graph searches

    公开(公告)号:US11630864B2

    公开(公告)日:2023-04-18

    申请号:US16803832

    申请日:2020-02-27

    Abstract: Techniques are described for a vectorized queue, which implements a vectorized ‘contains’ function that determines whether a value is in the queue. A three-phase vectorized shortest-path graph search splits each expanding and probing iteration into three phases that utilize vectorized instructions: (1) The neighbors of nodes that are in a next queue are fetched and written into a current queue. (2) It is determined whether the destination node is among the fetched neighbor nodes in the current queue. (3) The fetched neighbor nodes that have not yet been visited are put into the next queue. According to an embodiment, a vectorized copy operation performs vector-based data copying using vectorized load and store instructions. Specifically, vectors of data are copied from a source to a destination. Any invalid data copied to the destination is overwritten, either with a vector of additional valid data or with a vector of nonce data.

    Inferring intra package and module dependencies

    公开(公告)号:US11385889B2

    公开(公告)日:2022-07-12

    申请号:US16703499

    申请日:2019-12-04

    Abstract: Herein are machine learning (ML) feature processing and analytic techniques to detect anomalies in parse trees of logic statements, database queries, logic scripts, compilation units of general-purpose programing language, extensible markup language (XML), JAVASCRIPT object notation (JSON), and document object models (DOM). In an embodiment, a computer identifies an operational trace that contains multiple parse trees. Values of explicit features are generated from a single respective parse tree of the multiple parse trees of the operational trace. Values of implicit features are generated from more than one respective parse tree of the multiple parse trees of the operational trace. The explicit and implicit features are stored into a same feature vector. With the feature vector as input, an ML model detects whether or not the operational trace is anomalous, based on the explicit features of each parse tree of the operational trace and the implicit features of multiple parse trees of the operational trace.

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