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    Building and using a sparse time series database (TSDB)
    1.
    发明授权
    Building and using a sparse time series database (TSDB) 有权

    公开(公告)号：US12105722B2

    公开(公告)日：2024-10-01

    申请号：US17817806

    申请日：2022-08-05

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Peng Hui Jiang ,  Jun Su ,  Sheng Yan Sun ,  Hong Mei Zhang ,  Meng Wan

    IPC: G06F16/2458 ,  G06F16/22 ,  G06F16/2455

    CPC classification number: G06F16/2477 ,  G06F16/2228 ,  G06F16/24556

    Abstract: Provided are techniques for building and using a sparse Time Series Database (TSDB). Time series records are received from a native TSDB, where each of the time series records includes a timestamp and one or more tags. Timeslots are determined for shards for the sparse TSDB based on the timestamp included in each of the time series records. The sparse TSDB is built by creating the shards for the determined timeslots and storing the time series records in the shards, while filling in empty ranges in the shards. A query that specifies at least one of the one or more tags is received. It is determined whether to execute the query against the sparse TSDB, and, in response to a determination to execute the query against the sparse TSDB, the query is executed against the sparse TSDB to generate results that are returned.

    HETEROGENEOUS SCHEMA DISCOVERY FOR UNSTRUCTURED DATA
    3.
    发明公开
    HETEROGENEOUS SCHEMA DISCOVERY FOR UNSTRUCTURED DATA 审中-公开

    公开(公告)号：US20230409593A1

    公开(公告)日：2023-12-21

    申请号：US17807884

    申请日：2022-06-21

    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

    Inventor： Peng Hui Jiang ,  Jun Su ,  Sheng Yan Sun ,  Hong Mei Zhang ,  Meng Wan

    IPC: G06F16/25 ,  G06F16/21 ,  G06F16/242 ,  G06F16/28

    CPC classification number: G06F16/258 ,  G06F16/211 ,  G06F16/254 ,  G06F16/2433 ,  G06F16/284

    Abstract: An embodiment for analyzing and tracking data flow to determine proper schemas for unstructured data. The embodiment may automatically use a sidecar to collect schema discovery rules during conversion of raw data to unstructured data. The embodiment may automatically generate multiple schemas for different tenants using the collected schema discovery rules. The embodiment may automatically use ETL to export unstructured data to SQL databases with the generated multiple schemas for the different tenants. The embodiment may automatically monitor usage data of the SQL databases and collect the usage data. The embodiment may automatically optimize schema discovery using the collected usage data. The embodiment may automatically discover schemas with hot usage and apply the discovered schemas with hot usage to other tenants for consumption and further monitoring.

    SMART TEST DATA WORKLOAD GENERATION
    6.
    发明申请
    SMART TEST DATA WORKLOAD GENERATION 有权

    公开(公告)号：US20230089759A1

    公开(公告)日：2023-03-23

    申请号：US17481399

    申请日：2021-09-22

    Applicant: International Business Machines Corporation

    Inventor： Xue Rui Hu ,  Wang Liu ,  Meng Wan ,  Mei Qin Si ,  Li Na Guo ,  Hong Yan Zhang

    IPC: G06F11/36 ,  G06F11/34 ,  G06F11/32 ,  G06K9/62 ,  G06N20/00

    Abstract: In an approach for smart test data workload generation, a processor receives a plurality of expected image frames for a user interface application to be tested. The plurality of expected image frames is pre-defined and represents a series of workflows and operations of the user interface application to be expected based on a design requirement. A processor calculates a first set of hash-values for each corresponding expected image frame. A processor samples the user interface application with a frequency to a plurality of testing image frames during a test run on the user interface application. A processor calculates a second set of hash-values for each sampled testing image frame. A processor compares the first set of hash-values to the second set of hash-values. A processor verifies that the second set of hash-values matches the first set of hash-values.

    PREDICTIVE MICROSERVICES ACTIVATION USING MACHINE LEARNING
    7.
    发明申请
    PREDICTIVE MICROSERVICES ACTIVATION USING MACHINE LEARNING 有权

    公开(公告)号：US20220108147A1

    公开(公告)日：2022-04-07

    申请号：US17060165

    申请日：2020-10-01

    Applicant: International Business Machines Corporation

    Inventor： Meng Wan ,  Li Na Guo ,  Wang Liu ,  Xue Rui Hu ,  Mei Qin Si ,  Hong Yan Zhang

    IPC: G06N3/04 ,  G06K9/62 ,  G06N3/08 ,  H04L29/08 ,  H04L29/06

    Abstract: Described are techniques for predictive microservice activation. The techniques include training a machine learning model using a plurality of sequences of coordinates, where the plurality of sequences of coordinates are respectively based upon a corresponding plurality of series of vectors generated from historical usage data for an application and its associated microservices. The techniques further include inputting a new sequence of coordinates representing a series of application operations to the machine learning model. The techniques further include identifying a predicted microservice for future utilization based on an output vector generated by the machine learning model. The techniques further include activating the predicted microservice prior to the predicted microservice being called by the application.

    DYNAMIC COMBINATION OF PROCESSES FOR SUB-QUERIES
    9.
    发明申请
    DYNAMIC COMBINATION OF PROCESSES FOR SUB-QUERIES 审中-公开

    公开(公告)号：US20190258639A1

    公开(公告)日：2019-08-22

    申请号：US16402259

    申请日：2019-05-03

    Applicant: International Business Machines Corporation

    Inventor： Shuo Li ,  Meng Wan ,  Xiaobo Wang ,  Xin Ying Yang

    IPC: G06F16/2453 ,  G06F16/2455 ,  G06F16/2452

    Abstract: A tool for combining common processes shared by at least two or more sub-queries within a query is provided. The tool determines whether one or more sub set relationships are shared between the at least two or more sub-queries. Responsive to a determination that one or more sub set relationships are shared between the at least two or more sub-queries, the tool determines an order class for the at least two or more sub-queries based on the one or more sub set relationships, wherein determining the order class includes transforming the query to include one or more differing aspects within the single shared common process, with the one or more differing aspects arranged based, at least in part, on a query style, a query type, and a query function. Responsive to determining an access path for the query, the tool executes the access path during run-time for data accessing.

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