METHODS AND SYSTEMS FOR AUTOMATING DEPLOYMENT OF APPLICATIONS IN A MULTI-TENANT DATABASE ENVIRONMENT

    公开(公告)号:US20240036913A1

    公开(公告)日:2024-02-01

    申请号:US18486527

    申请日:2023-10-13

    CPC classification number: G06F9/465 G06F8/60 G06F9/54 G06F9/5016

    Abstract: In accordance with embodiments disclosed herein, there are provided mechanisms and methods for automating deployment of applications in a multi-tenant database environment. For example, in one embodiment, mechanisms include managing a plurality of machines operating as a machine farm within a datacenter by executing an agent provisioning script at a control hub, instructing the plurality of machines to download and instantiate a lightweight agent; pushing a plurality of URL (Uniform Resource Locator) references from the control hub to the instantiated lightweight agent on each of the plurality of machines specifying one or more applications to be provisioned and one or more dependencies for each of the applications; and loading, via the lightweight agent at each of the plurality of machines, the one or more applications and the one or more dependencies for each of the one or more applications into memory of each respective machine.

    Smart moderation and/or validation of product and/or service details in database systems

    公开(公告)号:US11887088B2

    公开(公告)日:2024-01-30

    申请号:US16749021

    申请日:2020-01-22

    CPC classification number: G06Q20/1235 H04L51/10 H04L51/212

    Abstract: In accordance with embodiments, there are provided mechanisms and methods for facilitating smart moderation and/or validation of product and/or service details in database systems according to one embodiment. In one embodiment and by way of example, a method includes identifying images as a media description associated with a product, wherein the media description to be communicated using one or more media outlets over one or more communication networks, and wherein the images are captured using one or more cameras; detecting an image having an object potentially not suitable for communication based on a rule; assigning a score to the object; comparing the score to a threshold reflecting a minimum level of suitability for communication of the media description based on the rule; disallowing the object or the image to be communicated when the score is less than the threshold; and facilitating publication, by a website, of the media description without the object or the image.

    Multi substrate public cloud big data jobs as a service

    公开(公告)号:US11886912B2

    公开(公告)日:2024-01-30

    申请号:US17162675

    申请日:2021-01-29

    CPC classification number: G06F9/4881 G06F9/3891 G06F9/505

    Abstract: Data processing approaches are disclosed that include receiving a configuration indicating a plurality of parameters for performing a data processing job, identifying available compute resources from a plurality of public cloud infrastructures, where each public cloud infrastructure of the plurality of public cloud infrastructures supports one or more computing applications, one or more job schedulers, and one or more utilization rates, selecting one or more compute clusters from one or more of the plurality of public cloud infrastructures based on a matching process between the parameters for performing the data processing job and a combination of the one or more computing applications, the one or more job schedulers, and the one or more utilization rates, and initiating the one or more compute clusters for processing the data processing job based on the selecting.

    Machine-learnt field-specific standardization

    公开(公告)号:US11886461B2

    公开(公告)日:2024-01-30

    申请号:US16528175

    申请日:2019-07-31

    CPC classification number: G06F16/258 G06F16/2456 G06N7/01 G06N20/00

    Abstract: A system tokenizes raw values and corresponding standardized values into raw token sequences and corresponding standardized token sequences. A machine-learning model learns standardization from token insertions and token substitutions that modify the raw token sequences to match the corresponding standardized token sequences. The system tokenizes an input value into an input token sequence. The machine-learning model determines a probability of inserting an insertion token after an insertion markable token in the input token sequence. If the probability of inserting the insertion token satisfies a threshold, the system inserts the insertion token after the insertion markable token in the input token sequence. The machine-learning model determines a probability of substituting a substitution token for a substitutable token in the input token sequence. If the probability of substituting the substitution token satisfies another threshold, the system substitutes the substitution token for the substitutable token in the input token sequence.

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