DATA SELECTION BASED ON CONSUMPTION AND QUALITY METRICS FOR ATTRIBUTES AND RECORDS OF A DATASET

    公开(公告)号:US20230289839A1

    公开(公告)日:2023-09-14

    申请号:US17693799

    申请日:2022-03-14

    Applicant: ADOBE INC.

    CPC classification number: G06Q30/0204

    Abstract: Embodiments provide systems, methods, and computer storage media for management, assessment, navigation, and/or discovery of data based on data quality, consumption, and/or utility metrics. Data may be assessed using attribute-level and/or record-level metrics that quantify data: “quality”—the condition of data (e.g., presence of incorrect or incomplete values), its “consumption”—the tracked usage of data in downstream applications (e.g., utilization of attributes in dashboard widgets or customer segmentation rules), and/or its “utility”—a quantifiable impact resulting from the consumption of data (e.g., revenue or number of visits resulting from marketing campaigns that use particular datasets, storage costs of data). This data assessment may be performed at different stages of a data intake, preparation, and/or modeling lifecycle. For example, a data selection interface may filter based on consumption and/or quality metrics to facilitate discovery of more effective data for machine learning model training, data visualization, or marketing campaigns.

    PERSONALIZED VISUALIZATION RECOMMENDATION SYSTEM

    公开(公告)号:US20220147540A1

    公开(公告)日:2022-05-12

    申请号:US17091941

    申请日:2020-11-06

    Applicant: ADOBE INC.

    Abstract: Systems and methods for personalized visualization recommendation are described. Embodiments of the described systems and methods are configured to identify a first matrix representing user interactions with a plurality of data attributes corresponding to a plurality of datasets, a second matrix representing user interactions with a plurality of visualizations, and a third matrix representing a plurality of meta-features for each of the data attributes; compute low-dimensional embeddings representing user characteristics, the data attributes, visualization configurations, and the meta-features using joint factorization of the first matrix, the second matrix and the third matrix; generate a model for predicting visualization preference weights based on the low-dimensional embeddings; predict the visualization preference weights for a user corresponding to a plurality of candidate visualizations of dataset using the model; and generate a personalized visualization of the dataset for the user based on the predicted visualization preference weights.

    Enterprise applications driven by common metadata repository

    公开(公告)号:US11822525B2

    公开(公告)日:2023-11-21

    申请号:US17653792

    申请日:2022-03-07

    Applicant: ADOBE INC.

    CPC classification number: G06F16/212 G06F16/248 G06F16/258

    Abstract: Systems and methods for enterprise applications supported by common metadata repository are described. One or more aspects of the systems and methods include storing a plurality of entity schemas in a metadata repository, wherein each of the plurality of entity schemas corresponds to a different entity service from a plurality of entity services that interact with an application; storing a plurality of extension schemas in the metadata repository, wherein each of the plurality of extension schemas corresponds to a different extension service from a plurality of extension services utilized by the application; receiving, at the metadata repository from an extension service of the plurality of extension services, an entity schema request indicating an entity schema corresponding to an entity service of the plurality of entity services; and providing, from the metadata repository to the extension service, the entity schema in response to the entity schema request.

Patent Agency Ranking