Facilitating efficient identification of relevant data

    公开(公告)号:US11907232B2

    公开(公告)日:2024-02-20

    申请号:US17146651

    申请日:2021-01-12

    Applicant: ADOBE INC.

    CPC classification number: G06F16/24578 G06F17/18 H04L67/306 H04L67/535

    Abstract: The present technology provides for facilitating efficient identification of relevant metrics. In one embodiment, a set of candidate metrics for which to determine relevance to a user is identified. For each candidate metric, a set of distribution parameters is determined, including a first distribution parameter based on implicit positive feedback associated with the metric and usage data associated with the metric and a second distribution parameter based on the usage data associated with the metric. Such usage data can efficiently facilitate identifying relevance even with an absence of negative feedback. Using the set of distribution parameters, a corresponding distribution is generated. Each distribution can then be sampled to identify a relevance score for each candidate metric indicating an extent of relevance of the corresponding metric. Based on the relevance scores for each candidate metric, a candidate metric is designated as relevant to the user.

    Software component defect prediction using classification models that generate hierarchical component classifications

    公开(公告)号:US11467817B2

    公开(公告)日:2022-10-11

    申请号:US16259454

    申请日:2019-01-28

    Applicant: Adobe Inc.

    Abstract: Systems and methods for facilitating updates to software programs via machine-learning techniques are disclosed. In an example, an application generates a feature vector from a textual description of a software defect by applying a topic model to the textual description. The application uses the feature vector and one or more machine-learning models configured to predict classifications and sub-classifications of the textual description. The application integrates the classifications and the sub-classifications into a final classification of the textual description that indicates a software component responsible for causing the software defect. The final classification is usable for correcting the software defect.

    COLD START AND ADAPTIVE SERVER MONITOR

    公开(公告)号:US20220237066A1

    公开(公告)日:2022-07-28

    申请号:US17158643

    申请日:2021-01-26

    Applicant: Adobe Inc.

    Abstract: A server monitoring methodology uses a time-series model for predicting value of a metric of a server. The model is built using initial training data that includes median values of the metric, each median value based on previously measured values of that metric, from servers of a group to which the server is being added. The methodology includes observing the value of the metric of the server, and comparing that observed value to a predicted value of the model. In response to the observed value being within an expected tolerance, the training data is updated to include the observed value; and in response to the observed value being outside the expected tolerance, the training data is updated to include a value between the observed value of the server metric and the predicted value. The model is updated using the updated training data, and eventually adapts to performance of the server.

    Facilitating efficient identification of relevant data

    公开(公告)号:US12287797B2

    公开(公告)日:2025-04-29

    申请号:US18406426

    申请日:2024-01-08

    Applicant: Adobe Inc.

    Abstract: The present technology provides for facilitating efficient identification of relevant metrics. In one embodiment, a set of candidate metrics for which to determine relevance to a user is identified. For each candidate metric, a set of distribution parameters is determined, including a first distribution parameter based on implicit positive feedback associated with the metric and usage data associated with the metric and a second distribution parameter based on the usage data associated with the metric. Such usage data can efficiently facilitate identifying relevance even with an absence of negative feedback. Using the set of distribution parameters, a corresponding distribution is generated. Each distribution can then be sampled to identify a relevance score for each candidate metric indicating an extent of relevance of the corresponding metric. Based on the relevance scores for each candidate metric, a candidate metric is designated as relevant to the user.

    Determining data loss for internet traffic data

    公开(公告)号:US12177088B2

    公开(公告)日:2024-12-24

    申请号:US17938742

    申请日:2022-10-07

    Applicant: Adobe Inc.

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that determine internet traffic data loss from internet traffic data including bulk ingested data utilizing an internet traffic forecasting model. In particular, the disclosed systems detect that observed internet traffic data includes bulk ingested internet traffic data. In addition, the disclosed systems determine a predicted traffic volume for an outage period from the bulk ingested internet traffic data utilizing an internet traffic forecasting model. The disclosed systems further generate a decomposed predicted traffic volume for the outage period. The disclosed systems also determine an internet traffic data loss for the outage period from the decomposed predicted traffic volume while calibrating for pattern changes and late data from previous periods.

    Selecting attributes by progressive sampling to generate digital predictive models

    公开(公告)号:US11948094B2

    公开(公告)日:2024-04-02

    申请号:US17136727

    申请日:2020-12-29

    Applicant: Adobe Inc.

    CPC classification number: G06N5/022 G06N20/00 G06N5/01

    Abstract: The present disclosure includes methods and systems for generating digital predictive models by progressively sampling a repository of data samples. In particular, one or more embodiments of the disclosed systems and methods identify initial attributes for predicting a target attribute and utilize the initial attributes to identify a coarse sample set. Moreover, the disclosed systems and methods can utilize the coarse sample set to identify focused attributes pertinent to predicting the target attribute. Utilizing the focused attributes, the disclosed systems and methods can identify refined data samples and utilize the refined data samples to identify final attributes and generate a digital predictive model.

    Adaptive sampling scheme for imbalanced large scale data

    公开(公告)号:US10346861B2

    公开(公告)日:2019-07-09

    申请号:US14933254

    申请日:2015-11-05

    Applicant: ADOBE INC.

    Abstract: Embodiments of the present invention relate to providing business customers with predictive capabilities, such as identifying valuable customers or estimating the likelihood that a product will be purchased. An adaptive sampling scheme is utilized, which helps generate sample data points from large scale data that is imbalanced (for example, digital website traffic with hundreds of millions of visitors but only a small portion of them are of interest). In embodiments, a stream of sample data points is received. Positive samples are added to a positive list until the desired number of positives is reached and negative samples are added to a negative list until the desired number of negative samples is reached. The positive list and the negative list can then be combined, shuffled, and fed into a prediction model.

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