At&t Intellectual Property I, L.p.

Rajat Malik

Divesh Srivastava

Yaron Kanza

Subhabrata Majumdar

Balachander Krishnamurthy

Tamraparni Dasu

Eleftherios Koutsofios

Emily Dodwell

Ritwik Mitra

Gordon Woodhull

Caroline Stone

Aspects of the subject disclosure may include, for example, detecting data streams by a processing system including a processor, wherein the processing system is associated with an anomaly detection and alerting system in which stream processing and model maintenance is decoupled from one another, and wherein one or more dedicated virtual machines (VMs) store and maintain anomaly detection and alerting models, based on the detecting, causing, by the processing system, a plurality of stream-processing VMs to be instantiated for processing the data streams, and managing, by the processing system, data stream assignments for the plurality of stream-processing VMs based on monitoring of one or more conditions, wherein the plurality of stream-processing VMs process assigned data streams by executing instances of the anomaly detection and alerting models, and provide model outputs to the one or more dedicated VMs for updating of the anomaly detection and alerting models. Other embodiments are disclosed.

System and method for cloud-based anomaly detection and alerting for streaming data

Aspects of the subject disclosure may include, for example, a device including a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, define operations of modules that monitor a computational hierarchy, the modules include a first module for generating base alerts with a hierarchical key associated with an operation of the computational hierarchy and for rolling up the base alerts based on the computational hierarchy, resulting in rolled-up base alerts; a second module for generating super alerts from a concentration of the rolled up base alerts; and a third module comprising a first plurality of models that selects the super alerts based on priority, persistence of anomalies, pervasiveness of the super alerts generated, recency, or a combination thereof, generates a smart alert based on voting on the super alerts selected by the first plurality of models, and presents the smart alert on a user interface. Other embodiments are disclosed.

Architecture for scalable smart alerting across a multitude of data streams

Aspects of the subject disclosure may include, for example, a device having a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of: receiving a series of values from a data stream generated by one or more equipment in a communications network; creating a first window of values received before a first point in time and a second window of values received on or after the first point in time; comparing a distribution of values in the first window and values in the second window to compute a distance at the first point in time; repeating the creating and comparing at subsequent points in time after the first point in time, thereby generating a series of distances; computing a z-score for a first distance in the series of distances; generating an alert responsive to the z-score exceeding a threshold; and storing the alert in a log of alerts. Other embodiments are disclosed.

System and method for detecting anomalous changes in data streams and generating textual explanations

Aspects of the subject disclosure may include, for example, training a decision scoring model for an e-sport based on historical data for the e-sport. In various embodiments, the decision scoring model may be trained based on the historical data and on metadata associated with the e-sport. Some embodiments can include identifying a plurality of candidate in-game decision sequences based on decision parameters for a gameplay decision of an ongoing gaming session and a gaming session history for the ongoing gaming session. Various embodiments can include applying the decision scoring model to rank the plurality of candidate in-game decision sequences. Other embodiments are disclosed.

Sequential decision analysis techniques for e-sports

Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning (ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias. Machine learning models may be used for project clustering and bias score determination and may be readily updated as new ML projects are evaluated. Other embodiments are disclosed.

Bias scoring of machine learning project data

Aspects of the subject disclosure may include, for example, a device that includes a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations such as collecting historical data on viewership of media content; gathering metadata of events in the media content; training a scoring model on the metadata and historical data, wherein the scoring model ranks placement choices and provide a score for each placement choice for advertisements within the media content; and evaluating a placement choice for ad completion rate, thereby generating an evaluation of the placement choice. Other embodiments are disclosed.

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Metuchen, NJ, United States of America

Rajat Malik