Microsoft Technology Licensing, LLC

Yi Wang

Olga Ivanova

Venkat Narayanan

Smita Ojha

Yang Zhou

Lei He

Songtao Guo

Art Sadovsky

Joonhyung Lim

Alexandre De Baynast

Lizabeth Li

Markus Cozowicz

Carrie Peng

Albert Lingfeng Cui

Ashish Premaraj

Ulrich Pinsdorf

Philipp Kranen

Qiannan Yin

Qunzeng Liu

In an embodiment, the disclosed technologies include receiving a set of digital inputs; where the set of digital inputs includes a candidate entity-member entity pair that includes candidate entity data and member entity data; where the member entity data has been extracted from a node of an online service; where an exact match has not been found between the candidate entity data and the member entity data; in response to the set of digital inputs, outputting models of the candidate entity data and the member entity data, respectively; where the models indicate weight values assigned to text in the candidate entity data and weight values assigned to text in the member entity data, respectively; calculating a similarity score using the models; in response to the similarity score matching a threshold, inputting the candidate entity-member entity pair to a classifier to produce a classification; where the classifier uses a machine learning model that has been trained using features derived from previously-analyzed candidate entity-member entity pairs; where the classification indicates a mathematical likelihood that a match has been found between the candidate entity data and the member entity data.

Hybrid entity matching to drive program execution

Outage detection in a cloud based service is provided using usage data based error signals. Usage data is collected from component of the cloud based service or client devices of the cloud based service based on customer actions on the cloud based service. The usage data is aggregated and normalized to generate an error signal from errors generated from a component of the cloud based service. An outage is detected from the error signal. An alert that includes information associated with the outage and one or more customers impacted by the outage is generated.

Detection of outage in cloud based service using usage data based error signals

Anomalies detection in error signals of a cloud based service is provided. An application such as an analysis application identifies a machine learning algorithm that matches error signals of components of a cloud based service. A periodic pattern from the error signals is removed with the machine learning algorithm to filter the periodic pattern from an error count in the error signals. The error signals are processed with the machine learning algorithm to detect one or more anomalies with the components. The machine learning algorithm is updated while processing new data to detect new patterns.

Detection of anomalies in error signals of cloud based service

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Redmond, WA, United States of America

Yi Wang