Microsoft Technology Licensing, LLC

Yi-Lei Wu

Matthew Joseph Koscumb

Molly Grossman

Scott McMurray

Peter Alexi Gurevich

Eric M Borzello

Robert Novitskey

George Arthur Herbert, Iii

Erik R Ashby

Jacob Richard Pitts

Larry Jack Israel

Eric Thomas Smith

Danny Thayer

Adam Kenneth Mihalcin

Bruce Wiedeman

Lopez Gilsinia Gilroy

Mukunda Murthy

Bala Murali Loganath

Peter A Gurevich

Systems, methods, and software for telemetry event correlation is provided herein. An exemplary method includes obtaining an indication of a data selection defining at least one data entry of interest among datasets that comprise data entries determined by one or more associated telemetry elements, establishing a joined dataset based at least on contextually corresponding characteristics derived from the datasets, and processing the joined dataset with a plurality of adaptive functions to correlate data entries of the joined dataset to the data selection, with each of the plurality of adaptive functions configured to produce an associated correlation dataset comprising data entries potentially related to the data selection. The method also includes evaluating each associated correlation dataset to select an output dataset comprising contextual data entries related to the data selection.

Telemetry data contextualized across datasets

A telemetry module integrated with an application may include a data collection and analysis engine configured to implement a data collection pattern comprised of data collection rules to perform data collection and analysis for reporting to a service associated with the application. The telemetry module may also include a scaling profile manager configured to dynamically scale the data collection and analysis performed by the data collection and analysis engine for a target device such that parameters of the data collection and analysis correspond to resources and capabilities of the target device. After scaling, the data collection and analysis engine may then be further configured to determine additional data collection rules based on the scaled data collection and analysis, and dynamically modify the data collection pattern implemented based on the additional data collection rules such that the data collection pattern also corresponds to resources and capabilities of the target device.

Dynamic data collection pattern for target device

A telemetry module integrated with an application may include a data collection and analysis engine configured to perform data collection and analysis for reporting to a service associated with the application. The telemetry module may include additional components configured to dynamically scale the data collection and analysis performed by the data collection and analysis engine for a target device such that parameters of the data collection and analysis correspond to resources and capabilities of the target device. For example, a scaling profile manager may receive a profile for a device on which the application is being executed from the service, where the profile defines criteria for the scaling of the data collection and analysis. The scaling profile manager may then determine one or more resources and capabilities of the device, compare the determined resources and capabilities to the criteria, and scale the data collection and analysis based on the comparison.

Dynamically scalable data collection and analysis for target device

Techniques and systems are described to improve computing device telemetry collection by automatic analysis and dynamic adjustment of telemetry collection rules. Certain implementations use a telemetry service to analyze telemetry messages and direct the adjusted rules at the client computing device, automatically and gradually refining telemetry collection as more relevant telemetry is received. Dynamic adjustments that can be made to the collection rules include, for example, changes to the collection frequency of telemetry messages, changes to the level of detail transmitted in the telemetry messages, and changes to the targets (e.g., applications, components, devices) from which to collect.

Dynamic telemetry message profiling and adjustment

Technologies are generally described for collecting, analyzing and reporting telemetry data. A telemetry engine is built into a client application installed on a client device, and the telemetry engine is configured to collect and analyze application data at the client device and report the analyzed data to a service provider associated with the application. The telemetry application includes a specialized set of components, such as a telemetry transport component configured to communicate with the service provider, a data collection module configured to retrieve data from the application, and a rule manager and analyzer configured to analyze collected data according to a set of data collection rules provided by the service provider. The telemetry engine enables collection and analysis of telemetry data from multiple distributed client devices. The client devices dynamically change over time to ensure that current and important information is reported to the service provider.

Dynamic collection analysis and reporting of telemetry data

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Redmond, WA, United States of America

Yi-Lei Wu