Microsoft Technology Licensing, LLC

Microsoft Corporation

Tetsuo Seto

Russell Steven Krajec

Alexander G Gounares

Takanobu Murayama

Adrian James Garside

Jeffrey West Pettiross

Shawna Julie Davis

A computer monitoring system may predict near term and long term performance by comparing a segment of current time series data with previously observed time series to find matching segments. From a matching segment, a prediction of performance may be made by examining later observations in the time series. Each time series element may include a large number of parameters, and one mechanism for comparing segments may be treating the elements as multi-dimensional vectors and using cosine similarity for finding significant matches. A deployment mechanism may store time series segments in a searchable database, and search the database with a newly observed time series segment for matches.

Computer performance prediction using search technologies

Recommendations may be generated while calculating performance metrics from multiple uses of a software component. A tracing service may collect trace data from multiple uses of a software component, where each use may be done on different conditions. The performance metric analysis may identify various factors that may affect the performance of a software component, then present those factors to a user in different delivery mechanisms. In one such mechanism, a recommended set of hardware and software configurations may be generated as part of an operational analysis of a software component.

Software component recommendation based on multiple trace runs

Dimensionality reduction, such as principal component analysis, may be used at least in part to generate a model of time series observations of a computer application. The model may be applied to current and predicted observations. Outliers may be identified from current or predicted observations by analyzing those observations against the model, and statistically relevant outliers may generate alerts or corrective or other action to be taken. The outliers may be analyzed by searching for similar outliers that may have been previously observed, and predicting any future events based on similar observations of the past.

Behavior clustering analysis and alerting system for computer applications

Dimensionality reduction, such as principal component analysis, may be performed against a time series of performance observations for a computer application. A visual representation of the results may be displayed in one, two, or three dimensions, and often show clusters of operational behavior. The representation may be animated to show a sequence of observations and how the behavior of an application may change from one cluster of operation to another. The representation may be further applied to show both a historical view of the observations and new observations. The time series may contain performance and operational data, as well as metadata observed from a computer application.

Visualization of behavior clustering of computer applications

Visualizing execution of an application in a trace coverage graph includes receiving module trace data gathered during execution of an executable module. The module trace data includes data representing sequences of execution between individual executable components. A trace coverage graph is generated and displayed from the module trace data. The trace coverage graph includes graph nodes that each correspond to a different executable component. The trace coverage graph also includes graph edges that each visually connects two of the graph nodes. Each graph edge visually indicates an amount of trace data present for the sequence of execution between the graph nodes that are connected by the graph edge. Thicker graph edges represent the presence of a larger amount of trace data, and thinner graph edges represent the presence of a lesser amount of trace data.

Module specific tracing in a shared module environment

A database of module performance may be generated by adding tracing components to applications, as well as by adding tracing components to modules themselves. Modules may be reusable code that may be made available for reuse across multiple applications. When tracing is performed on an application level, the data collected from each module may be summarized in module-specific databases. The module-specific databases may be public databases that may assist application developers in selecting modules for various tasks. The module-specific databases may include usage and performance data, as well as stability and robustness metrics, error logs, and analyzes of similar modules. The database may be accessed through links in module description pages and repositories, as well as through a website or other repository.

Module database with tracing options

A tracing system may trace applications and their modules, and may make module-specific data available through various interfaces. The tracing system may collect tracer data while an application executes, and may preprocess the data into application-specific and module-specific databases. An analysis engine may further analyze and process these databases to create application-specific views and module-specific views into the data. The application-specific views may be intended for a developer of the application, while the module-specific views may have a public version accessible to everybody and a module developer version that may contain additional details that may be useful to the module developer.

Tracing system for application and module tracing

A module-specific tracing mechanism may trace the usage of a module on behalf of the module developer. The module may be used by multiple application developers, and the tracing system may collect and summarize data for the module in each of the different applications. The data may include usage data as well as performance data. Usage data may include anonymized data for each time the module may be invoked and called, and performance data may include the processing time, memory consumption, and other metrics. The module-specific tracing may be enabled or disabled by an application developer.

A method and apparatus for receiving strokes of electronic ink in more than one language is provided. An input device is used to enter at least one stroke of electronic ink into a processing device. The processing device stores a language indication associated with the electronic ink, such that the language indication provides a way to associate a handwriting recognizer with the ink. The user may change the language or recognizer associated with the ink by flipping over a pen and using another tip of the pen or may use another pen or other input device. Aspects of the described system and method accommodate various optional interfaces to associate a pen tip or click of a button to one or more languages. Other aspects of the system and method include language switching of a displayed software keyboard when a pen input device is within a predetermined distance from a display.

Boxed and lined input panel

An in-place data input panel provides a user with access to a list of alternate characters to which displayed characters may be converted. The data input panel provides an indicator, such as an indicator bar, corresponding to segmentation of the characters. A user can change the segmentation of the characters by expanding or contracting the segmentation bar. Also, the user can access a drop-down menu listing alternate character choices corresponding to the characters in the segment indicated by the segmentation bar.

Data input panel character conversion

In a computer system that accepts handwritten input, a digitizer tracks the location of a user's pen while the pen is not in contact with the digitizer's writing surface. The digitizer maps the pen's three-dimensional location into a two-dimensional location on the digitizer's writing surface. Handwriting-recognition is triggered for handwriting entered into a handwriting-recognition task area upon detecting that the user has lifted the pen off of the digitizer's writing surface and that the pen has moved out of proximity from the handwriting-recognition task area. The handwriting-recognition triggering may be inhibited when the pen is down and in contact with the digitizer's writing surface in an inking orientation.

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Redmond, WA, United States of America

Tetsuo Seto