Microsoft Technology Licensing, LLC

Euan Peter Garden

Sumit Gulwani

Chairy Chiu Ying Cheung

Ranvijay Kumar

Daniel Adam Perelman

John Raymond Justice

Michael James Coulson

Taurean Addair Jones

Kevin David James Grealish

Craig Allen Lee

Jose Ramon Calzada Gomez

Chairy Cheung

Giovanni Moises Della-Libera

J Kirk Haselden

Daniel Galen Simmons

Michael Entin

Mohammad Raza

Robert E Walters

A method for transforming strings includes identifying one or more candidate example input strings from a database including a set of input strings. The candidate example input strings are presented for example transformation. For one or more of the candidate example input strings, an example output string corresponding to that example input string is received, where each example input string and its corresponding example output string define a transformation example in an example set. A string transformation program is generated based on transformation examples in the example set.

Example management for string transformation

Various technologies described herein pertain to interactive data splitting. A program for splitting an input column of an input data set into multiple output columns can be synthesized based on input-only examples. The program can further be generated based on various user input; thus, the user input can guide the synthesis of the program. Moreover, the program can be executed on the input data set to split the input column of the input data set into the multiple output columns.

Interactive splitting of a column into multiple columns

A computer program file or a sequence of processing steps from an authoring environment of a data processing system is used to generate a graph including nodes representing the processing steps is generated. Unique identifiers (tags) are assigned to the nodes. One or more processing steps are optionally modified while preserving the original identifiers. If an error occurs during execution, error information is saved including the processing step that failed and data records that triggered the failure. The identifier of the failed processing step is attached to the error information. The error information is collected and processed. The collected error information is used to correlate the error to the failed processing step and to data records triggering the error using the identifier of the failed processing step recorded with the error information. The correlated error information indicates cause and location of the error.

Systems and methods for correlating errors to processing steps and data records to facilitate understanding of errors

A user interface having components that each provide a view on underlying data, and controls for preparing the data. This allows the user to have a wide variety of views on the data set, and corresponding controls appropriate for that view. The level of control over data preparation is even further enhanced by a selection control and navigation control. The selection control allows a portion of the view to be selected, which would in turn affect the view(s) displayed in one or more other user interface components. The navigation control causes the user interface components to display views on the data set as that data set existed at various prior stages in data preparation. The selection control remains active even when viewing prior stages of the data set.

Navigation of data set preparation

A user interface that includes components that each provide a view on the underlying data set, and also provide controls for editing or preparing the data set. The user interface components include a detail user interface component that displays a detail view on the data set, and summary user interface component(s) that each display a corresponding summary view on the data set. A promotion control promotes a summary view on the data set to a detailed view on the data set, and corresponding changes the controls usable in the promoted detail view. This allows the user to have a wide variety of views on the data set, and corresponding controls appropriate for that view. Thus, the user can take the most efficient and intuitive approach to editing or preparing the data, thereby allowing data preparation to be more efficient, intuitive and likely correct.

Multi-view control on data set

A computing device is provided, comprising a processor configured to select at least one pair of tuples of columns including a source tuple from a first table and a target tuple from a second table. For each pair, the processor may select one or more rows from the source tuple and elements of the target tuple. For each selected row, the processor may programmatically generate a script that, when performed on the source tuple, produces a value consistent with the target tuple. The processor may apply each script to other rows of the source tuple and determine that an output is in the target tuple. For each column of the target tuple, for the script with output that meets a matching criterion, the processor may convey the output and, in response to a signal accepting the script, join the tables at least in part by performing each accepted script.

Join with predictive merging of multiple columns

A computing device is provided comprising a processor configured to select at least one pair of elements, including an element in a source column of the first table and an element in a target column of the second table. The processor may detect that the elements are in different formats. For at least one element, the processor may apply a predetermined mapping to a common format. The processor may modify at least one element to have the same format as the other, and may generate an example including the modified pair. The processor may programmatically generate a script that, when performed on the selected elements, produces a value consistent with the example. For the script with output matching the elements of the target column, the processor may convey the output for display, and may join the tables at least in part by performing the script on the source column.

Join with format modification by example

Aspects of the present invention describe a nonhomogeneous server deployment in which different classes of servers are placed within a data center unit, such as a rack or chassis. In one aspect, the cooling capacity for the unit is intentionally sized to be incapable of providing enough cooling to maintain an acceptable operational temperature for the servers, if all servers in the rack are simultaneously in an active processing mode. The control fabric maintains an acceptable operating temperature within the unit by assigning workloads to only a portion of the servers within the unit at a given point in time. In one aspect, servers are arranged within a data center unit according to anticipated peak periods of usage. Units can be arranged to be adjacent to servers optimized for a specialized workload having a peak period of usage that differs from each other.

Nonhomogeneous server arrangement

A computing device is provided, comprising a processor configured to select at least one pair of columns. Each pair may include a source column of the first table and a target column of the second table. For each pair, the processor may detect that the columns contain data with different granularities. The processor may modify the data to have the same granularity, and may generate an example including an element from the source column and an element from the target column. For each example, the processor may programmatically generate a script that, when performed on the source column, produces a value consistent with the target column. For the script with output that meets a matching criterion, the processor may convey the output for display, and may, in response to a signal accepting the script, join the tables at least in part by performing the script on the source column.

Join with predictive granularity modification by example

In various embodiments, methods and systems for intelligent streaming of game content based on the level of interactivity of the game content are provided. The level of interactivity of game content is determined using techniques that classify the level of interactivity. The level of interactivity is defined for different components of game content. Streaming techniques are associated with game content having specific levels of interactivity. An edge computing infrastructure may facilitate intelligent streaming in that game assets classified as prefetch game assets or stream game assets are associated with prefetch instructions. The prefetch instructions are communicated from a game server to a game platform to instruct the game platform to prefetch a prefetch game asset to the edge computing infrastructure in advance of an anticipated game context in which the prefetch game asset is used. During the anticipated game context, the assets are retrieved for output at the game platform.

Intelligent streaming of media content

Aspects of the present invention relate to a multimode gaming server that can run in game mode and video encode mode. Aspects of the invention can monitor demand for different computing projects in a data center and change the gaming servers into different modes to meet the demand. The arrangement of gaming servers in the different modes can be established to balance heat generation evenly throughout a data center.

Data center management of multimode servers

An automatic task generation mechanism provides generation of tasks such as maintenance tasks for a computer system. A set of tasks is set up along with rules for performing the tasks. The rules may be associated with metadata that allow the tasks to be mapped to the tasks. Events may be detected that are related to database operations such as a create, modify, delete or add command. Upon event detection by the system, the system may store the event and associated metadata. Another process may query the event and metadata storage and compare those items to the rules previously set up. If there is a match between the rules and the event, one or more tasks may be established which correspond to an action that is desired to be taken. The task may then be inserted into a computer system for subsequent execution.

Automatic task generator method and system

Various technologies and techniques are disclosed that automatically optimize package execution performance. A profiling phase executes each task in a control flow package and measures performance metrics, such as task execution length, task memory usage, task correlation to CPU versus input/output operations, network bandwidth, and running applications. An optimization phase optimizes subsequent executions of the package by using the performance metrics to make appropriate adjustments to the package. The profiling phase and optimizing phase are repeated over a period of time as appropriate to improve the package's performance without requiring a user to make adjustments to the tasks in the package.

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Bellevue, WA, United States of America

Euan Peter Garden