Microsoft Technology Licensing, LLC

Chairy Chiu Ying Cheung

Sumit Gulwani

Euan Peter Garden

Ranvijay Kumar

Taurean Addair Jones

Daniel Adam Perelman

Jose Ramon Calzada Gomez

Raghunath Mallya

Amir Netz

Giovanni Moises Della-Libera

Christian Liensberger

Daniel Galen Simmons

Yoav Yassour

Mark Wade Heninger

Christina Storm

Mohammad Raza

Yifat Sagiv

Yury Berezansky

Ran Didi

James Eldon Tillinghast

Moshe Golan

Oded Bar Levy

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 user interface that allows the user to lock in a comparison state of an inspector control that provides a view on state of a data set being subjected to transformations. The inspector control visualizes a state of at least a portion of the data set (such as a column, table or database) as that data set is navigated by a navigation control. At least under some circumstances, the inspector control also visualizes a comparison state of the data set. A comparison lock control allows the comparison state of the data set to be locked at a particular comparison state regardless of the navigation control navigating the data set. Accordingly, until the comparison state is unlocked via a comparison unlock control, the inspector control visualizes a current state of the data set along with a visualization of the locked comparison state of the data set.

Data set state visualization comparison lock

Examples provide for intelligent navigation of hierarchical groups of items. A current level of items being viewed by a user is automatically detected in response to a context change. The context change includes, for example, a zoom-in, zoom-out, or scroll associated with an item in the hierarchical group of items. A transient user interface control including a contextual breadcrumb list representing a path from a root node to the current level is automatically generated for presentation to the user. An action including, for example, a search, filter, or sort of items within the detected current group of items is performed based on input received from the user via the presented transient user interface control. A result of the performed action is presented for display.

Intelligent navigation via a transient user interface control

A system includes an operating system executed by a processor and memory. A data type identifier changing application is executed by the operating system and is configured to display a table including data entries that are arranged in rows and columns and that include data type identifiers. The data type identifier changing application is configured to generate a list including two or more of the data type identifiers corresponding to rows in a selected column. The data type identifier changing application is configured to change one of the data type identifiers in the list corresponding to a plurality of rows in the selected column to one of another one of the data type identifiers in the list or a data type identifier that is not in the list.

Systems and methods for data type identification and adjustment

Organizations may comprise a variety of resources that are assigned to various organizational units, and allocated for various projects or tasks. A user who wishes to understand the organizational structure of the resources may examine information sources, such as resource manifests and resource access logs, and interpret such information with the user's knowledge of the organization, but may fail to achieve a comprehensive, accurate, and up-to-date understanding of the organization. Instead, interactions among users and resources may be monitored to infer organizational structure; assignment of resources to organizational units; relationships among resources; and usages arising within such resource relationships. Based on such inferences, an interactive visual map may be automatically generated that depicts the arrangement of the organization and the interrelationships of the resources. A user may interact with the visualized resources, such as changing the time range or level of detail, or selecting resources to view usage information.

Inference-based visual map of organizational structure and resource usage

Examples provide for intelligent navigation of informational items within an augmented reality environment. A hierarchical set of informational items within the augmented reality environment is automatically generated. A current level in the hierarchy includes items currently within a field of view (FOV), a current topic of conversation, items associated with user movements, or items selected by the user. A transient user interface control directed to the current group of informational items and a contextual breadcrumb list is generated and displayed within the FOV. An action is performed on a selected group of informational items based on input received from the user via the transient user interface control. The results of the performed action are presented to the user within the FOV.

Navigation in augmented reality via a transient user interface control

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

Transformation pipelines are applied to disparate data sets and domains. An existing transformation pipeline is configured as a stored actionable task structure with a predefined plurality of sequenced tasks for transforming a first data set having a first set of attributes into a modified data set. The existing transformation pipeline is then applied to a different data set having a different set of attributes. Compatibility and operability for implementing the tasks of the actionable data set on the second data set are then determined, based at least in part on the stored attributes of the tasks and the second data set. Interface elements are also utilized to reflect the compatibility and operability of the tasks in the visual representation of the actionable task structure. Some elements are also utilized to provide suggestions for improving the compatibility of the actionable task structure.

Application of actionable task structures to disparate data sets for transforming data in the disparate data sets

Methods, systems and computer program products that are provided for resequencing actionable task structures include features for detecting and reflecting compatibility issues of discrete tasks in the actionable task structures. After receiving input for resequencing an actionable task structure, attributes associated with the resequenced tasks are accessed and analyzed. Compatibility is determined and reflected in the display of the task identifiers, with one or more visual identifiers. The visual indicators can also be used to reflect suggested changes to the actionable task structure, based on a comparison of the task attributes.

Resequencing actionable task structures for transforming data

Providing predictive data predicting data values for a historical dataset. The method facilitates improving the accuracy of the predictive data by identifying for a user, and allowing the user to select ancillary datasets that can be evaluated, using a predictive evaluation, together with a historical dataset to improve the accuracy of the predictive data. A user interface is provided to a user. The user interface identifies one or more ancillary datasets. The ancillary datasets are identified to the user based on the ancillary datasets meeting a threshold condition to a historical dataset. The ancillary datasets are selectable by the user in the user interface. User input is received at the user interface selecting one or more of the ancillary datasets. A predictive dataset is displayed to the user. The predictive dataset is determined by predictive evaluation of the historical dataset and the one or more selected ancillary datasets.

Real time interactive prediction

Actionable task structures comprised of a plurality of tasks are generated by systems and methods utilizing interfaces that suggest tasks for assembly into the actionable task structures based on contextual relevance to data set attributes, other tasks in the actionable task structures and user annotations. The Actionable task structures are stored and selectively applied to one or more different domains for transformation data in the corresponding data sets.

Interfaces and methods for generating and applying actionable task structures

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.

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Redmond, WA, United States of America

Chairy Chiu Ying Cheung