Abstract: 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.

Abstract: Described herein are various technologies pertaining to construction and application of a data cleaning template. A data cleaning tool, when applying the data cleaning template to a data set, is configured to identify a column in the data set that has data entries of a data type specified in the data cleaning template. In response to identifying the column in the data set, the data cleaning tool, when applying the data cleaning template to the data set, alters a format of the data entries in the column from a first format to a second format, the second format specified in the data cleaning template.

Abstract: 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.

Abstract: A user interface that concurrently shows both the input tabular data in an input portion and the result of pivot operation(s) derived from the input tabular data in a results portion. Association visualizations show associations between the input tabular data and the result of the pivot operation(s). For instance, a column of the input table may be visually associated with rows or columns of the result of the pivot operation. As another example, aggregated data may be visualized as associated with the corresponding input values from which the aggregated data was formed. Thus, a user may see how a pivot table or other result was constructed from input tabular data. Once the user selects an apply control, the input portion is deemphasized and the results portion is further emphasized, and association visualizations may be removed. Thus, the results portion can act as a preview of the pivot operation.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A filter builder is data characteristic guided. A data characteristic can be determined that describes data or the distribution thereof. A visualization of the data characteristic can be generated and displayed. A selection signal from an input device can be received selecting one or more elements of visualizations. Based on one or more selected elements, a filter condition can be generated automatically and presented in the same context with the visualizations.

Abstract: Various technologies pertaining to extracting data encoded in a tree-structured document and generating a table based upon the extracted data are described herein. In a first embodiment, the table is generated without requiring input from a data cleaner. In a second embodiment, the table is generated based upon examples set forth by a data cleaner.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Described herein are various technologies pertaining to construction and application of a data cleaning template. A data cleaning tool, when applying the data cleaning template to a data set, is configured to identify a column in the data set that has data entries of a data type specified in the data cleaning template. In response to identifying the column in the data set, the data cleaning tool, when applying the data cleaning template to the data set, alters a format of the data entries in the column from a first format to a second format, the second format specified in the data cleaning template.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Job execution can be scheduled and monitored. Execution of a job results in generation of one or more data slices, which can be consumed as input by one or more related jobs. Data slices can be presented in an interactive view. Selection of a data slice in the view can trigger automatic identification of related data slices based on dependencies between data slices. Each data slice can also include and be presented with a status indicating the availability of corresponding data, such as pending, ready, or failed. Furthermore, selection of a data slice that failed to generate successfully can trigger identification of related data slices that failed or are predicted to fail to generate successfully.

Abstract: A view of data transformation jobs can be presented by way of a user interface. Related jobs can subsequently be identified automatically after a job is selected based on data dependencies between jobs. Execution status can also be determined and presented such that successful and failed execution of jobs, for example, can be differentiated. Furthermore, selection of a job run that failed to execute successfully can trigger identification of related jobs runs that failed or are predicted to fail to execute successfully.