Abstract: Interactive dialog communication via callbacks is provided by a dialog execution environment executed in response to a selection of a first user interface element in a panel execution environment. The dialog execution environment receives a first set of information including a callback method. A dialog includes a second user interface element selectable to show a second set of information. To obtain this information, the dialog execution environment sends a request for the second set of information to the panel execution environment via the callback method. The panel execution environment sends a request for the second set of information to a server and receives a response including the second set of information. The dialog execution environment receives a response including the second set of information from the panel execution environment and provides the second set of information in a user interface of the dialog.

Abstract: Some embodiments provide a program. The program receives a visualization collection definition specifying a plurality of visualization definitions for a plurality of visualization definitions. Each visualization definition in the plurality of visualization definitions specifies a multi-dimensional array of data definition. The program further identifies a set of multi-dimensional array of data definitions specified in the plurality of visualization definitions of the visualization collection definition. The program also sends a request for the set of multi-dimensional array of data definitions to a computing system. The program further receives the set of multi-dimensional array of data definitions from the computing system. The program also stores the set of multi-dimensional array of data definitions in a cache storage of the mobile device for later use.

Abstract: Embodiments may be associated with a user interface design for an application. An intelligent user interface platform may collect user experience data associated with a user's interactions with the application over time (e.g., user actions, touchscreen interactions, computer mouse clicks, attention information, context information, etc.). The intelligent user interface platform may then analyze the user experience data (e.g., looking for most visited interface locations, most used actions, infrequently accessed functions, common user mistakes, etc.). The intelligent user interface platform may also automatically create a user interface design adjustment based on the analysis. For example, the user interface design adjustment might be associated with a menu item, a sub-menu item, an application action, an icon location, adding a display element, removing a display element, etc.

Abstract: Computer-readable media, methods, and systems are disclosed for displaying paginated growing widgets. At generation time, a pagination service negotiates widget bounds. The pagination service transmits proposed bounds and a starting index to the growing widgets and receives a number of items that can be displayed as well as updated bounds for all items that can be displayed. The pagination service creates a cache entry including a widget identifier, updated widget bounds, and a starting and ending widget data item index. If a particular page is not present in the widget pagination cache, the pagination service performs another bounds negotiation. At display time, the pagination service instructs each of the paginated growing widgets to render updated widget data items based on a corresponding widget pagination cache entry, without reprocessing a document object model associated with a front-end application on which the paginated growing widgets are displayed.

Abstract: The present disclosure pertains to linked filter groups for linking a driver element of a user interface to a receiver element of the user interface such that data filters applied to driver element are propagated to the receiver element. A first linked group may include a first set of filters for a first visualization of the driver element and a first set of element identifiers of other elements in the user interface, including a first identifier of a first receiver element. When updating visualizations of the receiver element, the first set of filters of the first linked group may be aggregated with a second set of filters of the first receiver element based on the receiver element being a member of the first linked group. As such, the updates to the receiver element are based on both sets of filters.

Abstract: The present disclosure pertains to processing a data model having hierarchical data. A front-end computer sends a request to a back-end computer for dimension candidates for the data model, which is visualized by the front-end computer. The front-end computer is configured score and rank such dimension candidates in order to determine statistics from the data model. The back-end computer determines dimension candidates based on their cardinality and hierarchical information and sends the dimension candidates to the front-end computer. The front-end computer performs the scoring and ranking of dimension candidates and determines statistics for a set of the dimension candidates. The statistics may be presented to a user along with charts and graphs representing the data model.

Abstract: A system includes reception of a user request for a set of data visualizations, execution, in response to the user request, of a main thread to request metadata associated with the set of data visualizations from a remote system, execution of a worker thread to request a set of persisted queries associated with the set of data visualizations from the remote system, to issue the set of persisted queries to the remote system, to receive query results associated with the set of persisted queries from the remote system, and to initiate storage of the query results in a local memory, and execution of the main thread to retrieve the query results stored in the local memory and render the set of data visualizations based on the retrieved query results.

Abstract: Techniques and solutions are described for analyzing results of a machine learning model. A result is obtained for a data set that includes a first plurality of features. A plurality of feature groups are defined. At least one feature group contains a second plurality of features of the first plurality of features. The second plurality of features is less than all of the first plurality of features. Feature groups can be defined based on determining dependencies between features of the first plurality of features, including using contextual contribution values. Group contextual contribution values can be determined for feature groups by aggregating contextual contribution values of the constituent features of the feature groups.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program executable by a device. The program identifies a set of visualizations associated with the user. The program further determines, for each visualization in the set of visualizations, a score associated with changes in the visualization. The program also determines a subset of the set of visualizations based on the set of scores. The program further provides to the user notifications associated with the subset of the set of visualizations.

Abstract: Provided is a system and method for building context from software applications and applying the context to visual settings in a graphical user interface. In one example, the method may include receiving an identification of actions performed by a user with respect to a user interface of a software application, receiving application metadata of the actions from the software application, the application metadata providing context associated with the actions, training one or more predictive models to predict user interface preferences for the user based on the actions and the application metadata, and storing the one or more trained predictive models via a storage device.

Abstract: Techniques for implementing nested group hierarchies for analytics applications are disclosed. In some embodiments, a computer-implemented method comprises: creating a hierarchy object in a semantic layer based on a request comprising a definition for a nested group hierarchy, the definition specifying a hierarchical relationship structure for non-leaf group nodes and at least one leaf node, the non-leaf group nodes and the leaf node(s) corresponding to data stored in a data source in a non-hierarchical structure, the hierarchy object specifying the hierarchical relationship structure based on the definition; generating a query result based on a request comprising an indication of the hierarchy object using the hierarchy object from the semantic layer to retrieve the data from the data source; and causing the query result to be displayed on a computing device using the hierarchy object to display the retrieved data in a hierarchical format indicating the hierarchical relationship structure.

Abstract: Using approximated bin intervals to label the histograms provides clarity and allows for the histogram to be more intuitively understood. A dataset may comprise a plurality of records having a plurality of features including one or more continuous features. A selection of a continuous feature may be obtained. A bin width based on a number of bins and feature statistics of the continuous feature may be determined. An approximated bin interval range is determined by applying a bin mask based on the bin width to the feature statistics. An approximated bin width is determined based on the number of bins and the approximated bin interval range. Approximated bin intervals for the histogram are determined based on the approximated bin width. A histogram is generated having bins with intervals based the approximated bin intervals.

Abstract: Embodiments of the present disclosure include systems and methods for generating names for cloud storage containers. A unique identifier associated with a user of the client device is received from the client device. Next, a hash value is generated based on the unique identifier associated with the user of the client device. A character encoding scheme is then used to encode the hash value into a first encoded value. Instances of a first character in the encoded value is replaced with a defined second character to form a second encoded value. Finally, the second encoded value is sent to a cloud storage service for the cloud storage service to create a storage container using the second encoded value as a name of the storage container.

Abstract: A method, a system, and a computer program product for templatizing error messages in computing systems. An error log generated as a result of an execution of at least one task of a computing system is monitored. The error log includes a plurality of error messages. Each error message includes a first portion and a second portion. Each error message is extracted from the generated error log. One or more error message processing rules for converting each error message into a corresponding template format error message is determined. The error message processing rules are associated with at least one task. The determined error message processing rules are executed to convert each extracted error message into the corresponding template format error message. The converted error message includes the first portion, where the second portion is removed from the converted error message. A converted error log is generated.

Abstract: The present disclosure involves systems, software, and computer implemented methods for automatically detecting hot areas in heat map visualizations. One example method includes identifying a two-dimensional heat map. The identified two-dimensional heat map is converted to a one-dimensional heat map. Cells of the one-dimensional heat map are clustered using a density-based clustering algorithm to generate at least one dense region of cells. A mean value of cells in each dense region is calculated and the dense regions are sorted by mean value in descending order. An approach for identifying hot areas is selected and the selected approach is used to identify at least one dense region as a hot area of the one-dimensional heat map.

Abstract: Techniques and solutions are described for analyzing results of a machine learning model. A result is obtained for a data set that includes a first plurality of features. A plurality of feature groups are defined. At least one feature group contains a second plurality of features of the first plurality of features. The second plurality of features is less than all of the first plurality of features. Feature groups can be defined based on determining dependencies between features of the first plurality of features, including using contextual contribution values. Group contextual contribution values can be determined for feature groups by aggregating contextual contribution values of the constituent features of the feature groups.

Abstract: Systems and methods include determination, for each of a plurality of discrete features, of statistics based on a number of occurrences of each discrete value of the discrete feature in the data, determination of first summary statistics based on the determined statistics, determine of a dissimilarity for each discrete feature based on the first summary statistics and on the statistics determined for the discrete feature, determination of candidate discrete features based on the determined dissimilarities, determination, for each of the candidate discrete features, of second summary statistics based on values of a continuous feature associated with each discrete value of the candidate discrete feature, determination of a deviation score for each of the candidate discrete features based on the second summary statistics, and transmission of the candidate discrete features for display in association with the continuous feature based on the determined deviation scores.

Abstract: A method, a system, and a computer program product for analyzing error messages. A first error log generated as a result of an execution of at least one task of a computing system at a first instance is received. The first error log include a plurality of first error messages. A first association rules model is generated using the first error messages. The first association rules model includes a plurality of association rules defining one or more relationships. A second error log, including a plurality of second error messages, generated as a result of an execution of the task at a second instance is received and a second association rules model is generated using the second error messages. Based on the first and second association rules models, at least one error message pattern associated with execution of the at least one task is determined.

Abstract: Systems and methods include reception of a set of data including continuous features and a discrete feature, each continuous feature associated with a plurality of values and the discrete feature associated with a plurality of discrete values, determine, for each continuous feature, a relationship factor representing a relationship between the discrete feature and the continuous feature based on the plurality of values associated with the continuous feature and the plurality of discrete values, identify one of the continuous features associated with a largest one of the determined relationship factors, generate, for each of the other features, a correlation factor representing a correlation between the continuous feature and the identified continuous feature, determine, for each of the continuous features other than the identified continuous feature, a composite relationship score based on the relationship factor and the correlation factor associated with the feature, and present a visualization associated wi

Abstract: Systems and methods include determination, determine, for each of a plurality of discrete features, of statistics for each discrete value of the discrete feature based on values of a continuous feature associated with the discrete value, determination, for each discrete feature, of first summary statistics based on the statistics determined for each discrete value of the discrete feature, determination, for each discrete feature, of a dissimilarity based on the first summary statistics determined for the discrete feature and on the statistics determined for each discrete value of the discrete feature, determination of candidate discrete features of the discrete features based on the determined dissimilarities, the candidate discrete features comprising less than all of the discrete features, determination, for each of the candidate discrete features, of second summary statistics based on values of the continuous feature associated with each discrete value of the candidate discrete feature, determine of a devi