Abstract: Provided is a system and method which generates a composite machine learning model that can filter downtime data from a time-series data signal and perform a prediction on remaining time-series data. In one example, the method may include detecting a pattern of downtime data within a time-series data signal, removing a subset of data from the time-series data based on the detected pattern of downtime and building a machine learning model to make predictions based on remaining data in the time-series data, generating segregation instructions configured to remove downtime data from a time-series data signal of a same type and to predict zero on future dates matching the downtime segregation codes, and building a composite machine learning model that includes the trained machine learning model and the segregation instructions for filtering data that is input to the trained machine learning models.

Abstract: Systems and methods include reception of a plurality of records, each of the plurality of records associating each of a plurality of features with a respective value, a second feature with a value, and a target feature with a value, a first machine learning model trained based on the plurality of records to output a value of the target feature based on values of each of the plurality of features, a second machine learning model trained based on the plurality of records to output a value of the second feature based on the values of each of the plurality of features, determination, based on the trained second machine learning model, of a first one or more of the plurality of features which are correlated to the second feature, determination of an influence of each of the first one or more features on the trained first machine learning model, and determination of a first value associated with the second feature based on the determined influences and on the trained second machine learning model.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for a continuous software development and delivery system. Currently, the software development and delivery process is complicated and time consuming. The process requires multiple checkpoints and human intervention to ensure that software updates do not cause errors in the existing software when implemented. Coordination throughout the development process is also problematic as developers may be using varying development environment that are different than the environment used to test and produce the software. The continuous development and delivery system of the present disclosure remedies these issues by providing a consistent development environment throughout the development process. This reduces issues that may arise from use of inconsistent development environments and allows for automation of previously manual portions of the software development and delivery process.

Abstract: A system and method including receiving numeric data of a first dataset including a plurality of columns having numeric values with one of the plurality of columns specified as a target column; generating a trained generative model based on numeric values in non-target columns of the plurality of columns; generating a trained predictive model based on numeric values in non-target columns of the plurality of columns being input variables and the target column being a target variable; generating, by the trained generative model, a new set of numeric data for the non-target columns; generating predicted target values for the non-target columns by the trained predictive model using the new set of numeric data as an input to the predictive model; and generating anonymized numeric data for the first dataset by combining the new set of numeric data and the target column populated with the generated predicted target values.

Abstract: Provided is a system and method which can identify a causal relationship for anomalies in a time-series signal based on co-occurring and preceding anomalies in another time-series signal. In one example, the method may include identifying a recurring anomaly within a time-series signal of a first data value, determining a time-series signal of a second data value that is a cause of the recurring anomaly in the time-series signal of the first data value based on a preceding and co-occurring anomaly in the time-series signal of the second data value, and storing a correlation between the preceding and co-occurring anomaly in the time-series signal of the second data value and the recurring anomaly in the time-series signal of the first data value.

Abstract: Provided is a system and method which decomposes a predicted output signal of a time-series forecasting model into a plurality of sub signals that correspond to a plurality of components, and determines and displays a global contribution of each component. In one example, the method may include iteratively predicting an output signal of a time-series data value via execution of a time-series model, decomposing the predicted output signal into a plurality of component signals corresponding to a plurality of components of the time-series machine learning algorithm, respectively, and displaying the plurality of global values via a user interface.

Abstract: An example method and system for display of out-of-window status indicators in a virtual shelf of a diagram window. A diagram framework displays a first portion of a diagram within a diagram window of a display device. The diagram comprises a set of shapes and a set of connectors representing a corresponding set of relationships between a set of objects. The framework detects that a first shape of the set of shapes at a first position of the first shape and a first status indicator associated with the first shape at a first position of the first status indicator are at least partially outside a first visible portion of the diagram within the diagram window. The diagram framework determines a second position of the first status indicator within the diagram window. The first status indicator at the second position of the first status indicator is displayed within the diagram window.

Abstract: Systems and methods include reception of an object instance representing a date dimension member and comprising a key value, a user interface representation of the date dimension member, key values of ancestor date dimension members, and user interface representations of the one or more ancestor date dimension members, display of the user interface representation and the user interface representations of the ancestor date dimension members, reception of a request to cast the date dimension member to a higher granularity, and, in response to the request, generation of a second object instance representing a second date dimension member comprising key values of a second one or more of the ancestor date dimension members and user interface representations of the second one or more of the ancestor date dimension members, and display of the user interface representations of the second one or more of the ancestor date dimension members.

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: 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: 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: 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: 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: 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: 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: 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