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: Systems and methods include determination of a first plurality of sets of data, each including values associated with respective ones of a first plurality of features, partial training of a first machine-learning model based on the first plurality of sets of data, determination of one or more of the first plurality of features to remove based on the partially-trained first machine-learning model, removal of the one or more of the first plurality of features to generate a second plurality of sets of data, partial training of a second machine-learning model based on the second plurality of sets of data, determination that a performance of the partially-trained second machine-learning model is less than a threshold, addition, in response to the determination, of the one or more of the first plurality of features to the second plurality of sets of data, and training of the partially-trained first machine-learning model based on the first plurality of sets of data.

Abstract: Some embodiments provide a program that receives a first selection of a first set of attributes in a first data model, a second selection of a second set of attribute in a second data model, a third selection of a first dimension in the first data model, and a fourth selection of a second dimension in second data model. The program further receives a request for data for a visualization. The program also generates a blend definition based on the first set of attributes, the second set of attributes, the first dimension in the first data model, and the second dimension in the second data model. The program further generates a blended query model based on the blend definition. The program also executes a query based on the blended query model to generate a result set of data. The program provides a visualization that includes the result set of data.

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: 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: Computer-readable media, methods, and systems are disclosed for an in-memory cache in a memory of a client device. The system may send a first request for a first data from the client device to the in-memory cache and may receive a null response. The system may send a second request from the client device for the first data to a server and may receive a response from the server with the first data. The system may then send the first data to the in-memory cache and store the first data in the in-memory cache, thereby eliminating an additional request for the first data from the server.

Abstract: Some embodiments provide a program that generates a first query for a first visualization based on a first query model associated with the first visualization. The first query model includes a first reference to a shared object. The program further sends the first query to a computing system. The program also receives from the computing system a first set of data. The program further generates the first visualization based on the first set of data. The program also generates a second query for a second visualization based on a second query model associated with the second visualization. The second query model includes a second reference to the shared object. The program further sends the second query to the computing system. The program also receives from the computing system a second set of data. The program further generates the second visualization based on the second set of data.

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: The present disclosure provides for accurate and efficient identification of candidate features for an input dataset comprising one or more continuous features and one or more categorical features is obtained. A number of categorical feature categories based on the one or more categorical features is determined. Record counts for each of the categorical feature categories are determined. Skew statistics for each category are determined based on the record counts for each of the categorical feature categories. Cardinality skew factors for each of the one or more categorical features are then determined based on the record counts and the skew statistics. A number of the one or more categorical features having the highest cardinality skew factors are selected from among the cardinality skew factors. Then, a top contributor deviation analysis is performed using the selected number of the categorical features having the highest cardinality skew factors.

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: Anomalies may be detected using a multiple machine learning model anomaly detection framework. A clustering model is trained using an unsupervised machine learning algorithm on a historical anomaly dataset. A plurality of clusters of records are determined by applying the historical anomaly dataset to the clustering model. Then it is determined whether each cluster of the plurality of clusters is an anomaly-type cluster or a normal-type cluster. The plurality of labels for the plurality of records are updated based on the particular record's cluster classification. Non-pure clusters are determined from among the plurality of clusters based on a purity threshold. A supervised machine learning model is trained for each of the non-pure clusters using the records in the given cluster and the labels for each of those records. Then, predictions of an anomaly are made using the clustering model and the supervised machine learning models.

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: 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.