Abstract: Some embodiments provide a program. The program receives from a first webview a first request for data for a first visualization in a plurality of visualizations specified in a visualization collection definition. The program further retrieves the data for the first visualization in the plurality of visualizations. The program also sends the first webview the data for the first visualization in order for the first webview to render the first visualization. The program further receives from a second webview a second request for data for a second visualization in the plurality of visualizations specified in the visualization collection definition. The program also retrieves the data for the second visualization in the plurality of visualizations using the background processing thread. The program further sends the second webview the data for the second visualization in order for the second webview to render the second visualization.

Abstract: Methods, systems, and computer-readable storage media for receiving, by a semantic context service, a semantic context associated with a report generated by a first analytical tool, storing, by the semantic context service, the semantic context, receiving, by the semantic context service, a semantic context request from a second analytical tool, the semantic context request including a set of parameters, determining, by the semantic context service, a response to the semantic context request based on the set of parameters, the response including the semantic context, and returning, by the semantic context service, the response to the second analytical tool, the second analytical tool applying the semantic context to a report generated by the second analytical tool.

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: Techniques are described for performing a time series analysis using a clustering based symbolic representation. Implementations employ a clustering based symbolic representation applied to time series data. In some implementations, the time series data is discretized into subsequences with regular time intervals, and symbols encoding the time intervals may be derived by performing clustering algorithms on the subsequences. In the new representation, a time series is transformed into a sequence of categorical values. The symbolic representation is suitable to perform time series classification and forecast with higher accuracy and greater efficiency compared to previously used techniques. Through use of the symbolic representation, a dimension reduction is applied to transform the time sequences to a feature space with lower dimensions. As output of such transformation, a new representation is obtained based on the original time series.

Abstract: A method includes receiving a plurality of items, grouping the plurality of items into a plurality of clusters, where each of the plurality of clusters comprises items having similar features to one another, applying a classification model to each cluster to predict whether each item of a cluster will be delivered on time or delivered late, applying a regression model that determines an expected measure of tardiness of each item predicted to be delivered late, and outputting a delivery date prediction for each item predicted to be delivered late based on the expected measure of tardiness of the item.

Abstract: Some embodiments provide a program. The program receives a first request to perform a first function in a plurality of functions configured for execution by a web browser operating on a client device. The first function is implemented by a second function on the mobile device using native code. The second function is configured to emulate the first function. In response to the first request, the program further performs the second function. The program also receives a second request to perform a third function in the plurality of functions. The third function is implemented by a fourth function on the mobile device using scripting code compliant with a defined scripting standard. The fourth function is configured to emulate the third function. In response to the second request, the program further performing the fourth function.

Abstract: A database system includes a first table comprising a plurality of columns and a plurality of column values associated with each of the plurality of columns. For each of the plurality of columns, a structural relationship is determined with each other of the plurality of columns based on the plurality of column values associated with each of the plurality of columns. One or more of the plurality of columns comprising a primary key of the first table are determined based on the structural relationships.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for improving recommendation to users in data intelligence systems. In one aspect, a method includes the actions of receiving a current observation describing an interaction of a user with a data intelligence system; identifying a current user interest based on the current observation, wherein past observations of the user with the data intelligence system are clustered to form user interests in a Markov model; using the Markov model and based on the current user interest, determining a next user interest from the user interests; extracting a one past observation from the determined next user interest based on a selection criterion and a threshold, wherein the selection criterion is based on how closely the at least one past observation matches the current observation; and sending a recommendation to the user based on the past observation.

Abstract: In some example embodiments, a graphical user interface (GUI) is caused to be displayed on a computing device of a user. The GUI can be configured to enable the user to submit an identification of a dataset and at least one configuration parameter. The identification of the data source, the at least one configuration parameter, and the at least one wrangling parameter can be received via the GUI on the computing device. A sampling algorithm can be configured based on the at least one configuration parameter. A sample of data from the dataset can be generated using the configured sampling algorithm. At least one data wrangling operation can be performed on the sample of data based on the at least one wrangling parameter.

Abstract: A system includes determination of a component state tree of an application, determination of a node of the component state tree, where all child nodes of the node have been refactored to comprise an immutable model, determination of a consumer component associated with invalid access to a state of the node, and modification of the access to the state of the node by the consumer component.

Abstract: A raw dataset including measures and dimensions is processed, by a preprocessing module, using an algorithm that produces a preprocessed dataset such that at least one type of statistical analysis of the preprocessed dataset yields equal results to the same type of statistical analysis of the raw dataset. The preprocessed dataset is then analyzed by a statistical analysis module to identify subsets of the preprocessed dataset that include a non-random structure or pattern. The analysis of the preprocessed dataset includes the at least one type of statistical analysis that produces the same results for both the preprocessed and raw datasets. The identified subsets are then ranked by a statistical ranker based on the analysis of the preprocessed dataset and a subset is selected for visualization based on the rankings. A visualization module then generates a visualization of the selected identified subset that highlights a non-random structure of the selected subset.

Abstract: A system includes determination of a plurality of queries, each of the plurality of queries indicating a first object as a data source, generation of a first query on the first object based on a first two or more of the plurality of queries, modification of each of the first two or more queries to indicate the first query as the data source, requesting of execution of the first query and the modified first two or more queries, and reception of a result set associated with each of the first two or more queries.

Abstract: Techniques are described for identifying an input training dataset stored within an underlying data platform; and transmitting instructions to the data platform, the instructions being executable by the data platform to train a predictive model based on the input training dataset by delegating one or more data processing operations to a plurality of nodes across the data platform.

Abstract: A state capture designation for an element of an application within an application design environment may be received, indicating that the application, once deployed, will include a state capture functionality for capturing a state of the element, and a state restoration function for restoring the state of the element. Following deployment of the application within a runtime environment, and in response to an invocation of the state capture functionality, a captured state of the element may be stored. A state restoration request for the captured state may be received by way of the state restoration functionality, and following a re-deployment of the application with an update to the element that was included as part of an update to the application within the application design environment. The captured state may then be restored to the updated element, in response to the state restoration request, and within the runtime environment.

Abstract: A computer-implemented method for generating a plurality of data query suggestions is described. The method includes receiving a textual input in a user interface of a software application implementing a plurality of business processes and determining a query context for the textual input. The method also includes computing a plurality of ranked numerical scores based on the query context in which the ranked numerical scores are computed using information obtained from a plurality of usage metrics associated with the query context and determining, using the plurality of ranked numerical scores, a plurality of candidate data combinations. The method also includes ordering the plurality of candidate data combinations according to the plurality of ranked numerical scores and generating at least one data suggestion using the ordered plurality of candidate data combinations and providing the at least one data suggestion in the user interface.

Abstract: Embodiments visualize large data volumes utilizing initial sampling to reduce size of a dataset. This sampling may be random in nature. The sampled dataset may be refined (wrangled) by binning, grouping, cleansing, and/or other techniques to produce a wrangled sample dataset. A user defines useful end visualization(s) by inputting expected dimension/measures. From these visualizations of sampled data, minimal grouping sets are deduced for application to the full dataset. The user publishes/schedules the wrangled operation and grouping sets definition. Based on this, a wrangled dataset and grouping sets are produced in the big data layer. When the user accesses the visualization(s), minimal grouping sets are retrieved in the in-memory engine of the client and processed by an in-memory database engine according to the common processing plan. This produces result sets and a final set of visualizations of the full dataset, in which the user can recognize valuable data trends and/or relationships.

Abstract: Software program “help” functionality includes features enhancing its relevance to users. An engine may receive/record context data arising from interactions between a user and the software program. The engine may process that context data to reference a plurality of sources (e.g., official help documentation, user blogs, partner web sites, etc.) and harvest relevant help information. Based upon stored context data and help information available from the plurality of sources, certain embodiments may generate suggested questions allowing further exploration of help issues. Some embodiments may offer help information in the form of an interactive “hotspot” focusing upon a specific UI element (e.g., icon). Processing the context data may allow the engine to assign a particular skill level (e.g., novice/intermediate/expert) to individual users, tailoring the help information to that skill level.

Abstract: Software program “help” functionality includes features enhancing its relevance to users. An engine may receive/record context data arising from interactions between a user and the software program. The engine may process that context data to reference a plurality of sources (e.g., official help documentation, user blogs, partner web sites, etc.) and harvest relevant help information. Based upon stored context data and help information available from the plurality of sources, certain embodiments may generate suggested questions allowing further exploration of help issues. Some embodiments may offer help information in the form of an interactive “hotspot” focusing upon a specific UI element (e.g., icon). Processing the context data may allow the engine to assign a particular skill level (e.g., novice/intermediate/expert) to individual users, tailoring the help information to that skill level.

Abstract: Methods, systems, and computer program products for visually representing and displaying data are described. The visual representation may be a data animation. A data query may be submitted, a time measurement for processing the query may be obtained, and a sample size of the query may be adjusted based on the time measurement and a frame refresh rate of a data animation. A data animation may be generated based on one or more results of the query.

Abstract: A plurality of graphical elements may be represented as a document object model (DOM) having a plurality of nodes, each node representing a different graphical element, the DOM having an ordering of the nodes, each node having a z-value of front or back, indicating that the corresponding graphical element is to be displayed in a front layer for a z-value of front or a back layer for a z-value of back. The z-value of one or more of the plurality of nodes corresponding to one or more graphical elements to be moved to the front layer or back layer may be altered. The DOM can then be reordered so that the one or more graphical elements to be moved to the front layer or back layer are at the front or back of the DOM, while maintaining the ordering between nodes having z-values of front and the ordering between nodes having z-values of back.