Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, to predict future Day Sales Outstanding (DSO) forecasts for a number of future time periods. In one aspect, a method includes receiving open receivables financial line item data and revenue financial line item data, providing the open receivables financial line item data to a DSO predictor engine to generate a predicted open receivables that includes a multi-step time series forecasting regression generated from the open receivables financial line item data, providing the revenue financial line item data to the DSO predictor engine to generate a predicted revenue comprising the multi-step time series forecasting regression generated from the revenue financial line item data; generating a predicted DSO with the predicted open receivables and predicted revenue, and providing the predicted DSO to a client device.

Abstract: Some embodiments provide execution of a query on a target data model which is filtered on a measure of a source data model, even if the source data model and the target data model are not logically linked. Some embodiments further support execution of a query on a target data model which is filtered on a measure of a source data model and on a dimension filter of another data model. Some embodiments provide for a substantial amount of query execution to occur on the backend, thereby freeing client resources in comparison to prior approaches.

Abstract: Embodiments provide an overview visualization of graph data by rendering a graph's structure based upon different node/edge types, rather than upon a total number of nodes and/or edges. These node/edge types are recognized based upon associated metadata. The small number of different node/edge types of a typical graph reduces components for representation in the overview, simplifying that visualization and facilitating user interaction. A user can switch between the graph overview, and a different view affording node-level details. In analysis and/or querying, switching between the overview and the detailed visualization can afford natural and intuitive control over data granularity. For example the overview visualization of graph data may allow a user to initially filter on particular node types, with the subsequent node-level view permitting exploration of finer details while maintaining the overall context of the original graph.

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: According to some embodiments, a business intelligence data store contains semantic layer information. A business intelligence language platform may receive a business intelligence language query expression from a user. The business intelligence language platform may then access the mapping information in the business intelligence model data store to compute an output type describing a result structure entity for the query expression. According to some embodiments, the output type may be used to automatically recommend at least one visualization chart to the user. Moreover, in some embodiments the output type is annotated with meta-data information about a business object.

Abstract: A system includes reception of a first query from a first user, identification, in response to reception of the first query, of a first plurality of data entries, each of the first plurality of data entries associating the first user, the first query and a respective visualization type with a respective counter value, determination of one of the first plurality of data entries associated with a greatest respective counter value of the counter values of the first plurality of data entries, determination of a respective visualization type of the one of the first plurality of data entries, and presentation of a visualization of the determined visualization type of a first result set corresponding to the first query.

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 system includes acquisition of data from a first data source, identification of logical entities of a first data model of the first data source based on the data of the first data source, identification, for one or more of the logical entities of the first data model, of a respective knowledge vault entity described by first metadata, and generation of second metadata describing a plurality of semantic layer entities, the second metadata describing a semantic layer entity comprising at least one semantic attribute of a knowledge vault entity corresponding to the semantic layer entity.

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: According to some embodiments, an abstraction engine may determine a data manipulation definition generated by a creator platform to accesses a database via a first connection protocol (e.g., ODBC). The abstraction engine may then automatically analyze the data manipulation definition to discover a connectivity parameter (e.g., a DSN, a DBMS type, a DBMS host name, a port, etc.) associated with the access to the database via the first connection protocol. The data manipulation definition may then be stored along with the connectivity parameter as a meta-connection into a metadata repository. A consuming platform may retrieve the meta-connection from the metadata repository translate the meta-connection into the database manipulation definition to accesses the database via a second connection protocol (e.g., JDBC).

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 presentation of a visualization of a result set, the visualization including a plurality of graphical representations, each of the plurality of graphical representations associated with a respective data point comprising at least one dimension value and at least one measure of the result set, reception of a selection of a first data point of the plurality of data points, presentation of an interface to receive a one or more comments in response to selection of the first data point, reception of a first one or more comments, and storage, in response to reception of the first one or more comments, of an identifier of the first data point in association with the first one or more comments.

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.