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: 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 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: Techniques are described for integrating prediction capabilities from data management platforms into applications. Implementations employ a data science platform (DSP) that operates in conjunction with a data management solution (e.g., a data hub). The DSP can be used to orchestrate data pipelines using various machine learning (ML) algorithms and/or data preparation functions. The data hub can also provide various orchestration and data pipelining capabilities to receive and handle data from various types of data sources, such as databases, data warehouses, other data storage solutions, internet-of-things (IoT) platforms, social networks, and/or other data sources. In some examples, users such as data engineers and/or others may use the implementations described herein to handle the orchestration of data into a data management platform.

Abstract: In some example embodiments, an indication of a selected data point of a current visualization can be received. A context of the selected data point can be determined based on a dimension of the data point, and explanation candidates can be generated based on the context of the selected data point. Each exploration candidate can have a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context. For each one of the explanation candidates, a corresponding score can be generated based on a difference between the value for the explanation candidate and an average value of all the values of the explanation candidates. The explanation candidates can be ranked based on the scores. At least one of the explanation candidates can be selected based on the ranking, and selectable explanation(s) for the selected explanation candidate(s) can be displayed.

Abstract: The present disclosure describes methods, systems, and computer program products for enabling advanced analytics with large datasets.

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 involves systems, software, and computer implemented methods for ranking time dimensions. One example method includes receiving a request for an insight analysis for a dataset that includes a value dimension and a set of multiple date dimensions. Each date dimension is converted into a time series and a value quality factor is determined for each time series that represents a level of data quality for the time series. A time series informative factor is determined for each time series that represents how informative the time series is within a specified time window. An insight score is determined, for each time dimension, based on the determined value quality factors and the determined time series informative factors. The insight score for the time dimension is provided, for at least some of the time dimensions.

Abstract: Examples of dynamic database query efficiency improvement are provided herein. Query portions of a received database query can be identified as candidates for replacement. The candidates for replacement can be query portions that reduce the efficiency of the query. Alternative queries can be determined that include substitute query portion(s) in place of candidate(s) for replacement. An expected performance of the alternative queries can be determined. Based at least in part on the expected performance of the alternative queries, one or more alternative queries can be selected as replacement database queries for the received database query.

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: The present disclosure involves systems, software, and computer implemented methods for automatically recommending one or more frequencies for time series data. One example method includes receiving a request for an insight analysis for an input time series included in a dataset. For each of multiple frequencies to analyze, the input time series is transformed into a frequency time series. An absolute percentage change impact factor and an absolute trend impact factor are determined for each frequency time series. A frequency interest score is determined based on the determined absolute percentage change factors and the determined absolute trend impact factors, for each time frequency time series. The frequency interest score is provided for at least some of the frequency time series.

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: 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: In some example embodiments, an indication of a selected data point of a current visualization can be received. A context of the selected data point can be determined based on a dimension of the data point, and explanation candidates can be generated based on the context of the selected data point. Each exploration candidate can have a different dimension context that is within the context of the selected data point and a corresponding value for the dimension context. For each one of the explanation candidates, a corresponding score can be generated based on a difference between the value for the explanation candidate and an average value of all the values of the explanation candidates. The explanation candidates can be ranked based on the scores. At least one of the explanation candidates can be selected based on the ranking, and selectable explanation(s) for the selected explanation candidate(s) can be displayed.

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: 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: 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.