Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program executable by a device. The program identifies a set of visualizations associated with the user. The program further determines, for each visualization in the set of visualizations, a score associated with changes in the visualization. The program also determines a subset of the set of visualizations based on the set of scores. The program further provides to the user notifications associated with the subset of the set of visualizations.

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: 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: 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: 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: Embodiments of the present disclosure include systems and methods for generating names for cloud storage containers. A unique identifier associated with a user of the client device is received from the client device. Next, a hash value is generated based on the unique identifier associated with the user of the client device. A character encoding scheme is then used to encode the hash value into a first encoded value. Instances of a first character in the encoded value is replaced with a defined second character to form a second encoded value. Finally, the second encoded value is sent to a cloud storage service for the cloud storage service to create a storage container using the second encoded value as a name of the storage container.

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: 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: Systems and methods include determination, for each of a plurality of discrete features, of statistics based on a number of occurrences of each discrete value of the discrete feature in the data, determination of first summary statistics based on the determined statistics, determine of a dissimilarity for each discrete feature based on the first summary statistics and on the statistics determined for the discrete feature, determination of candidate discrete features based on the determined dissimilarities, determination, for each of the candidate discrete features, of second summary statistics based on values of a continuous feature associated with each discrete value of the candidate discrete feature, determination of a deviation score for each of the candidate discrete features based on the second summary statistics, and transmission of the candidate discrete features for display in association with the continuous feature based on the determined deviation scores.

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

Abstract: A system and method including determining, for a specified target measure column of a first dataset including a plurality of records, the metadata of the first dataset, including a probability distribution for the specified target column and dimension scores for the dimensions for the first dataset conditioned on the specified target measure column, where the first dataset comprises a plurality of columns including the at least one target measure column and a plurality of non-numeric, dimension columns for the records of the first dataset; determining, for a subset of data of the first dataset based on one or more specified variables, dimension scores for the dimensions of the subset of data approximately derived from the determined metadata of the first dataset; and providing recommendations of top contributors based on the approximated dimension scores of dimensions of the subset of data.

Abstract: A question library aids in intuitive analysis of stored data. The question library comprises: 1) a plurality of text questions, 2) a numerical representation (e.g., a vector) of each text question, and 3) a corresponding query in a query language. A numerical vector is generated for a question posed to a database. If a matching library question (based upon vector similarity) is not found, the user receives the original answer. If a matching library question based upon vector similarity is found, the user receives the answer to that library question (with potential modifications). Embodiments may determine similarity by calculating Pearson's coefficient, Spearman's rho, or Kendall's tau. Embodiments may parse the first query to identify constituent elements (measures, dimensions, filters). These entities are extracted and compared to elements of the second question matched within the library, to allow modification of the library query to align with the initial query.

Abstract: Point-of-Interest (POI) data is stored according to a first, POI library as a flat file in a database. An engine is positioned between the POI library and an interface. The interface is configured to interact with data structured in a hierarchical manner and stored according to a second library (e.g., a modeling interface utilizing folders). The engine functions to wrap a POI object taken from a table of the POI library, into an augmented object recognized and handled by the second library of the (hierarchical) interface. To accomplish this, the engine augments the POI table with an additional column that is to be indexed by the second library. In this manner, embodiments may impart additional, hierarchy-based functionality to existing POI handling schemes. Particular embodiments may impart enhanced security (e.g., permissions at the data object-level) to existing POI schemes that implement permissions only at the POI-level and/or at the application-level.

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: 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: Computer-readable media, methods, and systems are disclosed for displaying a visual indication that an analytics rendering is authentic, and the integrity of the data is accurate and trusted. An analytics rendering comprising at least one table, chart, or graphic rendered from a plurality of aggregated data inputs from a plurality of microsystems may be selected. In a user interface for the analytics rendering, one or more structural identifiers associated with each data input of the plurality of aggregated data inputs can be displayed. A data input from the plurality of data inputs can then be selected and, responsive to receiving an instruction from a user, a visual indicator can be applied to the data input. If the one or more data inputs having an applied visual indicator is modified, the visual indicator will be visually altered in response the modification to the one or more data inputs.

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