Business Objects Software Limited

Sap Se

Microsoft Technology Licensing, LLC

Flavia Silvana Moser

Julian Gosper

Scott Cameron

Alexander MacAulay

Katherine Wright

Ying Li

Arungunram Chandrasekaran Surendran

Viren Suresh Kumar

Johnson Wong

Saeed Jahankhani

Geoffrey Neil Peters

Jung-Rung Han

Jiandong Shi

Ahmet Yoldemir

Steve Norton

Sepideh Hashtroodi

Martin Ester

Teresa Hsin Yi Su

Alan Maher

Sajjad Gholami

Balazs Pete

Zeyu Ni

Andong Wang

Guanjie Zhao

Shariq Aziz

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.

Analytic insights for hierarchies

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.

Alerting system for software applications

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.

System and method of providing visualization explanations

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.

Multivariate insight discovery approach

Embodiments of the present disclosure pertain to network based machine learning generated simulations. In one embodiment, the present disclosure includes a computer implemented method comprising sending first code comprising a programmable calculator from a server system to a client system across a network. A data request is sent to a database, the data request configured to retrieve data from the database comprising a plurality of fields and a target field. The retrieved data is processed using a machine learning algorithm to produce a weight for each field of the plurality of fields and a scoring data structure. The fields and the scoring data structure are sent to the client system across the network. A user selects values for the plurality of fields and the programmable calculator is configured based on the scoring data structure to generate a simulated value for the target field based on the user selected values.

Network based machine learning generated simulations

Some embodiments provide a program that receives from a client device a dimension value for a first dimension in a plurality of dimensions of a data model and a measure in a set of measures of the data model. The program further determines a cardinality of dimension values for each dimension in the plurality of dimensions. The program also selects a subset of dimensions in the plurality of dimensions based on the cardinalities of dimension values for the plurality of dimensions. The program further sends the subset of dimensions in the plurality of dimensions to the client device. The program also receives from the client device a request for a chart visualization comprising data values associated with the measure and categorized according to a second dimension in the subset of dimensions. In response to the request, the program further sends the chart visualization to the client device.

Data insights for visualizations based on dimensions

Techniques of providing visualization suggestions are disclosed. In some example embodiments, a current visualization of at least a portion of data of a dataset is determined to be displayed to a user in a graphical user interface of a device, a plurality of visualization candidates is generated based on an application of transition rules to the current visualization, a corresponding score for each one of the plurality of visualization candidates is generated based on a corresponding level of data variance for the data of the corresponding visualization candidate, a ranking of the plurality of visualization candidates is generated based on the scores, at least one of the plurality of visualization candidates is selected based on the ranking, and a plurality of selectable visualization suggestions corresponding to the selected visualization candidates is caused to be displayed to the user in the graphical user interface of the device.

System and method of providing visualization suggestions

A dataset and some user selected columns of the dataset are received by a statistical analysis module for analysis. The statistical analysis module generates a score for each unselected column of the dataset based on statistical analysis of the unselected columns and all or a subset of the selected columns. A ranking of the unselected columns is presented to the user for selection of one additional column of the dataset, after which the remaining unselected columns are re-ranked according to their associated scores and once again displayed to the user. The user may continue selecting from among the ranked columns until a threshold number of columns has been selected, at which point the user may deselect a selected column in order to continue selecting additional columns. A visualization suggestion application program interface then matches the selected columns with compatible visualization configurations and presents some of these visualizations to the user.

Visualization suggestion application programming interface

Methods, systems, and computer-readable media for identifying intentions related to a user's present intention are provided. The user's present intention may be determined by mapping the user's current actions to a present intent. A related intent may be determined using an intent chain. The intent chain may be generated by analyzing patterns of user activities and mapping the patterns of activities to the underlying intents.

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Vancouver, Canada

Flavia Silvana Moser