Sap Se

Prasanna Bhat Mavinakuli

Rahul Tiwari

Nikita Jain

Pranav Acharya

Tanushree Nandy

Ganesh Vaitheeswaran

Arindam Bhattacharjee

Tejram Jagannath Sonwane

Sathya G

Ashwini Deshpande

Srivathsa Gunda Dixit

Sivakanth Jayaram

Madhukar Samak

Sowmya Kalidindi

Balasubramaniyan Krithivasan

Vignesh Sankaran

Shruthi Annappa

Shanu Gupta

Hariprasaad Sabapathy

Dipti Sengupta

Example methods and systems are directed to data extraction from input data objects. A data extraction schema may define at least a first feature and a second feature. The first feature may be associated with a first machine learning model and the second feature may be associated with a second machine learning model. A first input data object is accessed. The first machine learning model may be used to extract a first output data object associated with the first feature from the first input data object, based on the data extraction schema. The second machine learning model may be used to extract a second output data object associated with the second feature from the first input data object, based on the data extraction schema. The first output data object and the second output data object may be presented on a user interface.

Customizable data extraction service

Methods, systems, and computer-readable storage media for receiving, by an IRFT, a commit, the commit including one or more code changes to the project, in response to receiving the commit, providing, by the IRFT, a dependency map, the dependency map defining dependencies between the project and one or more of one or more other projects, one or more packages, and one or more bundles, generating, by the IRFT, a project test set based on the dependency map, the project test set including one or more projects each having one or more features to be regression tested, and initiating, by the IRFT, execution of regression testing of features of projects included in the project test set, wherein regression testing comprises executing a sub-set of test cases based on priority.

Intelligent regression fortifier

A method and system including one or more data sources; a memory storing processor-executable process steps; and a processor to execute the processor-executable process steps to cause the system to: initiate a business intelligence (BI) tool; connect to a BI platform; receive selection of an initiate query control; retrieve one or more database coupling components, wherein each database coupling component corresponds to a data source; display the retrieved one or more database coupling components; and receive selection of at least one of the retrieved one or more database coupling components to provide data in response to execution of a query. Numerous other aspects are provided.

Automatic routing of connection requests in business intelligence (BI) tools

Example embodiments of a system and method for analyzing and causing presentation of an impact or influence of a value of a dimension on a data set are described. In an example embodiment, a data set organized according to a first dimension is accessed from a data store. An influence value indicating an influence on the data set of at least one value of the first dimension is calculated. At least one of an influence rating and an influence ranking of the at least one value of the first dimension is determined based on the calculated influence value. The influence rating or ranking of the at least one value relative to other values of the first dimension is caused to be presented in conjunction with at least a portion of the data set organized according to the first dimension.

Data dimension impact analysis

A system includes reception of a first user instruction to access a dataset stored in a data source, retrieval of metadata of the dataset from the data source in response to the first user instruction, the metadata defining a plurality of dimensions and measures associated with the dataset, reception of a second user instruction to associate a first one of the plurality of dimensions and measures with a first visualization, determination of a first one or more dimensions or measures associated with the first one of the plurality of dimensions and measures, retrieval, from the data source, of data of the dataset associated with the determined first one or more dimensions or measures and the first one of the plurality of dimensions and measures, updating of the first visualization based on the retrieved data, and display the updated first visualization on a display.

Hybrid connectivity for reporting application

Collaboration application includes a set of shared documents shared with users with different authorization rights. A request to provide relevant shared documents from the set of shared documents is received. Document ranks corresponding to the relevant shared documents are determined. The document ranks are determined based on weighted document factors related to the set of shared documents. A ranked list of the relevant shared documents is provided according to the determined document ranks. User rank for users in relation to a document from the relevant shared documents is determined. The user ranks are determined based on user factors and weights of the user factors. The user factors are related to authorization rights of the users to the document, sharing characteristics of the document and the users, and measurements over interactions of the users with documents from the set of shared documents that are shared with the users.

Ranking of shared documents and users

The disclosure generally describes computer-implemented methods, software, and systems, including a method for providing suggestions. Transaction information is received that is associated with user actions during use by a user of a business intelligence tool. Each user action is associated with an operation in a particular stage of processing on business data obtained from one or more databases. The transaction information for a particular user action includes a user identifier identifying the user performing the particular user action, stage information, an associated operation, and parameters. The transaction information is stored. Subsequent user actions are monitored, including determining a time at which stage conditions match stage information in the stored transaction information. In response to determining matching stage conditions, pertinent transactions are identified. Suggestions are created. Each suggestion is associated with groups of one or more transactions of the pertinent transactions. The suggestions are provided for presentation to the user.

Business information service tool

The described technologies can be used for scheduled synchronization of documents. In one example, a method of controlling synchronization according to a stored update schedule can comprise locally storing a document comprising one or more visualizations based on at least one or more datasets. The update schedule can be stored in association with the document. The update schedule can indicate a periodic interval for synchronizing the document. Login credentials for a remote server can be stored. An upload of the document to the remote server can be initiated without user interaction and according to the update schedule. Uploading can comprise logging into the remote server using the stored login credentials and transmitting the document to the remote server, whereby the locally stored document and the uploaded document are synchronized at periodic intervals.

Scheduled synchronization

Techniques allow automatic identification of statistically significant attribute combinations in a dataset, and provide users with an understanding thereof including starting points for further analysis. Statistically significant combinations may be obtained from large data sets by limiting combinations to four or fewer attributes. The combinations obtained may be ranked to differentiate patterns, e.g. according to factors such as error ratio, decision tree depth, occurrences, and number of attributes. Still further insights may be achieved by ranking attributes according to the number of statistically significant combinations in which they appear. For useful visualization of statistically significant information within the patterns, only those having at least one measure/numeric may analyzed for further insight (e.g. by an outlier algorithm) and presented as output in a chart (e.g. pie, bar) form. The decision tree approach of various embodiments may facilitate 'What if' analysis of the data, as well as obtaining the reverse inference.

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Sagar Tq., India

Prasanna Bhat Mavinakuli