Sap Se

Apoorv Bhargava

Markus Schmidt-Karaca

Ismail Basha

Gonda Marcusse

Vishnu Kare

Naveed Mohammed

Raghavendra Rao M G

Aswin Kumar Jayaraman

Madathiveetil Bipin

Praveen Kumar

Santhosh Rao

Daniel Zimmermann

Jaison Jacob

Syed Aleemuddin Noor

Naveed Mohammed B

Neenu Vincent

Anthony Arun B

Markus Goeppert

Gowthami Agumamidi

Technologies are described for determining between synchronous and asynchronous modes for computing service requests. Computing service requests are received by a computing service from clients. The computing service dynamically determines whether to use synchronous mode or asynchronous mode for processing the computing service requests. The computing service makes the dynamic determination of which mode to use (synchronous or asynchronous) based on various criteria, which can include synchronous/asynchronous mode recommendations generated by machine learning models and/or synchronous/asynchronous mode recommendations generated by static rules.

Learning-based dynamic determination of synchronous/asynchronous behavior of computing services

Methods and systems are disclosed for data retrieval, from databases to clients, in an environment requiring runtime authorization. In response to a request for T data records, a learning module provides a prediction R of a suitable number of data records to retrieve from a database. Following retrieval of R records or record identifiers, authorization is sought from an authorization service, resulting in A of the records being authorized. The A authorized records are returned to the requesting client, and, if more records are needed, T is decremented and the cycle is repeated. A performance notification is provided to the learning module for training, with respect to providing values of prediction R. The performance notification can be based on a measure of authorization service performance, the number A of authorized records, latency, communication or resource costs, a measure of resource congestion, or other parameters. Variants are disclosed.

Adaptive data retrieval with runtime authorization

Technologies are described for performing automated data integration, reconciliation, and/or self-healing using machine learning. For example, data integration can be checked using a reconciliation procedure. The number of times that the reconciliation is performed can be determined dynamically by a machine learning model. For each iteration, reconciliation can be performed to check integrated data against source data. If any reconciliation errors are found, then self-healing operations can be performed. Results of the reconciliation can be output. The reconciliation results can be used to update the machine learning model so that the machine learning model can dynamically adjust the number of iterations to perform based at least in part on reconciliation results.

Automated data integration, reconciliation, and self healing using machine learning

Methods, systems, and computer program products for a database processing system are described. A core database comprises common procedures for processing information, at least one of the common procedures configured to provide a common functionality to an analysis application. A content import database comprises content-specific procedures, at least one of the content-specific procedures configured to provide access for at least one of the common procedures to content of the content import database that conforms to a content-specific data model.

Flexible content integration

Methods and systems are disclosed for flexibly managing lock modes in a data-sharing environment, including optimistic and pessimistic lock modes. A lock mode determination service receives a request from an application instance for a lock mode with which to access specified data objects. The service can determine and provide a suitable lock mode based on a context of the application, and optionally other parameters, by evaluating a checklist of criteria sorted in precedential order. Criteria can include geo-boundaries, platform, role, and others. The requesting application instance uses the determined lock mode to access the data, with optional assistance of a lock service. The lock mode determination service can support one or more applications having one or more instances each, and can also support multiple disjoint data-sharing environments. Suitable environments include cloud, datacenter, mobile, client-server, and SAAS.

Lock mode determination service

To probe linear and non-linear relationships of entities in a network, in a graphical user interface, in a login session, a selection of an action on a first node is received. In response to the selection of the action on the first node, the action is automatically performed. A navigation breadcrumb is dynamically generated in the graphical user interface with a first navigation identifier corresponding to the action performed on the first node and a corresponding first state information of the nodes. In database tables in a database, the first state information of the nodes corresponding to the first navigation identifier is stored. Selection of the first navigation identifier on the navigation breadcrumb is received, and in response to the selection, the first state information of the nodes is retrieved from the database tables and displayed in a canvas in an area of focus.

Probing linear and non-linear relationships between entities in a network

A system includes reception, at a server and in a first browser session, of a request from a client for a token to access a first software service, determination of a token stored in a server memory of the server and associated with the first service and the client, determination, at the server, of whether a validity period of the token is within a predetermined period of expiration, and, if it is determined that the validity period of the token is within a predetermined period of expiration, transmission of a request for a new token to access the first software service from a token provider associated with the first service, reception of the new token from the token provider, and provision of the new token to the client in the first browser session.

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Bengaluru, India

Apoorv Bhargava