Sap Se

Infosys Limited

Balaji Raghunathan

Debashis Banerjee

Hari Babu Krishnan

Krishna Hindhupur Vijay Sudheendra

Prasanna Kumar Govindappa

David Herman

Shruthi Jinadatta

Amit Saxena

Gaurav Rathi

Anilkumar Tambali

Pravinth Ganesan

Darpan Dinker

Panish Ramakrishna

Vipul Gupta

Hemanth Rajeswari Anantha

Swaminathan Natarajan

Venugopal Subbarao

Prasanna Nagesh Teli

Vishal Krishna Saxena

Thirumaleshwar Kunamalla

Sivaram Thangam Varghese

A system includes reception of a definition of a first attribute to associate with a logical object of the first application, reception of a definition of a second attribute to associate with a second logical object of the first application, determination, based on the definition of the first attribute and the definition of the second attribute, that the first attribute and the second attribute are associated with a same semantic, based on the determination, generation of e a first identifier associated with the first attribute and the second attribute and storing the first identifier associated with the first attribute and the second attribute in a first data schema associated with the first application, and storage of the first identifier associated with the first attribute and the second attribute in an analytics schema associated with an analytics application.

Custom term unification for analytical usage

A method for machine learning based document editing is provided. The method may include receiving, from a client, one or more inputs associated with a document. A recommendation to include and/or exclude a clause, a term, and/or a line item from the document may be generated by at least processing the one or more inputs with a machine learning model. The recommendation to include and/or exclude the clause, the term, and/or the line item from the document may be provided to the client. Related systems and articles of manufacture, including computer program products, are also provided.

Machine learning based document editing engine

Various embodiments of systems, computer program products, and methods for encrypting data in a multi-tenant cloud environment are described herein. In an aspect, an encryption time frame to encrypt data associated with a user in a multi-tenant cloud environment may be retrieved. Based on the encryption time frame, a list of object types to be encrypted may be identified. A batch encryption period may be determined for encrypting data corresponding to the list of object types. Further, batches are sequentially selected based on the batch encryption period, for a selected batch: one or more data records may be retrieved based on the batch encryption period and the one or more data records may be encrypted in groups based on at least one throttling value.

Data encryption in a multi-tenant cloud environment

A computer implemented method for customer response management the method comprising the step of collecting information from social networking sites wherein the information is at least a post or comment for at least a brand or product; identifying customer among pluralities of authors of the information on the social networking sites; categorizing the information into at least a complaint, lead, service request, information request or feedback; analyzing the categorized information; prioritizing the analyzed information; and routing the prioritized information to at least a concerned personnel or a work flow of the Customer Relationship Management (CRM) system for response. The method further comprising identifying the influence of the customer wherein the influence is at least a global influence or local influence. The identification of customer is done by fetching information from at least the Customer Relationship Management (CRM) system or social profile aggregator service.

System and method for categorization of social media conversation for response management

System and methods for anonymizing data is disclosed. The method includes intercepting a communication, identifying sensitive data in the communication and transmitting data to an anonymization engine for masking. Masked data is received from the anonymization engine and transmitted to a client device.

Methods and systems for runtime data anonymization

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Bengaluru, India

Balaji Raghunathan