Sap Se

Accenture Global Solutions Limited

Suresh Venkatasubramaniyan

Chung-Sheng Li

Madhura Shivaram

Kaushik Nath

Emmanuel Munguia Tapia

Abhishek Gunjan

Jingyun Fan

Abhishek Datta Sharma

Sawani Bade

Himani Shukla

Srikrishna Raamadhurai

Saumya Shekhar

Siddhartha Asthana

A system for reconciliation comprises a determination engine to determine whether data is structured or unstructured, a data structuring engine to structure the data, and a rule extraction engine to determine relations between pairs of values of a first set and a second set of data. The system further comprises a matching engine to generate a confidence score for each pair of the values, a categorization engine to classify the pairs of values into matched pairs and unmatched pairs, a validation engine to validate matching and classification of the pairs based on a user feedback, and a learning engine to store details pertaining to the validation of the matching and the classification over a period of time. The learning engine forwards the details to the rule extraction engine and the categorization engine to determine the relations between subsequent pairs of values and classify the pairs based on the stored details.

Data reconciliation

A centralized data reconciliation system processes at least two data streams transmitting data related to one of a plurality of processes and executes a data reconciliation procedure. Unmatched data records identified during the data reconciliation procedure are further categorized into categorized records based on various reason categories and irreconcilable records which could not be categorized into the reason categories. The irreconcilable records are flagged for user input. The user input is recorded to further train the data reconciliation system. The at least two data streams are initially converted into self-describing data streams from which the entities and entity attributes are extracted using the data models received from the data streams. The data records from the first and second self-describing data streams are mapped. The matched pairs and unmatched pairs are selected from the mappings based on respective confidence scores that are estimated in accordance with the rules of data reconciliation.

Centralized data reconciliation using artificial intelligence mechanisms

A system for facilitating content creation includes collecting profiles which are analyzed to build a profile parameter index. A dummy profile is created based on the profile parameter index. The dummy profile is a fictitious character having profile parameters based on input from a user of the profile parameter index. The control of the dummy profile is under the user.

Content creation

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Bengaluru, India

Suresh Venkatasubramaniyan