Tata Consultancy Services Limited

Ashish Diwan

Sudhir Kumar

Sridhar Gopal Pattar

Nandish Kirtikumar Solanki

Ashim Roy

Nikhil Girish Patwardhan

Anantha Desik Puranam Hosudurg

Sumiran Naman

As discussed earlier, labelling techniques that are available for labelling of unlabelled tabular data use some semi supervised models for identification purposes. However, they require sample labeled data for training purposes. Further, the same labelling model/technique cannot be used for all data types. Present disclosure provides method and system for identifying labels of unlabeled column data. The system uses a hybrid approach i.e., it uses language models, regular expressions and known dictionaries for labelling of unlabelled tabular data. For performing labelling, system first classifies received unlabelled tabular data into one or more data buckets. The system then uses appropriate techniques, based on data types, for identification of labels of unlabeled data present in data buckets. Thereafter, system uses feedback mechanism which will impart maturity to system over time. Finally, once system is matured, system can identify labels for all types of data.

Method and system for identifying labels of unlabelled column data

A method and system to identify similar names and addresses from given data set comprising plurality of names and addresses. The invention more specifically addresses the challenge faced in Spanish data quality assurance. The name and data is parsed through parsing engine to parse the plurality of Spanish names and addresses. The parsed Spanish names and addresses are sent to a Probable identification engine to identify the probable matches. The combination of name and address matching process can be used for assuring data quality for Spanish names and addresses. The Spanish name matching process consists of identification of probable matches and finding similarity percentages between those probable. Similarly, the Spanish address matching process consists of identification of probable matches (criteria like same city) and finding similarity percentages between those probable. The system includes a parsing engine, a probable identification engine and a match percentage calculation engine.

Method and system for managing data quality for Spanish names in a database

Method and system for managing data quality for spanish names in a database

Method and system for managing data quality for Spanish names and addresses in a database

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Pune, India

Ashish Diwan