Tata Consultancy Services Limited

Symantec Corporation

Veritas Technologies LLC

Thales Dis Cpl Usa, Inc.

Sudhir Kumar

Sridhar Gopal Pattar

Ashish Diwan

Nandish Kirtikumar Solanki

Venkeepuram R Satish

Ashish Karnik

Nirendra Awasthi

Yen-Fen Hsu

Haochong Shen

Provided is a system and platform for Machine Learning (ML) based Data Discovery and Classification. The system and platform comprising components of a user console, a ML agent, and a ML data engine. By way of a ML pipeline, sensitive data is obfuscated that would otherwise by in the clear when transmitted to a centralized server. The ML model pipeline decouples embedding from model training. In a first step, the ML Agent runs on data endpoint machine or proxy to convert clear text data to embedding vectors. In a second step, the ML data engine runs on a centralized server to train models using the embedding vectors. The separation of pipeline components and respective handling of workflow requests and messages associated therewith prevents the transfer of clear data in the open. Other embodiments disclosed.

Machine Learning (ML) model pipeline with obfuscation to protect sensitive data therein

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

A method and apparatus for reliable I/O performance anomaly detection. In one embodiment of the method, input/output (I/O) performance data values are stored in memory. A first performance data value is calculated as a function of a first plurality of the I/O performance data values stored in the memory. A first value based on the first performance data value is calculated. An I/O performance data value is compared to the first value. A message is generated in response to comparing the I/O performance value to the first value.

Method and apparatus for reliable I/O performance anomaly detection in datacenter

Techniques for managing data storage within storage tiers are disclosed. In one particular embodiment, the techniques may be realized as a method for managing data storage having the steps of assigning a storage class for each of a plurality of storage tiers, defining at least one rule for storing data in the plurality of storage tiers, determining whether performance assisted tier migration is requested, monitoring the plurality of storage tiers when it is determined that the performance assisted tier migration is requested, determining whether to redefine the storage class of one of the plurality of storage tiers based on the monitoring, and determining whether to migrate the data based on the monitoring.

Techniques for managing data storage

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Pune, India

Sudhir Kumar