Tata Consultancy Services Limited

Tata Consultancy Services Limited Mumbai

Brajesh Singh

Sachin Shetty

Prasanta Panda

Sitarama Brahmam Gunturi

Ritesh Kumar Singh

Pranavi Sura

Prince Kumar Singh

This disclosure relates to a system and method for quantitative measure of subject specific sentiment analysis of a text input. The text input comprises subjects and objects. The text input is tokenized, and each word of the tokenized text input is tagged based on a part-of-speech (POS) and a universal dependency tag. A universal dependency tag tree is prepared based on dependency tags. Further, the subjects and objects are identified using a subject-verb-object (SVO) detection. The universal dependency tree is analyzed for each identified subject to determine a token dependency of the subject. The identified subject is quantified using a deep learning-based sentiment analyzer and finally a sentiment score is recommended for the subject using a probability score and a class score is assigned to the subject.

System and method to quantify subject-specific sentiment

System and method for multi-core processing based time series management, capable of handling high volume time series data with high speed processing for time series forecasting. The method includes storing each time series as a Structured Query Language (SQL) array in a single row using a plurality of time series parameters. Further, stored plurality of time series are analyzed on receiving a forecast request using a correlation based pattern detection mechanism. The pattern detection mechanism enables deriving a subset of forecasting models, from which an optimal or best-fit forecasting model is identified to generate one or more forecasted time series.

Method and system for multi-core processing based time series management with pattern detection based forecasting

This disclosure relates generally to a method and a system for extraction of key-terms and synonyms for the key-terms. The method discloses to extract the key-terms and further determine a set of relevant synonyms for the extracted key-terms, wherein the key-terms is a collection of small sub-set of words and phrases which are individually meaningful and collectively provide a generic context of a given input text. The method discloses techniques for extraction of key-terms that is independent of any specific supervised training, does not require a huge amount of training data and computationally less intensive. The key-terms are determined in several stages using frequency based techniques based on a relevancy scoring of the key-terms. Further a set of synonyms are determined for the identified key-terms based on a language-based approach or domain specific approach.

Method and system for extraction of key-terms and synonyms for the key-terms

Methods and systems for selecting an appropriate forecasting model for accurate workload prediction of a processor is disclosed. The processor is configured to monitor workload and extract workload history of the processor for a given time interval. Further, the processor is configured to create plurality of forecasting models based on the extracted workload history and apply the group of forecasting model on the extracted workload history to obtain a plurality of predicted future workload for the given time interval. Further, the processor is configured to compute an error measure of the plurality of predicted future workload in reference to an actual workload of the processor and select the appropriate forecasting model from plurality of the forecasting models having least error measure among the computed error measures for dynamically scaling frequency and voltage required by the processor and thereby optimizing energy consumption in the processor.

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Mumbai, India

Brajesh Singh