Tata Consultancy Services Limited

Prasanta Panda

Brajesh Singh

Subhranshu Kumar Sahoo

Shajeer Kootala Basheer

Anjusree Ramavarma

Sarjinder Singh Sethi

Considering the number of OSS components and the number of OSS license types available today, the number of license attributes to be considered for analyzing a product at a granular level is a challenge to perform manually, prudently considering legal implications of non-compliance and contamination and also within the limited time available today before go to market in the software industry. Systems and methods of the present disclosure intelligently facilitates a matrix which is able to identify OSS components in a deliverable and also facilitates the product owner to identify proprietary IP that can be suitably protected and licensed without contamination by the accompanying OSS components in the product under consideration. License attributes of the OSS components are mapped suitably and a final attribute is derived for each OSS component embedded in the product under consideration.

Systems and methods to analyze open source components in software products

The disclosure herein generally relates to recommender model selection, and, more particularly, to a method and system for selecting a recommender model matching user requirements. The system collects a user requirement including at least one error measure and corresponding Error Value (EV) and Error Tolerance (ET) as input. A recommendation learned model processes the user requirement and selects at least one of a plurality of recommender models as a recommender model matching the user requirement and generates a recommendation.

Method and system for recommender model selection

Method and system for model fitting to hierarchical time series clusters is described. A plurality of time series to be analyzed are clustered as hierarchical time series clusters using Dynamic Time Warping (DTW) as the optimal distance measure to create time series hierarchical clusters. The method disclosed recognizes least dissimilarity time series in the hierarchical time series clusters, and a best fit model is identified for the time series. The same model is continued up the hierarchy along the branch of the hierarchical time series clusters till the model identified satisfies Error Tolerance (ET) and Error Difference (ED) criteria. The method reduces the model fitting time or model building time by more than 50%.

Method and system for model fitting to hierarchical time series cluster

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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Bhubaneswar, India

Prasanta Panda