Abstract: The invention relates to a method and system for predicting optimal configuration parameters for a data analytics engine. The method and system utilizes Supervised Machine Learning (ML) techniques for predicting optimal configuration parameters for the data analytics engine to run a specific application in a given time frame, wherein the optimal configuration includes estimating an optimal number of executor instances in real-time. The invention analyzes input parameters such as data metrics, software metrics, and hardware metrics to determine if the input parameters comprise a new dataset or a continuous dataset and deploy one or more models such as a Forecasting Model and a Regression Model. The one or more models then derive and allocate the possible number of executor instances. The executor instances allocated can be a number of executors, executor cores, the memory of executors, driver memory, and parallelism.

Abstract: Provided is a method and system (108) for building and leveraging a knowledge fabric (110) in a Software Development Lifecycle (SDLC). A plurality of SDLC artifacts are received from a plurality of heterogeneous data sources (102). The plurality of SDLC artifacts are then correlated to build an end-to-end correlation and are clustered to generate an SDLC knowledge fabric (110). This includes extracting semantic and contextual data from the plurality of SDLC artifacts using Natural Language Processing (NLP) and deep text analytics and transforming the extracted semantic and contextual data to knowledge graphs. One or more actionable items (112) are then derived using the SDLC knowledge fabric (110) and the one or more actionable items (112) are used to improve overall process efficiency and accelerate software delivery in the SDLC.

Abstract: The invention provides a smart building model (104) for calculating real-time global sustainability score and forecasting global sustainability score of an entity (102) with buildings at multiple geographical locations. The smart building model (104) comprises a data acquisition module (214) that acquires data from a plurality of sensors that monitor data of assets associated with each building. A key performance indicator (KPI) module (216) assess KPIs based on data acquired from the sensors. Sustainability score of each building is evaluated by a sustainability score evaluation module (218) that compares KPIs of the assets against a selected compliance standard. A global sustainability score of the entity (102) is estimated by aggregating sustainability scores of each building and assessing weights assigned to the KPIs by an aggregation module (220). A reporting module (222) displays a report with global sustainability score of the entity (102) along with sustainability scores of each building.

Abstract: The invention provides an intelligent resiliency engineering module for investigating resiliency of a software application, wherein the module is configured to identify business transactions associated with the software application to determine observability scenarios and orchestrate chaos engineering in a target infrastructure. The intelligent resilience engineering module identifies chaos simulation scenarios and a target infrastructure for the software application, to inject the one or more chaos simulation scenarios to simulate and orchestrate chaos attacks and the business transaction in the target infrastructure. Based on the simulation and orchestration, a correlation module by leveraging Artificial Intelligence (AI) models, correlates infrastructure telemetry data with Software Development Lifecycle (SDLC) digital assets.