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 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: The invention relates to a method and system for performing content-aware deduplication of video files and content storage cost optimization. The method includes pre-processing video files into a plurality of groups of video files based on type of genre and run-time of a video. The genre of a plurality of video files is automatically detected using a sliding-window similarity index, which is utilized to improve accuracy of genre detection. After the pre-processing step, each group of the plurality of groups of video files are simultaneously fed into a plurality of machine learning (ML) instances and models which measure a degree of similarity corresponding to each group of video files by detecting one or more conditions that exists in the video files. The one or more conditions are detected by performing deep inspection of content in the video files using hash-based active recognition of objects.

Abstract: The invention relates to a system (300) and method for performing end-to-end simulation and testing of an IoT application (102). An IoT data simulator (310) is configured to simulate an IoT environment using data received from different components in the IoT environment, which include IoT messages/data from IoT devices (106), master data from different databases (108) and data from third-party web services (110). Device templates are created that are used as blueprint for defining a plurality of device instances which include simulated device instances and live device instances. An IoT application validator (326) is configured for testing and validating the IoT application (102) by transmitting a plurality of IoT messages to the IoT application (102) and validating the behavior of the IoT application (102) to the plurality of IoT messages for all layers including, but not limited to, a UI layer (112), a business logic (114) and a data layer (116), using one or more device instances.

Abstract: The present invention discloses a method and a system for clustering of short and long text documents. The documents are input through an input module and a pre-processing module overtakes the documents from the input module. The pre-processing module refines the documents and removes unwanted text from the documents. Then a decision driven hybrid text clustering algorithm is applied via different modules to achieve clustering of the documents. Firstly, a context module computes a moment value of a feature signifying at least one feature importance value of the feature for the documents. The moment value is used by a decision module to calculate a decision score. Based on the decision score the documents are split into two sets. A clustering module then forms clusters of the two sets of documents based on n-tuple word distribution. Finally, a convergence module congregates the clusters in a final set of documents.

Abstract: The invention relates to a system (300) and method for performing end-to-end simulation and testing of an IoT application (102). An IoT data simulator (310) is configured to simulate an IoT environment using data received from different components in the IoT environment, which include IoT messages/data from IoT devices (106), master data from different databases (108) and data from third-party web services (110). Device templates are created that are used as blueprint for defining a plurality of device instances which include simulated device instances and live device instances. An IoT application validator (326) is configured for testing and validating the IoT application (102) by transmitting a plurality of IoT messages to the IoT application (102) and validating the behavior of the IoT application (102) to the plurality of IoT messages for all layers including, but not limited to, a UI layer (112), a business logic (114) and a data layer (116), using one or more device instances.

Abstract: The invention relates to a method and system for monitoring a plurality of critical assets (102a-102n) associated with a production/process management system using one or more edge devices (104a-104n). The one or more edge devices (104a-104n) track operations of the plurality of critical assets (102a-102n), which comprises obtaining consolidated information related to the operations of the plurality of critical assets (102a-102n). The one or more edge devices (104a-104n) then derive insights corresponding to the plurality of critical assets (102a-102n) based on the consolidated information using descriptive analytics and an AI/ML model (114) and derive a set of actionable insights to optimize the operations. The derived insights are then rendered in a real-time consolidated view, to enable a user to take immediate actions and decisions in relation to the plurality of critical assets (102a-102n).

Abstract: The invention relates to a method and system for performing content-aware deduplication of video files and content storage cost optimization. The method includes pre-processing video files into a plurality of groups of video files based on type of genre and run-time of a video. The genre of a plurality of video files is automatically detected using a sliding-window similarity index, which is utilized to improve accuracy of genre detection. After the pre-processing step, each group of the plurality of groups of video files are simultaneously fed into a plurality of machine learning (ML) instances and models which measure a degree of similarity corresponding to each group of video files by detecting one or more conditions that exists in the video files. The one or more conditions are detected by performing deep inspection of content in the video files using hash-based active recognition of objects.

Abstract: A system and method for generating a database query from a natural language using a query engine selector module for identifying appropriate database query format, a query engine builder module to generate appropriate database query using an intermediate query language 2 (IQL2) as input, and a data retriever module for fetching data from single or multiple data sources.

Abstract: The present invention discloses a system and method with associated feature capabilities for analyzing and resolving tickets raised in an IT environment. The system monitors health of multiple assets included in the environment and in case an alert is triggered, the system further analyses the alert and provides a recommendation on conversion of the alert to an incident ticket. The incident ticket can also be reported manually by human. Such tickets are then categorized based on certain influencing factors. To this end, a best possible resolution for each incident ticket is then recommended. Subsequently, automatic resolution scripts are identified intelligently and automatically to resolve the problem.

Abstract: The present invention discloses a method and a system for clustering of short and long text documents. The documents are input through an input module and a pre-processing module overtakes the documents from the input module. The pre-processing module refines the documents and removes unwanted text from the documents. Then a decision driven hybrid text clustering algorithm is applied via different modules to achieve clustering of the documents. Firstly, a context module computes a moment value of a feature signifying at least one feature importance value of the feature for the documents. The moment value is used by a decision module to calculate a decision score. Basis the decision score the documents are split into two sets. A clustering module then forms clusters of the two sets of documents basis n-tuple word distribution. Finally, a convergence module congregates the clusters in a final set of documents.

Abstract: Systems and methods for cognitive information mining are provided. A cognitive information extraction system provides intelligent information extraction from documents in different formats, types and forms. Since a huge portion of the data and information is still stored in unstructured documents in physical format, the system provides for a framework to extract information from such documents. Further, even in the digital form, the documents are available in multiple different formats, which can act as a great hindrance to useful extract information. The invention focuses upon mitigating this scenario by combining multiple AI models and modules to create a framework for document processing and human-machine interaction for training and QC verification, wherein the framework provides user the flexibility to work upon multiple types of documents, and also ensures that accuracy is maintained while the information is being extracted.

Abstract: The present invention discloses a system and method with associated feature capabilities for analyzing and resolving tickets raised in an IT environment. The system monitors health of multiple assets included in the environment and in case an alert is triggered, the system further analyses the alert and provides a recommendation on conversion of the alert to an incident ticket. The incident ticket can also be reported manually by human. Such tickets are then categorized based on certain influencing factors. To this end, a best possible resolution for each incident ticket is then recommended. Subsequently, automatic resolution scripts are identified intelligently and automatically to resolve the problem.

Abstract: The present disclosure discloses a system and method comprising a Natural Language Generation (NLG) module in a data visualization environment for generating a contextual narrative for visualization e.g. graphs in natural language. The narrative is generated using a composite system comprising business input, ontology structure comprising semantical relationship and a deep learning paraphrase model to express and enable semantics in a personalized manner. The essential element of the disclosure is the system and method for providing context to generated narrative, using ontology structure comprising semantic relationships and search criteria including, but not limited to, filters and types of aggregations.

Abstract: A method and a system for automatically detecting anomalies present within a dataset is provided. An embodiment performs analysis on detected anomalies to calculate severity and detects root cause of the anomalies. The dataset may contain sales, revenue, and profit data. The anomalies are detected may include a historical data deviation anomaly, a latest data deviation anomaly, a data add anomaly, or a data loss anomaly. Severity of the detected anomalies is calculated by a severity calculation scheme. The severity calculation uses several parameters in calculating the severity rank of the anomalies. A root cause of the detected anomalies is identified by lookup to detect probable reasoning behind the detected anomalies. Further, the dataset may be used to create a dashboard to visualize business insights.

Abstract: A system and method for generating a database query from a natural language using a query engine selector module for identifying appropriate database query format, a query engine builder module to generate appropriate database query using an intermediate query language 2 (IQL2) as input, and a data retriever module for fetching data from single or multiple data sources.