Abstract: The disclosure relates generally to knowledge retrieval using ontology-based context matching. Conventional knowledge retrieval methods and systems are based on generative context that is modeled loosely as some meta data annotations. The meta data annotations do not provide sufficient semantic basis for meaningful context matching, compromising safety aspect. Other typical systems are designed such that the context is encoded into knowledge elements themselves, thus matching is too strict and the knowledge can only be used in contexts that match exactly, compromising the reuse aspect. The disclosed method and system explicitly models the context in which knowledge is generated, and matches the problem context with the generative context of knowledge, with respect to the underlying domain ontology, to assess the degree of similarity between the two, which then provides the basis for ranking the suitability of different knowledge elements for the problem context.

Abstract: Failure analysis of industrial plants are stored in various types of documents associated with industrial plant. The documents are used by operators of plant to address any deviation that is active in plant. The operator generally has prior knowledge of relevant processes, equipment and sensors described in a deviation scenario in these documents. However, a system that is envisaged to aid operator in real-time does not have this information readily available as this knowledge is spread across documents. Currently available systems manually curate failure knowledge thereby making the process time consuming and prone to human errors. Present disclosure provides method and system for performing extracting and collating failure knowledge from diverse sources in industrial plant.

Abstract: The present disclosure provides a dynamic functional adaptation of software systems based on real time need, which is unaddressed by the conventional methods. Initially, the system receives an output generated by a software system under execution corresponding to a given input. Further, a deviation in the generated output is identified based on a comparison between the generated output and a corresponding expected output. Further, an optimal policy is identified based on the generated output and the identified deviation using a reward based Reinforcement Learning (RL) model. Further, an adaptation specification is generated based on the identified optimal policy using an Adaptation Specification Generation (ASG) technique, Further, a software code is generated dynamically for the identified optimal policy using an ASG based code generation technique.

Abstract: The disclosure generally relates to methods and systems for generating synthetic microstructure images of a material with desired features. Conventional techniques that make use of unsupervised deep generative models has no control on the generated microstructure images with specific, desired set of features. The present disclosure generates the synthetic microstructure images of the material with desired feature, by using a variational autoencoder defined with a style loss function. In the first step, the variational autoencoder is trained to learn latent representation of microstructure image of the material. In the second step, some of the dimensions of learned latent representation is interpreted as physically significant features. In the third and last step, the latent representation required for getting the desired features is appropriately sampled based on the interpretation to generate the synthetic microstructure images of the material with desired features.

Abstract: All the model-driven systems may not have capability to perform designing and execution of experiments, which limits functionality of such model-driven systems. The disclosure herein generally relates to Design of Experiments (DOE), and, more particularly, to a model driven sub-system for design and execution of experiments. The sub-system when plugged into the model driven system, uses legacy components as well components of the sub-system to perform designing and execution of the design of experiments.

Abstract: Systems and methods for data migration in multi-layer model-driven applications is provided. The traditional systems and methods simply provide for comparison based migration approaches, and thus face severe challenges in case of model-driven applications, wherein continuous capturing of transformations in model changes is required. Embodiment of the proposed disclosure provide for a changelog based data migration methodology by modelling, a model-driven application conceptual model; generating, a plurality of optimized data models from the modelling; extracting, from each of the plurality of optimized data models, at least one changelog capturing one or more model changes and transformations in each of the plurality of optimized data models; and executing the data migration using each of an executing changelog.

Abstract: The disclosure relates generally to knowledge retrieval using ontology-based context matching. Conventional knowledge retrieval methods and systems are based on generative context that is modeled loosely as some meta data annotations. The meta data annotations do not provide sufficient semantic basis for meaningful context matching, compromising safety aspect. Other typical systems are designed such that the context is encoded into knowledge elements themselves, thus matching is too strict and the knowledge can only be used in contexts that match exactly, compromising the reuse aspect. The disclosed method and system explicitly models the context in which knowledge is generated, and matches the problem context with the generative context of knowledge, with respect to the underlying domain ontology, to assess the degree of similarity between the two, which then provides the basis for ranking the suitability of different knowledge elements for the problem context.

Abstract: The disclosure generally relates to methods and systems for generating synthetic microstructure images of a material with desired features. Conventional techniques that make use of unsupervised deep generative models has no control on the generated microstructure images with specific, desired set of features. The present disclosure generates the synthetic microstructure images of the material with desired feature, by using a variational autoencoder defined with a style loss function. In the first step, the variational autoencoder is trained to learn latent representation of microstructure image of the material. In the second step, some of the dimensions of learned latent representation is interpreted as physically significant features. In the third and last step, the latent representation required for getting the desired features is appropriately sampled based on the interpretation to generate the synthetic microstructure images of the material with desired features.

Abstract: Systems and methods for data migration in multi-layer model-driven applications is provided. The traditional systems and methods simply provide for comparison based migration approaches, and thus face severe challenges in case of model-driven applications, wherein continuous capturing of transformations in model changes is required. Embodiment of the proposed disclosure provide for a changelog based data migration methodology by modelling, a model-driven application conceptual model; generating, a plurality of optimized data models from the modelling; extracting, from each of the plurality of optimized data models, at least one changelog capturing one or more model changes and transformations in each of the plurality of optimized data models; and executing the data migration using each of an executing changelog.

Abstract: This disclosure relates to a model driven framework for realizing domain specific search system for unstructured text. The framework has two parts: information extraction system for defining various models such as a meta model, an instance model, an extraction model and mention model and a generic domain agnostic search system that works by interpreting the models specified via the information extraction system. The components of the search system are completely domain agnostic with no hard coded knowledge of the domain. The search system interprets the domain models specified in terms of the meta model to impart domain specificity to the search engine. In this sense, the framework is domain agnostic and it can be tailored for a new domain by just specifying domain related information in terms of the meta model. The model driven approach obviates need for re-coding the search system for any new domain of interest.

Abstract: The present disclosure provides a system and method for inverse inference in a chain of manufacturing processes using Bayesian networks is provided. The method generates a composite Bayesian network model for a chain of manufacturing processes from Bayesian network models of the unit processes in the chain. The models of unit processes might have been learned independently in other contexts and stored in a knowledge repository. Models relevant for the current problem context are obtained from the knowledge repository and checked for compatibility using ontological information about their inputs and outputs. The obtained compatible Bayesian network models of unit processes are composed to generate a composite Bayesian network model for the chain. The generated composite Bayesian network model is sampled to perform inverse inference.

Abstract: A method facilitating interoperability across the integrating platforms is provided by composing purpose specific integrating tools using conceptual modeling framework and a set of primitive foundational components. The model driven architecture provides easy migration from one solution architecture to another. Also, the present invention discloses a method to migrate from one implementation to another using set of composed purpose specific integration tools.

Abstract: This disclosure relates to a model driven framework for realizing domain specific search system for unstructured text. The framework has two parts: information extraction system for defining various models such as a meta model, an instance model, an extraction model and mention model and a generic domain agnostic search system that works by interpreting the models specified via the information extraction system. The components of the search system are completely domain agnostic with no hard coded knowledge of the domain. The search system interprets the domain models specified in terms of the meta model to impart domain specificity to the search engine. In this sense, the framework is domain agnostic and it can be tailored for a new domain by just specifying domain related information in terms of the meta model. The model driven approach obviates need for re-coding the search system for any new domain of interest.

Abstract: A method facilitating interoperability across the integrating platforms is provided by composing purpose specific integrating tools using conceptual modeling framework and a set of primitive foundational components. The model driven architecture provides easy migration from one solution architecture to another. Also, the present invention discloses a method to migrate from one implementation to another using set of composed purpose specific integration tools.

Abstract: A computer implemented system and method that combines data from multiple sources to provide a unified view of data using multi-layered conceptual modeling framework. The invention provides means to model conceptual data models at multiple levels of abstraction and a means to map them with each other and with physical models. A query rewriting approach adopted in the present invention is capable of translating queries down multiple levels of conceptual models into physical models and Conceptual modeling framework provide a means to capture and process semantic intent of data more accurately thereby reducing data integration errors significantly.

Abstract: A method facilitating interoperability across the integrating platforms is provided by composing purpose specific integrating tools using conceptual modeling framework and a set of primitive foundational components. The model driven architecture provides easy migration from one solution architecture to another. Also, the present invention discloses a method to migrate from one implementation to another using set of composed purpose specific integration tools.

Abstract: A computer implemented system and method that combines data from multiple sources to provide a unified view of data using multi-layered conceptual modeling framework. The invention provides means to model conceptual data models at multiple levels of abstraction and a means to map them with each other and with physical models. A query rewriting approach adopted in the present invention is capable of translating queries down multiple levels of conceptual models into physical models and Conceptual modeling framework provide a means to capture and process semantic intent of data more accurately thereby reducing data integration errors significantly.

Abstract: A model framework for generating batch programs has an abstraction representing a batch program, an abstraction representing a batch function of the program, an abstraction representing operation of the function, an abstraction representing a data provider to the function, and an abstraction representing a context class of the function. The framework is characterized in that instantiation of the model with the appropriate input data parameters input to each abstraction generates appropriate instances of batch functions including function operations wherein the generated instances are executable as part of a run sequence of the batch program.

Abstract: A tool for versioning and configuration management of object models in a computing system including a component container for grouping objects to form a component containing the objects, the objects having properties and associations and a configuration container for grouping the assembled components to form a configuration. Each component is assigned a version number upon creation whereupon subsequent versions, if any, of the component are derived directly or indirectly from an earlier version, the objects accessibly isolated within the assigned component versions except for inter-component associations established between compatible ones of the objects in separate but compatible components of the configuration, the associations forming the basis for assembling, managing and function of the configuration.

Abstract: A software control interface for creating and editing system views of a business information system using object models shaped to define the system views has a drawing tool for visually representing system views using model elements represented by icons, a first specification language for defining the various elements of a drawing, and a second specification language for defining mapping between drawing icons and objects stored in an object repository of the software system. The interface is characterized in that a user operating the drawing tool through a graphical user interface can, by dragging and dropping icons of the drawing tool onto a drawing sheet of the drawing tool, specify abstract models and model extensions expressed in diagrammatic notation transparent to the user and usable by the software system and associated repository.