Abstract: This disclosure relates generally to configuring/building of applications. Typically, a deep learning (DL) application having multiple models composed and interspersed with corresponding transformation functions has no mechanism of efficient deployment on underlying system resources. The disclosed system accelerates the development of application to compose multiple models where each model could be a primitive model or a composite model itself. In an embodiment, the disclosed system optimally deploys a composable model application and transformation functions on underlying resources using performance prediction models, thereby accelerating the development and deployment of the application.

Abstract: The disclosure generally relates to system architectures, and, more particularly, to a method and system for system architecture recommendation. In existing scenario, a solution architect often gets minimum details about requirements, hence struggles to design a system architecture that matches the requirements. The method and system disclosed herein are to provide system recommendation in response to requirements provided as input to the system. The system generates an acyclic dependency graph based on parameters and values extracted from an obtained user input. The system then identifies a reference architectures that matches the requirements, and further selects components that match the architecture requirements. The system further selects technologies considering inter-operability of the technologies. Further, the system generates architecture recommendations for the user, based on the selected components, and technologies.

Abstract: This disclosure relates generally to accelerating development and deployment of enterprise applications where the applications involve both data driven and task driven components in data driven enterprise information technology (IT) systems. The disclosed system is capable of determining components of the application that may be task-driven and/or those components which may be data-driven using inputs such as business use case, data sources and requirements specifications. The system is capable of determining the components that may be developed using task-driven and data-drive paradigms and enables migration of components from the task driven paradigm to the data driven paradigm. Also, the system trains a reinforcement learning (RL) model for facilitating migration of the identified components from the task driven paradigm to the data driven paradigm. The system is further capable of integrating the migrated and existing components to accelerate development and deployment an integrated IT application.

Abstract: This disclosure relates generally to configuring/building of applications. Typically, a deep learning (DL) application having multiple models composed and interspersed with corresponding transformation functions has no mechanism of efficient deployment on underlying system resources. The disclosed system accelerates the development of application to compose multiple models where each model could be a primitive model or a composite model itself. In an embodiment, the disclosed system optimally deploys a composable model application and transformation functions on underlying resources using performance prediction models, thereby accelerating the development and deployment of the application.

Abstract: This disclosure relates generally to a method and system for generating labelled dataset using a training data recommender technique. Recommender systems face major challenges in handling dynamic data on machine learning paradigms thereby rendering inaccurate unlabeled dataset. The method of the present disclosure is based on a training data recommender technique suitably constructed with a newly defined parameter such as the labelled data prediction threshold to determine the adequate amount of labelled training data required for training the one or more machine learning models. The method processes the received unlabeled dataset for labelling the unlabeled dataset based on a labelled data prediction threshold which is determined using a trained training data recommender technique.

Abstract: This disclosure relates generally to accelerating development and deployment of enterprise applications where the applications involve both data driven and task driven components in data driven enterprise information technology (IT) systems. The disclosed system is capable of determining components of the application that may be task-driven and/or those components which may be data-driven using inputs such as business use case, data sources and requirements specifications. The system is capable of determining the components that may be developed using task-driven and data-drive paradigms and enables migration of components from the task driven paradigm to the data driven paradigm. Also, the system trains a reinforcement learning (RL) model for facilitating migration of the identified components from the task driven paradigm to the data driven paradigm. The system is further capable of integrating the migrated and existing components to accelerate development and deployment an integrated IT application.

Abstract: This disclosure relates generally to data meta model and meta file generation for feature engineering and training of machine learning models thereof. Conventional methods do not facilitate appropriate relevant data identification for feature engineering and also do not implement standardization for use of solution across domains. Embodiments of the present disclosure provide systems and methods wherein datasets from various sources/domains are utilized for meta file generation that is based on mapping of the dataset with a data meta model based on the domains, the meta file comprises meta data and information pertaining to action(s) being performed. Further functions are generated using the meta file and the functions are assigned to corresponding data characterized in the meta file. Further functions are invoked to generate feature vector set and machine learning model(s) are trained using the features vector set. Implementation of the generated data meta-model enables re-using of feature engineering code.

Abstract: Synthetic data generation using conventional statistical approaches or Machine Learning based approaches are not effective as each of them used independently does not capture the features/advantages of the other approach. The method disclosed provides a hybrid approach. A Bayesian model is used for generating synthetic data based on a single behavioral user trait for a plurality of rows. Further, a Machine learning (ML) model based approach is used to incrementally generate the remaining columns of the data set providing values of other features of interest.

Abstract: The disclosed embodiments illustrate method and system for data processing to recommend a list of physical stores in real-time for user-specified products and/or services. The method includes receiving a request, that comprises one or more products and/or services and one or more user-defined parameters, from a user-computing device. The method further includes aggregating information associated with the received request, profile information of a user, real-time traffic information, and geographical locations of a plurality of physical stores. Further, the method includes generating a recommendation list based on the aggregated information and a similarity score of the user for each of the one or more products and/or services. The method further includes transmitting the generated recommendation list to the user-computing device. The user may select a recommendation from the recommendation list for purchasing and/or availing products and/or services based on the selected recommendation.

Abstract: A method and a system for recommendation of a succession of one or more services for a service workflow are disclosed. In an embodiment, a query is received to retrieve one or more services from a repository of a plurality of services stored in a hierarchical structure. The hierarchical structure comprises a plurality of hierarchical levels, each hierarchical level in the plurality of hierarchical levels comprising a set of nodes. Each node in the set of nodes in each hierarchical level, stores a first semantic information and a second semantic information. Further, the query is compared with the first semantic information and the second semantic information associated with a first node. The first node or the second node is selected based on the comparison. Further, the first set of services or the combination of services, associated with the selected node is recommended, as the service workflow, to the user.

Abstract: The present disclosure discloses methods and systems for enhancing shopping experience in physical stores. The method includes receiving at least one persona associated with a user based on one or more of: ethnographic data obtained from a user, demographic data associated with the user, buying behavioral data associated with the user, and social networking data associated with the user. After this, one or more historical activities of the user inside one or more physical stores are received. Also, one or more constraints associated with the user are received. Once received, the at least one persona, the one or more constraints, and the one or more historical activities are analyzed to generate a pre-defined number of personalized recommendations. Finally, the personalized recommendations are displayed to the user within a window of a user interface.

Abstract: The disclosure generally relates to system architectures, and, more particularly, to a method and system for system architecture recommendation. In existing scenario, a solution architect often gets minimum details about requirements, hence struggles to design a system architecture that matches the requirements. The method and system disclosed herein are to provide system recommendation in response to requirements provided as input to the system. The system generates an acyclic dependency graph based on parameters and values extracted from an obtained user input. The system then identifies a reference architectures that matches the requirements, and further selects components that match the architecture requirements. The system further selects technologies considering inter-operability of the technologies. Further, the system generates architecture recommendations for the user, based on the selected components, and technologies.

Abstract: A method and a system for recommending services to a requestor over a communication network. A request comprising one or more keywords and one or more service level agreements (SLAs), is received from a requestor computing device over the communication network, to process one or more tasks. One or more first services from one or more available services are selected based on the request. For a first service from the one or more first services, a first score and a second score is determined. The one or more first services are ranked based on the first score and the second score. A recommendation of the one or more first services is transmitted to the requestor based on the ranking.

Abstract: The disclosed embodiments illustrate methods and systems for predicting service assurance between requestors and crowd workers for task processing on a crowdsourcing platform. The method includes receiving one or more service level agreement (SLA) attributes of one or more tasks. The method further includes selecting a first set of crowd workers, from a plurality of crowd workers associated with the crowdsourcing platform. The method further includes selecting a second set of crowd workers from one or more SLA-based clusters of the selected first set of crowd workers. The method further includes predicting the service assurance between the requestor and each of the selected second set of crowd workers based on at least performance sustenance parameters associated with the one or more tasks and the selected second set of crowd workers.

Abstract: The present disclosure discloses methods and systems for enhancing shopping experience in physical stores. The method includes receiving at least one persona associated with a user based on one or more of: ethnographic data obtained from a user, demographic data associated with the user, buying behavioral data associated with the user, and social networking data associated with the user. After this, one or more historical activities of the user inside one or more physical stores are received. Also, one or more constraints associated with the user are received. Once received, the at least one persona, the one or more constraints, and the one or more historical activities are analyzed to generate a pre-defined number of personalized recommendations. Finally, the personalized recommendations are displayed to the user within a window of a user interface.

Abstract: The disclosed embodiments illustrate method and system for data processing to recommend a list of physical stores in real-time for user-specified products and/or services. The method includes receiving a request, that comprises one or more products and/or services and one or more user-defined parameters, from a user-computing device. The method further includes aggregating information associated with the received request, profile information of a user, real-time traffic information, and geographical locations of a plurality of physical stores. Further, the method includes generating a recommendation list based on the aggregated information and a similarity score of the user for each of the one or more products and/or services. The method further includes transmitting the generated recommendation list to the user-computing device. The user may select a recommendation from the recommendation list for purchasing and/or availing products and/or services based on the selected recommendation.

Abstract: Methods and systems for determining prices of customized virtual machines required to process customer-specified workloads are disclosed. A count of instances of the customized virtual machines, required to process the customer-specified workloads is determined, based on a configuration of the customized virtual machines. The instances of the customized virtual machines are consolidated on virtual machine servers. Further, the prices of the customized virtual machines are determined based on a count of the virtual machine servers, unused resources in the virtual machine servers, and unused resources in the customized virtual machines. The determined prices are recommended to the customer. Further, at least one of the prices of the customized virtual machines or the configuration of at least one or more customized virtual machines is modified, based on a response to the recommendation received from the customer.

Abstract: A method and a system for recommending services to a requestor over a communication network. A request comprising one or more keywords and one or more service level agreements (SLAs), is received from a requestor computing device over the communication network, to process one or more tasks. One or more first services from one or more available services are selected based on the request. For a first service from the one or more first services, a first score and a second score is determined. The one or more first services are ranked based on the first score and the second score. A recommendation of the one or more first services is transmitted to the requestor based on the ranking.

Abstract: A method and a system for recommendation of a succession of one or more services for a service workflow are disclosed. In an embodiment, a query is received to retrieve one or more services from a repository of a plurality of services stored in a hierarchical structure. The hierarchical structure comprises a plurality of hierarchical levels, each hierarchical level in the plurality of hierarchical levels comprising a set of nodes. Each node in the set of nodes in each hierarchical level, stores a first semantic information and a second semantic information. Further, the query is compared with the first semantic information and the second semantic information associated with a first node. The first node or the second node is selected based on the comparison. Further, the first set of services or the combination of services, associated with the selected node is recommended, as the service workflow, to the user.

Abstract: A method and a system are provided to derive one or more observations between a plurality of parameters in customer care data. The method includes receiving customer care data from a plurality of data sources. Thereafter the customer care data is transformed to create a plurality of data structures utilizing one or more semantic web protocols. The plurality of data structures represents a relationship between one or more parameters in the customer care data. Thereafter a subset of data structures is extracted from the plurality of data structures based on a query received via a query interface. One or more graph analytics techniques are applied on the subset of data structures to determine one or more observations associated with the subset of data structures. Thereafter the one or more observations pertaining to the subset of data structures are displayed on a display screen.