Abstract: Disclosed is a method and system for modifying data cleansing techniques for training and validating an Artificial Neural Network (ANN) model. The method comprises normalizing and validating data of different formats, obtained from different sources. The ANN model is trained using the normalized and validated data. Alternatively, the ANN model could be trained using data of a common format obtained from a deterministic data model. The trained ANN model is used to generate ANN stochastic data. Data validation component from the deterministic data model is reused for the normalizing and the validating of the data, for validating the ANN stochastic data.

Abstract: A method for performing deterministic data processing through Artificial Intelligence (AI) is disclosed. The method may include generating, via a deep learning network, a set of input feature vectors based on input data for a deterministic data processing model. The method may further include providing the set of input feature vectors to a trained AI model. The trained AI model may generate a set of output feature vectors that may correspond to an output data of the deterministic data processing model. The method may further include determining a variation between the set of output feature vectors and the output data, and iteratively performing incremental learning of the AI model based on the determined variation.

Abstract: A method for performing deterministic data processing through Artificial Intelligence (AI) is disclosed. The method may include generating, via a deep learning network, a set of input feature vectors based on input data for a deterministic data processing model. The method may further include providing the set of input feature vectors to a trained AI model. The trained AI model may generate a set of output feature vectors that may correspond to an output data of the deterministic data processing model. The method may further include determining a variation between the set of output feature vectors and the output data, and iteratively performing incremental learning of the AI model based on the determined variation.

Abstract: Disclosed is a method and system for modifying data cleansing techniques for training and validating an Artificial Neural Network (ANN) model. The method comprises normalizing and validating data of different formats, obtained from different sources. The ANN model is trained using the normalized and validated data. Alternatively, the ANN model could be trained using data of a common format obtained from a deterministic data model. The trained ANN model is used to generate ANN stochastic data. Data validation component from the deterministic data model is reused for the normalizing and the validating of the data, for validating the ANN stochastic data.

Abstract: Embodiments of the present invention provide systems, methods, and computer program products for processing data in an extract, transform, and load system. Embodiments of the present invention provide protective enhancements to be applied to data during extract-transform-load operations, including protections that can prevent unauthorized access and/or modifications to data stored on an intermediate computer system. Embodiments of the present invention can afford users with the ability to modify the protective enhancements and provide users with transformation operations compatible with the protective enhancements during extract-transform-load operations.

Abstract: Embodiments of the present invention provide systems, methods, and computer program products for processing data in an extract, transform, and load system. Embodiments of the present invention provide protective enhancements to be applied to data during extract-transform-load operations, including protections that can prevent unauthorized access and/or modifications to data stored on an intermediate computer system. Embodiments of the present invention can afford users with the ability to modify the protective enhancements and provide users with transformation operations compatible with the protective enhancements during extract-transform-load operations.

Abstract: Embodiments of the present invention provide, systems, methods, and computer program products for processing data in an extract, transform, and load system. Embodiments of the present invention provide protective enhancements to be applied to data during extract-transform-load operations, including protections that can prevent unauthorized access and/or modifications to data stored on an intermediate computer system. Embodiments of the present invention can afford users with the ability to modify the protective enhancements and provide users with transformation operations compatible with the protective enhancements during extract-transform-load operations.

Abstract: Embodiments of the present invention provide, systems, methods, and computer program products for processing data in an extract, transform, and load system. Embodiments of the present invention provide protective enhancements to be applied to data during extract-transform-load operations, including protections that can prevent unauthorized access and/or modifications to data stored on an intermediate computer system. Embodiments of the present invention can afford users with the ability to modify the protective enhancements and provide users with transformation operations compatible with the protective enhancements during extract-transform-load operations.

Abstract: A desired object stored on a computer system is retrieved by retrieving one or more objects based on an object attribute to produce initial search results, selecting an object from the initial search results, where the selected object is associated with one or more other attributes, and retrieving one or more additional objects based on one or more of the other attributes of the selected object to expand the initial search results and provide the desired object. The objects are linked to one or more tree structures each associated with an object attribute, such that the retrieving one or more additional objects includes traversing the tree structures linked to the selected object and associated with the one or more other attributes to identify the one or more additional objects.

Abstract: Embodiments of the present invention provide, systems, methods, and computer program products for processing data in an extract, transform, and load system. Embodiments of the present invention provide protective enhancements to be applied to data during extract-transform-load operations, including protections that can prevent unauthorized access and/or modifications to data stored on an intermediate computer system. Embodiments of the present invention can afford users with the ability to modify the protective enhancements and provide users with transformation operations compatible with the protective enhancements during extract-transform-load operations.

Abstract: Embodiments of the present invention provide, systems, methods, and computer program products for processing data in an extract, transform, and load system. Embodiments of the present invention provide protective enhancements to be applied to data during extract-transform-load operations, including protections that can prevent unauthorized access and/or modifications to data stored on an intermediate computer system. Embodiments of the present invention can afford users with the ability to modify the protective enhancements and provide users with transformation operations compatible with the protective enhancements during extract-transform-load operations.

Abstract: A desired object stored on a computer system is retrieved by retrieving one or more objects based on an object attribute to produce initial search results, selecting an object from the initial search results, where the selected object is associated with one or more other attributes, and retrieving one or more additional objects based on one or more of the other attributes of the selected object to expand the initial search results and provide the desired object. The objects are linked to one or more tree structures each associated with an object attribute, such that the retrieving one or more additional objects includes traversing the tree structures linked to the selected object and associated with the one or more other attributes to identify the one or more additional objects.