Abstract: Systems and techniques that facilitate computing touchpoint journey recommendations are provided. In various embodiments, an input component can receive a computing context of a client and a computing profile of a client. In various instances, the client can be engaged in a computing touchpoint journey. In various embodiments, a prediction component can predict, via a first machine learning classifier, a negative event likely to occur on the computing touchpoint journey. In various cases, the first machine learning classifier can receive as input the computing context and the computing profile and can generate as output the predicted negative event. In various embodiments, a decision component can recommend in real-time, via a second machine learning classifier, a computing touchpoint to which to transfer the client.

Abstract: Methods and systems for using machine learning to automatically determine a data loading configuration for a computer-based rule engine are presented. The computer-based rule engine is configured to use rules to evaluate incoming transaction requests. Data of various data types may be required by the rule engine when evaluating the incoming transaction requests. The data loading configuration specifies pre-loading data associated with at least a first data type and lazy-loading data associated with at least a second data type. Statistical data such as use rates and loading times associated with the various data types may be supplied to a machine learning module to determine a particular loading configuration for the various data types. The computer-based rule engine then loads data according to the data loading configuration when evaluating a subsequent transaction request.

Abstract: Methods and systems are presented for providing a container orchestration framework for facilitating development and deployment of software applications across different operating environments within an enterprise system. Upon receiving a service request for processing a set of data is received, the container orchestration framework determines one or more machines that store the set of data. Instead of processing the set of data remotely, the container orchestration framework deploys a container that encapsulates an application on the one or more machines. Each application instance running on the one or more machines are executed to process a corresponding subset of data stored on the machine locally. The container orchestration framework obtains the output data from executing the applications on each of the one or more machines, and present the output data as a response to the service request.

Abstract: Methods and systems are presented for providing a container orchestration framework for facilitating development and deployment of software applications across different operating environments within an enterprise system. Upon receiving a service request for processing a set of data is received, the container orchestration framework determines one or more machines that store the set of data. Instead of processing the set of data remotely, the container orchestration framework deploys a container that encapsulates an application on the one or more machines. Each application instance running on the one or more machines are executed to process a corresponding subset of data stored on the machine locally. The container orchestration framework obtains the output data from executing the applications on each of the one or more machines, and present the output data as a response to the service request.

Abstract: Methods and systems for using machine learning to automatically determine a data loading configuration for a computer-based rule engine are presented. The computer-based rule engine is configured to use rules to evaluate incoming transaction requests. Data of various data types may be required by the rule engine when evaluating the incoming transaction requests. The data loading configuration specifies pre-loading data associated with at least a first data type and lazy-loading data associated with at least a second data type. Statistical data such as use rates and loading times associated with the various data types may be supplied to a machine learning module to determine a particular loading configuration for the various data types. The computer-based rule engine then loads data according to the data loading configuration when evaluating a subsequent transaction request.

Abstract: Methods and systems for automatically discovering data types required by a computer-based rule engine for evaluating a transaction request are presented. Multiple potential paths for evaluating the transaction request according to the rule engine are determined. An abstract syntax tree may be generated based on the rule engine to determine the multiple potential paths. Based on an initial set of data extracted from the transaction request, one or more potential paths that are determined to be irrelevant to evaluating the transaction request are identified. Types of data required to evaluate the transaction request according to the remaining potential paths are determined. Only data that corresponds to the determined types of data is retrieved to evaluate the transaction request.

Abstract: Methods and systems for using machine learning to automatically determine a data loading configuration for a computer-based rule engine are presented. The computer-based rule engine is configured to use rules to evaluate incoming transaction requests. Data of various data types may be required by the rule engine when evaluating the incoming transaction requests. The data loading configuration specifies pre-loading data associated with at least a first data type and lazy-loading data associated with at least a second data type. Statistical data such as use rates and loading times associated with the various data types may be supplied to a machine learning module to determine a particular loading configuration for the various data types. The computer-based rule engine then loads data according to the data loading configuration when evaluating a subsequent transaction request.

Abstract: Systems and techniques that facilitate computing touchpoint journey recommendations are provided. In various embodiments, an input component can receive a computing context of a client and a computing profile of a client. In various instances, the client can be engaged in a computing touchpoint journey. In various embodiments, a prediction component can predict, via a first machine learning classifier, a negative event likely to occur on the computing touchpoint journey. In various cases, the first machine learning classifier can receive as input the computing context and the computing profile and can generate as output the predicted negative event. In various embodiments, a decision component can recommend in real-time, via a second machine learning classifier, a computing touchpoint to which to transfer the client.

Abstract: Methods and systems are presented for providing a container orchestration framework for facilitating development and deployment of software applications across different operating environments within an enterprise system. Upon receiving a service request for processing a set of data is received, the container orchestration framework determines one or more machines that store the set of data. Instead of processing the set of data remotely, the container orchestration framework deploys a container that encapsulates an application on the one or more machines. Each application instance running on the one or more machines are executed to process a corresponding subset of data stored on the machine locally. The container orchestration framework obtains the output data from executing the applications on each of the one or more machines, and present the output data as a response to the service request.

Abstract: Methods and systems for automatically discovering data types required by a computer-based rule engine for evaluating a transaction request are presented. Multiple potential paths for evaluating the transaction request according to the rule engine are determined. An abstract syntax tree may be generated based on the rule engine to determine the multiple potential paths. Based on an initial set of data extracted from the transaction request, one or more potential paths that are determined to be irrelevant to evaluating the transaction request are identified. Types of data required to evaluate the transaction request according to the remaining potential paths are determined. Only data that corresponds to the determined types of data is retrieved to evaluate the transaction request.

Abstract: Methods and systems for using machine learning to automatically determine a data loading configuration for a computer-based rule engine are presented. The computer-based rule engine is configured to use rules to evaluate incoming transaction requests. Data of various data types may be required by the rule engine when evaluating the incoming transaction requests. The data loading configuration specifies pre-loading data associated with at least a first data type and lazy-loading data associated with at least a second data type. Statistical data such as use rates and loading times associated with the various data types may be supplied to a machine learning module to determine a particular loading configuration for the various data types. The computer-based rule engine then loads data according to the data loading configuration when evaluating a subsequent transaction request.