Abstract: A claims processing system includes a claims assistance component to handle the flow of data through the claim process. The data may include any documents submitted for claim processing and may be generated by disparate sources. The system may train one or more machine learning (ML) models to classify the unstructured data by insurance categories and/or determine workflow. The system may assist a user through a user device to upload or capture claims data with the device. The claims assistance component may classify the data and verify the data classification before uploading it to the servers. In examples, the claims assistance component may determine that the file size of the data exceeds a cellular data transfer threshold and determine to compress the data and/or hold the transfer until a free connection is detected.

Abstract: A big-data view integration platform generates integration guided user interfaces (GUIs). A first edge node ingests push-based and pull-based data from a plurality of platform services, which include legacy and non-legacy services having incompatible communication protocols. An event-based queue receives from the first edge node a plurality of queue events as indirect push-based data. A second set of queue events includes direct push-based data as received directly from a non-legacy platform service. A conformity component integrates the push-based data, the pull-based data, and the plurality of queue events into integration data having an enhanced integration format. A view integration component generates a plurality of data views from the integration data. A second edge node exposes the plurality of data views via an access services application programming interface (API). A new service execution component accesses the access services API to generate integration GUIs based on the data views.

Abstract: A big-data view integration platform generates integration guided user interfaces (GUIs). A first edge node ingests push-based and pull-based data from a plurality of platform services, which include legacy and non-legacy services having incompatible communication protocols. An event-based queue receives from the first edge node a plurality of queue events as indirect push-based data. A second set of queue events includes direct push-based data as received directly from a non-legacy platform service. A conformity component integrates the push-based data, the pull-based data, and the plurality of queue events into integration data having an enhanced integration format. A view integration component generates a plurality of data views from the integration data. A second edge node exposes the plurality of data views via an access services application programming interface (API). A new service execution component accesses the access services API to generate integration GUIs based on the data views.

Abstract: A system and method for evaluating the performance and usage of a cognitive computing tool which answers questions from users. A log file for these interactions includes the questions, the answers and a confidence rating assigned by the tool to each answer. Questions and answers are analyzed to determine validity, accuracy, and categories by subject matter experts or text analytics tools, and the results are added to the log file. Comments and sentiments from users may be analyzed and added to the log file. Additional data about the users, such as identities, demographics, and locations, may be added. Data from the log file may be presented in a dashboard display as metrics, such as trends and comparisons, describing the usage and performance of the cognitive computing tool. Answers may be displayed as they were presented to the users. Selectable filters may be provided to control the data displayed.

Abstract: A big-data view integration platform generates integration guided user interfaces (GUIs). A first edge node ingests push-based and pull-based data from a plurality of platform services, which include legacy and non-legacy services having incompatible communication protocols. An event-based queue receives from the first edge node a plurality of queue events as indirect push-based data. A second set of queue events includes direct push-based data as received directly from a non-legacy platform service. A conformity component integrates the push-based data, the pull-based data, and the plurality of queue events into integration data having an enhanced integration format. A view integration component generates a plurality of data views from the integration data. A second edge node exposes the plurality of data views via an access services application programming interface (API). A new service execution component accesses the access services API to generate integration GUIs based on the data views.

Abstract: A big-data view integration platform generates integration guided user interfaces (GUIs). A first edge node ingests push-based and pull-based data from a plurality of platform services, which include legacy and non-legacy services having incompatible communication protocols. An event-based queue receives from the first edge node a plurality of queue events as indirect push-based data. A second set of queue events includes direct push-based data as received directly from a non-legacy platform service. A conformity component integrates the push-based data, the pull-based data, and the plurality of queue events into integration data having an enhanced integration format. A view integration component generates a plurality of data views from the integration data. A second edge node exposes the plurality of data views via an access services application programming interface (API). A new service execution component accesses the access services API to generate integration GUIs based on the data views.

Abstract: A system and method for evaluating the performance and usage of a cognitive computing tool which answers questions from users. A log file for these interactions includes the questions, the answers and a confidence rating assigned by the tool to each answer. Questions and answers are analyzed to determine validity, accuracy, and categories by subject matter experts or text analytics tools, and the results are added to the log file. Comments and sentiments from users may be analyzed and added to the log file. Additional data about the users, such as identities, demographics, and locations, may be added. Data from the log file may be presented in a dashboard display as metrics, such as trends and comparisons, describing the usage and performance of the cognitive computing tool. Answers may be displayed as they were presented to the users. Selectable filters may be provided to control the data displayed.

Abstract: A big-data view integration platform generates integration guided user interfaces (GUIs). A first edge node ingests push-based and pull-based data from a plurality of platform services, which include legacy and non-legacy services having incompatible communication protocols. An event-based queue receives from the first edge node a plurality of queue events as indirect push-based data. A second set of queue events includes direct push-based data as received directly from a non-legacy platform service. A conformity component integrates the push-based data, the pull-based data, and the plurality of queue events into integration data having an enhanced integration format. A view integration component generates a plurality of data views from the integration data. A second edge node exposes the plurality of data views via an access services application programming interface (API). A new service execution component accesses the access services API to generate integration GUIs based on the data views.

Abstract: A big-data view integration platform generates integration guided user interfaces (GUIs). A first edge node ingests push-based and pull-based data from a plurality of platform services, which include legacy and non-legacy services having incompatible communication protocols. An event-based queue receives from the first edge node a plurality of queue events as indirect push-based data. A second set of queue events includes direct push-based data as received directly from a non-legacy platform service. A conformity component integrates the push-based data, the pull-based data, and the plurality of queue events into integration data having an enhanced integration format. A view integration component generates a plurality of data views from the integration data. A second edge node exposes the plurality of data views via an access services application programming interface (API). A new service execution component accesses the access services API to generate integration GUIs based on the data views.

Abstract: A big-data view integration platform generates integration guided user interfaces (GUIs). A first edge node ingests push-based and pull-based data from a plurality of platform services, which include legacy and non-legacy services having incompatible communication protocols. An event-based queue receives from the first edge node a plurality of queue events as indirect push-based data. A second set of queue events includes direct push-based data as received directly from a non-legacy platform service. A conformity component integrates the push-based data, the pull-based data, and the plurality of queue events into integration data having an enhanced integration format. A view integration component generates a plurality of data views from the integration data. A second edge node exposes the plurality of data views via an access services application programming interface (API). A new service execution component accesses the access services API to generate integration GUIs based on the data views.