Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.

Abstract: In some embodiments, a request associated with a user account may be obtained, where the request includes a restriction associated with the user account. Based on the restriction included in the request, a graphic indicium (that encodes the restriction included in the request) may be generated. The graphic indicum may be sent to the client device to facilitate a transaction using the graphic indicium. In some embodiments, information associated with the graphic indicium and a request to authorize the transaction may be obtained, and the restriction may be identified based on the information associated with the graphic indicium. The transaction may be authorized based on a determination that the transaction satisfies the restriction.

Abstract: Aspects described herein may relate to a transaction exchange platform using a streaming data platform (SDP) and microservices to process transactions in accordance with corresponding workflows. The transaction exchange platform may receive transactions from origination sources, which may be added to the SDP as transaction objects. Microservices on the transaction exchange platform may interact with the transaction objects based on configured workflows associated with the transactions. Further, the microservices may leverage machine-learning models to determine whether transaction objects may be more effectively processed using alternative or secondary workflows. Processing on the transaction exchange platform may facilitate clearing and settlement of transactions. Some aspects may provide for dynamic and flexible reconfiguration of workflows.

Abstract: Aspects described herein may relate to a transaction exchange platform using a streaming data platform (SDP) and microservices to process transactions in accordance with corresponding workflows. The transaction exchange platform may receive transactions from origination sources, which may be added to the SDP as transaction objects. Microservices on the transaction exchange platform may interact with the transaction objects based on configured workflows associated with the transactions. Further, the microservices may leverage machine-learning models to determine whether transaction objects may be more effectively processed using alternative or secondary workflows. Processing on the transaction exchange platform may facilitate clearing and settlement of transactions. Some aspects may provide for dynamic and flexible reconfiguration of workflows.

Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.

Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.

Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.

Abstract: Various embodiments are generally directed to techniques for network strengthening, such as by detecting issues with one or more network components and reconfiguring one or more upstream or downstream network components to preempt issues with the one or more upstream or downstream network components, for instance. Some embodiments are particularly directed to a tool (e.g., strengthening agent) that implements pre-scripted or dynamic hardening of up and downstream dependencies of a network component in response to an issue identified with the network component. In many embodiments, up and downstream components of a network component may be reconfigured while the issue with the network component is being addressed to preempt issues with the up and downstream components.

Abstract: Techniques to utilize excess resources in a cloud system, such as by enabling an auxiliary resource utilizer to use resources while they are not needed to support primary resource utilizers, are described herein. Some embodiments are directed to identifying and allocating excess capacity of resources in a cloud system to auxiliary resource utilizers based on one or more policies. In various embodiments, excess resources in one or more of the set of resources in the cloud system, or cloud resources, may be determined based on monitoring utilization of the cloud resources by the primary resource utilizers. In many embodiments, an auxiliary resource utilizer that is in compliance with a set of utilization policies may be identified and the excess resources may be allocated to the auxiliary resource utilizer.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for transition-coded media, measuring engagement of transition-coded media, and distribution of components of transition-coded media. For instance, the method may include: obtaining a media file; determining whether the media file includes transition-coded media; in response to determining the media file includes the transition-coded media, setting up a dynamic media environment; detecting whether a user is interacting with the transition-coded media; and in response to detecting the user is interacting with the transition-coded media, executing the transition-coded media with the dynamic media environment.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for transition-coded media, measuring engagement of transition-coded media, and distribution of components of transition-coded media. For instance, the method may include: obtaining a media file; determining whether the media file includes transition-coded media; in response to determining the media file includes the transition-coded media, setting up a dynamic media environment; detecting whether a user is interacting with the transition-coded media; and in response to detecting the user is interacting with the transition-coded media, executing the transition-coded media with the dynamic media environment.

Abstract: Systems, methods, articles of manufacture, and computer program products for scalable subscriptions for virtual collaborative workspaces. An indication of a process event associated with a computer-implemented process may be received. A population of subscribers subscribed to at least one property of one of the process event or the computer-implemented process may be determined. A virtual collaborative workspace for the process event may be generated, the virtual collaborative workspace operative to present context information associated with the process event to a plurality of subscribers. A message may be sent to the population of subscribers indicating the process event, the message comprising a link to access the virtual collaborative workspace. The virtual collaborative workspace may be generated for display responsive to selection of the link by the at least one of the population of subscribers.

Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for transition-coded media, measuring engagement of transition-coded media, and distribution of components of transition-coded media. For instance, the method may include: obtaining a media file; determining whether the media file includes transition-coded media; in response to determining the media file includes the transition-coded media, setting up a dynamic media environment; detecting whether a user is interacting with the transition-coded media; and in response to detecting the user is interacting with the transition-coded media, executing the transition-coded media with the dynamic media environment.

Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.

Abstract: Aspects described herein may relate to a transaction exchange platform using a streaming data platform (SDP) and microservices to process transactions according to review and approval workflows. The transaction exchange platform may receive transactions from origination sources, which may be added to the SDP as transaction objects. As the transactions are received, the transactions may be analyzed to detect duplicate transactions and/or errors in the transactions. The transaction exchange platform may take steps to remediate transactions that are recognized as duplicates or predicted to generate one or more errors. Similarly, the transaction exchange platform may take steps to remediate transactions that are rejected by a clearinghouse.

Abstract: Various embodiments are generally directed to techniques for network strengthening, such as by detecting issues with one or more network components and reconfiguring one or more upstream or downstream network components to preempt issues with the one or more upstream or downstream network components, for instance. Some embodiments are particularly directed to a tool (e.g., strengthening agent) that implements pre-scripted or dynamic hardening of up and downstream dependencies of a network component in response to an issue identified with the network component. In many embodiments, up and downstream components of a network component may be reconfigured while the issue with the network component is being addressed to preempt issues with the up and downstream components.

Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.

Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.

Abstract: Aspects described herein may relate to a transaction exchange platform using a streaming data platform (SDP) and microservices to process transactions according to review and approval workflows. The transaction exchange platform may receive transactions from origination sources, which may be added to the SDP as transaction objects. As the transactions are received, the transactions may be analyzed to detect duplicate transactions and/or errors in the transactions. The transaction exchange platform may take steps to remediate transactions that are recognized as duplicates or predicted to generate one or more errors. Similarly, the transaction exchange platform may take steps to remediate transactions that are rejected by a clearinghouse.