Abstract: A device may receive information identifying a plurality of requests and identifying a plurality of targets for the plurality of requests. The device may generate respective routes for the plurality of targets, where a route, of the respective routes, for a target, of the plurality of targets, identifies a set of transformations to be applied to a corresponding request of the plurality of requests. The device may apply the respective routes to the plurality of requests to generate processed requests, and may communicate with at least one of the plurality of targets based on the processed requests. The device may receive results based on communicating with the at least one of the plurality of targets, wherein the results are based on the processed requests, and may provide information based on the results.

Abstract: Disclosed embodiments provide systems and techniques for mass execution of analytical models across multiple dimensions of client, collateral, deal structure, third party, and other data relevant to predicting optimal decisions in real-time. In some embodiments, disclosed systems and techniques increase decisioning speed through the reduction of computational loads on disclosed decisioning systems. Further disclosed systems and techniques may scale-out analytical modeling computations through, among other technological solutions, advanced execution environments that are asynchronous and non-blocking in nature so as to allow the execution of a plurality of analytical models in parallel and optimizing the results.

Abstract: A device may receive information identifying a plurality of requests and identifying a plurality of targets for the plurality of requests. The device may generate respective routes for the plurality of targets, where a route, of the respective routes, for a target, of the plurality of targets, identifies a set of transformations to be applied to a corresponding request of the plurality of requests. The device may apply the respective routes to the plurality of requests to generate processed requests, and may communicate with at least one of the plurality of targets based on the processed requests. The device may receive results based on communicating with the at least one of the plurality of targets, wherein the results are based on the processed requests, and may provide information based on the results.

Abstract: A device may receive information identifying a plurality of requests and identifying a plurality of targets for the plurality of requests. The device may generate respective routes for the plurality of targets, where a route, of the respective routes, for a target, of the plurality of targets, identifies a set of transformations to be applied to a corresponding request of the plurality of requests. The device may apply the respective routes to the plurality of requests to generate processed requests, and may communicate with at least one of the plurality of targets based on the processed requests. The device may receive results based on communicating with the at least one of the plurality of targets, wherein the results are based on the processed requests, and may provide information based on the results.

Abstract: A device may receive information identifying a plurality of requests and identifying a plurality of targets for the plurality of requests. The device may generate respective routes for the plurality of targets, where a route, of the respective routes, for a target, of the plurality of targets, identifies a set of transformations to be applied to a corresponding request of the plurality of requests. The device may apply the respective routes to the plurality of requests to generate processed requests, and may communicate with at least one of the plurality of targets based on the processed requests. The device may receive results based on communicating with the at least one of the plurality of targets, wherein the results are based on the processed requests, and may provide information based on the results.

Abstract: Disclosed embodiments provide systems and techniques for mass execution of analytical models across multiple dimensions of client, collateral, deal structure, third party, and other data relevant to predicting optimal decisions in real-time. In some embodiments, disclosed systems and techniques increase decisioning speed through the reduction of computational loads on disclosed decisioning systems. Further disclosed systems and techniques may scale-out analytical modeling computations through, among other technological solutions, advanced execution environments that are asynchronous and non-blocking in nature so as to allow the execution of a plurality of analytical models in parallel and optimizing the results.

Abstract: A device may receive information identifying a plurality of requests and identifying a plurality of targets for the plurality of requests. The device may generate respective routes for the plurality of targets, where a route, of the respective routes, for a target, of the plurality of targets, identifies a set of transformations to be applied to a corresponding request of the plurality of requests. The device may apply the respective routes to the plurality of requests to generate processed requests, and may communicate with at least one of the plurality of targets based on the processed requests. The device may receive results based on communicating with the at least one of the plurality of targets, wherein the results are based on the processed requests, and may provide information based on the results.

Abstract: A device may receive a message from a first different device that includes an identifier and may determine whether the device has previously received the message. The device may generate, based on determining that the device has not previously received the message, an additional identifier and a message status indicator and may cause the additional identifier, the message, and the message status indicator to be stored in a data structure associated with the device. The device may send an acknowledgment status to the first different device and may cause processing of the message to generate an additional message that includes the additional identifier. The device may send the additional message to a second different device and may cause, after sending the additional message to the second different device, the message status indicator in the data structure to be updated.

Abstract: Disclosed embodiments provide systems and techniques for mass execution of analytical models across multiple dimensions of client, collateral, deal structure, third party, and other data relevant to predicting optimal decisions in real-time. In some embodiments, disclosed systems and techniques increase decisioning speed through the reduction of computational loads on disclosed decisioning systems. Further disclosed systems and techniques may scale-out analytical modeling computations through, among other technological solutions, advanced execution environments that are asynchronous and non-blocking in nature so as to allow the execution of a plurality of analytical models in parallel and optimizing the results.

Abstract: A device may receive a message from a first different device that includes an identifier and may determine whether the device has previously received the message. The device may generate, based on determining that the device has not previously received the message, an additional identifier and a message status indicator and may cause the additional identifier, the message, and the message status indicator to be stored in a data structure associated with the device. The device may send an acknowledgment status to the first different device and may cause processing of the message to generate an additional message that includes the additional identifier. The device may send the additional message to a second different device and may cause, after sending the additional message to the second different device, the message status indicator in the data structure to be updated.

Abstract: A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of a method for generating a data tree structure for expediting rule-based decision-making. The system may receive a rules file having a plurality of rules, where each rule has one or more conditions and one or more actions. The system may generate a plurality of condition frequency counts that represent the total number of rules having a condition of a particular unique condition type. The system may then generate a tree structure based on the plurality of rules and the plurality of condition frequency counts, where the tree structure includes a root node and one or more node branches, where each node branch includes a first-level node and zero or more second-level nodes that each include a node condition and zero or more node actions.

Abstract: A device may receive a message from a first different device that includes an identifier and may determine whether the device has previously received the message. The device may generate, based on determining that the device has not previously received the message, an additional identifier and a message status indicator and may cause the additional identifier, the message, and the message status indicator to be stored in a data structure associated with the device. The device may send an acknowledgment status to the first different device and may cause processing of the message to generate an additional message that includes the additional identifier. The device may send the additional message to a second different device and may cause, after sending the additional message to the second different device, the message status indicator in the data structure to be updated.

Abstract: A device may receive information identifying a plurality of requests and identifying a plurality of targets for the plurality of requests. The device may generate respective routes for the plurality of targets, where a route, of the respective routes, for a target, of the plurality of targets, identifies a set of transformations to be applied to a corresponding request of the plurality of requests. The device may apply the respective routes to the plurality of requests to generate processed requests, and may communicate with at least one of the plurality of targets based on the processed requests. The device may receive results based on communicating with the at least one of the plurality of targets, wherein the results are based on the processed requests, and may provide information based on the results.

Abstract: A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of a method for generating a data tree structure for expediting rule-based decision-making. The system may receive a rules file having a plurality of rules, where each rule has one or more conditions and one or more actions. The system may generate a plurality of condition frequency counts that represent the total number of rules having a condition of a particular unique condition type. The system may then generate a tree structure based on the plurality of rules and the plurality of condition frequency counts, where the tree structure includes a root node and one or more node branches, where each node branch includes a first-level node and zero or more second-level nodes that each include a node condition and zero or more node actions.

Abstract: A device may receive information identifying a plurality of requests and identifying a plurality of targets for the plurality of requests. The device may generate respective routes for the plurality of targets, where a route, of the respective routes, for a target, of the plurality of targets, identifies a set of transformations to be applied to a corresponding request of the plurality of requests. The device may apply the respective routes to the plurality of requests to generate processed requests, and may communicate with at least one of the plurality of targets based on the processed requests. The device may receive results based on communicating with the at least one of the plurality of targets, wherein the results are based on the processed requests, and may provide information based on the results.

Abstract: Described are techniques for performing compliance testing. Configuration state information is received for one or more devices managed using a configuration management system. The configuration state information is stored in a data container. One or more compliance tests are executed using the configuration state information to determine if the one or more devices are compliant. The one or more compliance tests are defined using one or more queries and one or more compliance rules. The one or more queries are used for retrieving a portion of said configuration state information from said data container and generating one or more result sets. The one or more compliance rules specifying compliance criteria are used for determining whether the one or more result sets include configuration state information which is compliant.

Abstract: Disclosed embodiments provide systems and techniques for mass execution of analytical models across multiple dimensions of client, collateral, deal structure, third party, and other data relevant to predicting optimal decisions in real-time. In some embodiments, disclosed systems and techniques increase decisioning speed through the reduction of computational loads on disclosed decisioning systems. Further disclosed systems and techniques may scale-out analytical modeling computations through, among other technological solutions, advanced execution environments that are asynchronous and non-blocking in nature so as to allow the execution of a plurality of analytical models in parallel and optimizing the results.

Abstract: A system includes one or more memory devices storing instructions, and one or more processors configured to execute the instructions to perform steps of a method for generating a data tree structure for expediting rule-based decision-making. The system may receive a rules file having a plurality of rules, where each rule has one or more conditions and one or more actions. The system may generate a plurality of condition frequency counts that represent the total number of rules having a condition of a particular unique condition type. The system may then generate a tree structure based on the plurality of rules and the plurality of condition frequency counts, where the tree structure includes a root node and one or more node branches, where each node branch includes a first-level node and zero or more second-level nodes that each include a node condition and zero or more node actions.

Abstract: Disclosed embodiments provide systems and techniques for mass execution of analytical models across multiple dimensions of client, collateral, deal structure, third party, and other data relevant to predicting optimal decisions in real-time. In some embodiments, disclosed systems and techniques increase decisioning speed through the reduction of computational loads on disclosed decisioning systems. Further disclosed systems and techniques may scale-out analytical modeling computations through, among other technological solutions, advanced execution environments that are asynchronous and non-blocking in nature so as to allow the execution of a plurality of analytical models in parallel and optimizing the results.

Abstract: Disclosed embodiments provide systems and techniques for mass execution of analytical models across multiple dimensions of client, collateral, deal structure, third party, and other data relevant to predicting optimal decisions in real-time. In some embodiments, disclosed systems and techniques increase decisioning speed through the reduction of computational loads on disclosed decisioning systems. Further disclosed systems and techniques may scale-out analytical modeling computations through, among other technological solutions, advanced execution environments that are asynchronous and non-blocking in nature so as to allow the execution of a plurality of analytical models in parallel and optimizing the results.