Abstract: A device may receive data that is related to historical reports associated with an organization, historical audits of the historical reports, and individuals associated with the historical reports. The device may determine a multi-entity profile for the data. The multi-entity profile may include a set of groupings of the data by a set of attributes included in the data. The device may determine, using the multi-entity profile, a set of supervised model features for the historical reports. The device may determine, using the multi-entity profile, a set of unsupervised model features for the historical reports independent of the historical audits. The device may determine, utilizing a model, a score for a report. The device may perform one or more actions.

Abstract: A device may receive a computing resource request. The computing resource request may be related to allocating computing resources for a job. The device may process the computing resource request to identify a set of parameters related to the computing resource request or to the job. The set of parameters may be used to determine an allocation of the computing resources for the job. The device may utilize multiple machine learning models to process data related to the set of parameters identified in the computing resource request. The device may determine the allocation of the computing resources for the job based on utilizing the multiple machine learning models to process the data. The device may generate a set of scripts related to causing the computing resources to be allocated for the job according to the allocation. The device may perform a set of actions based on the set of scripts.

Abstract: A device may receive data that includes invoice data related to historical invoices from an organization, contact data related to historical contacts between the organization and various entities, and dispute data related to historical disputes between the organization and the various entities. The device may determine a profile for the data. The device may determine a set of supervised learning models for the historical invoices based on one or more of the historical contacts, the historical disputes, the historical invoices, or historical patterns related to the historical invoices. The device may determine, using the profile, a set of unsupervised learning models for the historical invoices independent of the one or more of the historical contacts, the historical disputes, or the historical patterns. The device may determine, utilizing a super model, a prediction for the invoice after the super model is trained. The device may perform one or more actions.

Abstract: A device may receive invoice data related to multiple invoices, requisition data related to multiple requisitions, or project data related to multiple projects. The device may process the data using a feature extraction engine to identify features of the data. The device may process the data using a transformation engine to reduce a size of the data. The device may process the data using a set of machine learning models. The device may generate a set of recommendations related to at least one of: categorizing each of the multiple invoices, each of the multiple requisitions, or each of the multiple projects into one or more of multiple categories, identifying a set of possible suppliers for each of the multiple requisitions or each of the multiple projects, or identifying a set of similar projects for each of the multiple projects. The device may perform one or more actions.

Abstract: A device may receive, from multiple systems, data related to an individual. The device may anonymize, after receiving the data and using an anonymization technique, information included in the data that identifies the individual. The device may apply a formatting to the data after anonymizing the information that identifies the individual. The device may identify, after applying the formatting to the data, historical data related to the individual, to a provider associated with a claim for care, or to historical claims, and population data associated with demographics of the individual. The device may process, in association with identifying the historical data and the population data, the data using a machine learning model. The machine learning model may be associated with generating a prediction related to the individual or the care provided to the individual. The device may perform one or more actions based on the prediction.

Abstract: A device may receive utility usage data for multiple buildings across multiple locations. The device may process the utility usage data using a first set of models associated with performing at least one of: an intra-building anomaly detection for the utility usage data, a first grouping of the utility usage data based on characteristics of the utility usage data, or a second grouping of the utility usage data based on the multiple locations. The device may process first output from the first set of models using a second set of models associated with pre-processing the first output in association with identifying anomalies in the first grouping or in the second grouping. The device may process the first output and second output from the second set of models using a super model associated with identifying the anomalies. The device may perform, based on the score, one or more actions.

Abstract: A device may receive data that includes invoice data related to historical invoices from an organization, contact data related to historical contacts between the organization and various entities, and dispute data related to historical disputes between the organization and the various entities. The device may determine a profile for the data. The device may determine a set of supervised learning models for the historical invoices based on one or more of the historical contacts, the historical disputes, the historical invoices, or historical patterns related to the historical invoices. The device may determine, using the profile, a set of unsupervised learning models for the historical invoices independent of the one or more of the historical contacts, the historical disputes, or the historical patterns. The device may determine, utilizing a super model, a prediction for the invoice after the super model is trained. The device may perform one or more actions.

Abstract: A device may receive a computing resource request. The computing resource request may be related to allocating computing resources for a job. The device may process the computing resource request to identify a set of parameters related to the computing resource request or to the job. The set of parameters may be used to determine an allocation of the computing resources for the job. The device may utilize multiple machine learning models to process data related to the set of parameters identified in the computing resource request. The device may determine the allocation of the computing resources for the job based on utilizing the multiple machine learning models to process the data. The device may generate a set of scripts related to causing the computing resources to be allocated for the job according to the allocation. The device may perform a set of actions based on the set of scripts.

Abstract: A device may receive data that is related to historical reports associated with an organization, historical audits of the historical reports, and individuals associated with the historical reports. The device may determine a multi-entity profile for the data. The multi-entity profile may include a set of groupings of the data by a set of attributes included in the data. The device may determine, using the multi-entity profile, a set of supervised model features for the historical reports. The device may determine, using the multi-entity profile, a set of unsupervised model features for the historical reports independent of the historical audits. The device may determine, utilizing a model, a score for a report. The device may perform one or more actions.

Abstract: A device may receive a computing resource request. The computing resource request may be related to allocating computing resources for a job. The device may process the computing resource request to identify a set of parameters related to the computing resource request or to the job. The set of parameters may be used to determine an allocation of the computing resources for the job. The device may utilize multiple machine learning models to process data related to the set of parameters identified in the computing resource request. The device may determine the allocation of the computing resources for the job based on utilizing the multiple machine learning models to process the data. The device may generate a set of scripts related to causing the computing resources to be allocated for the job according to the allocation. The device may perform a set of actions based on the set of scripts.

Abstract: Embodiments of the present invention address deficiencies of the art in respect to data synchronization and provide a novel and non-obvious method, system and computer program product for synchronization log driven data synchronization. In one embodiment of the invention, a data synchronization method can be provided to include assembling a group of initial updates for synchronization, consulting a synchronization log of updates excluded from past synchronizations to determine updates already applied during past synchronizations, removing updates already applied during past synchronizations from a filtered set of updates, and synchronizing the filtered set of updates. In this regard, assembling a group of initial updates for synchronization can include assembling a group of initial updates having a timestamp greater than a timestamp for an immediate past synchronization.

Abstract: A solution descriptor comprises a set of component workload units, a workload unit describing a deployable application component with application binary, configuration parameters and dependency declarations. An environment descriptor specifies a set of target platforms and plugins in an execution environment. A deployer interprets the solution descriptor and the environment descriptor, and generates a list of tuples comprising compatible workload-plugin-platform combinations. The deployer determines an execution order for the list of tuples, and invokes the plugins in the list of tuples in the execution order, wherein each of the plugins executes a corresponding compatible workload on a corresponding compatible target platform specified in the list of tuples.

Abstract: A solution descriptor comprises a set of component workload units, a workload unit describing a deployable application component with application binary, configuration parameters and dependency declarations. An environment descriptor specifies a set of target platforms and plugins in an execution environment. A deployer interprets the solution descriptor and the environment descriptor, and generates a list of tuples comprising compatible workload-plugin-platform combinations. The deployer determines an execution order for the list of tuples, and invokes the plugins in the list of tuples in the execution order, wherein each of the plugins executes a corresponding compatible workload on a corresponding compatible target platform specified in the list of tuples.

Abstract: A solution descriptor comprises a set of component workload units, a workload unit describing a deployable application component with application binary, configuration parameters and dependency declarations. An environment descriptor specifies a set of target platforms and plugins in an execution environment. A deployer interprets the solution descriptor and the environment descriptor, and generates a list of tuples comprising compatible workload-plugin-platform combinations. The deployer determines an execution order for the list of tuples, and invokes the plugins in the list of tuples in the execution order, wherein each of the plugins executes a corresponding compatible workload on a corresponding compatible target platform specified in the list of tuples.

Abstract: A solution descriptor comprises a set of component workload units, a workload unit describing a deployable application component with application binary, configuration parameters and dependency declarations. An environment descriptor specifies a set of target platforms and plugins in an execution environment. A deployer interprets the solution descriptor and the environment descriptor, and generates a list of tuples comprising compatible workload-plugin-platform combinations. The deployer determines an execution order for the list of tuples, and invokes the plugins in the list of tuples in the execution order, wherein each of the plugins executes a corresponding compatible workload on a corresponding compatible target platform specified in the list of tuples.

Abstract: A method of operating a computer system is disclosed. An eCommerce authentication request is received from a merchant node. The eCommerce authentication request has content including cardholder information. A risk score for the eCommerce authentication request is generated based on comparison of the cardholder information of the eCommerce authentication request to cardholder information of eCommerce authentication requests of a plurality of merchant nodes. The eCommerce authentication request is selectively provided to an authentication node based on the risk score.

Abstract: A method of operating a computer system is disclosed. An eCommerce authentication request is received from a merchant node. The eCommerce authentication request has content including merchant information. A risk score for the eCommerce authentication request is generated based on comparison of the merchant information of the eCommerce authentication request to merchant information of eCommerce authentication requests of a plurality of merchant nodes. The eCommerce authentication request is selectively provided to an authentication node based on the risk score.

Abstract: A method of operating a computer system is disclosed. An eCommerce authentication request is received. Content of the eCommerce authentication request is processed through a non-linear analytical model to generate a risk score. The eCommerce authentication request is selectively provided to an authentication node based on the risk score. Related authentication gateway nodes and computer program products are disclosed.

Abstract: A solution descriptor comprises a set of component workload units, a workload unit describing a deployable application component with application binary, configuration parameters and dependency declarations. An environment descriptor specifies a set of target platforms and plugins in an execution environment. A deployer interprets the solution descriptor and the environment descriptor, and generates a list of tuples comprising compatible workload-plugin-platform combinations. The deployer determines an execution order for the list of tuples, and invokes the plugins in the list of tuples in the execution order, wherein each of the plugins executes a corresponding compatible workload on a corresponding compatible target platform specified in the list of tuples.

Abstract: A solution descriptor comprises a set of component workload units, a workload unit describing a deployable application component with application binary, configuration parameters and dependency declarations. An environment descriptor specifies a set of target platforms and plugins in an execution environment. A deployer interprets the solution descriptor and the environment descriptor, and generates a list of tuples comprising compatible workload-plugin-platform combinations. The deployer determines an execution order for the list of tuples, and invokes the plugins in the list of tuples in the execution order, wherein each of the plugins executes a corresponding compatible workload on a corresponding compatible target platform specified in the list of tuples.