Abstract: In some examples, a computing device may implement a method that includes receiving microservice profile information at a microservice profiler, performing lexical analysis of the microservice profile information (where the lexical analysis produces tokenized information), generating microservice modification information by performing machine learning analysis of one or more inputs (where the one or more inputs comprise the tokenized information), and outputting the microservice modification information from the microservice profiler. The microservice profile information describes one or more characteristics of a microservice. The lexical analysis is performed by a lexical analysis engine of the microservice profiler, and the machine learning analysis is performed by a machine learning system of the microservice profiler.

Abstract: A method may include receiving data from a device within a network, wherein the data is associated with one or more features of the device, and determining a subset of the features of the device that is associated with a runtime behavior of the device. The method may also perform a univariate analysis on a feature dataset that is associated with the subset of the features of the device, perform a multivariate analysis on the feature dataset that is associated with correlated features in the subset of the features, and generate a device signature based on the univariate analysis and the multivariate analysis.

Abstract: A method, system and computer-usable medium are disclosed for executing a comparative analysis of microservice profiles and recommending modifications to a microservice. In at least one embodiment, a plurality of microservice analysis daemons and a plurality of microservices are deployed to nodes of a network. Run-time execution characteristic of the plurality of microservices are monitored and gathered by the analysis daemons for microservices executed at the node at which the microservice analysis daemon is deployed. Microservice profiles are generated using the run-time execution characteristics data and data retrieved from a service registry. Recommendations regarding modification of one or more microservices are made based on the results of a microservice profile comparative analysis.

Abstract: In some examples, a server may determine a specification associated with a software module that is to be integrated with a software system. The specification identifies how the software module interacts with the software system. The server may execute a machine learning module to perform an analysis of the specification. The machine learning module may suggest at least one modification to at least a first portion of the specification and may automatically modify at least a second portion of the specification. The server may convert the specification to one or more application programming interface (API) calls and provide a system interface that includes the one or more API calls to enable the software module to interact with the software system. The API calls may include calls to a data integration API, a file transfer API, a messaging API, a database API, or any combination thereof.

Abstract: As an example, a server hosting a search engine may receive a search query and determine a searched time interval, a searched object, and a searched event. The server may select, based on the searched time interval, a portion of an object-event bipartite graph that was created using information gathered from social media sites. The server may compare attributes of individual events in the portion with attributes of the searched event to identify a set of relevant events. The server may determine objects associated with the relevant events and compare attributes of individual objects with the attributes of the searched object to identify a set of relevant objects. The search engine may provide search results that include the set of relevant objects ordered according to their similarity to the searched object.

Abstract: An information handling system receives historical data that includes configuration information and recovery values of recycled assets, and builds a training dataset from a subset of the historical data. The information handling system also builds a validation dataset from another subset of the historical data, and trains a machine learning model on the training dataset to learn the recovery values of the recycled assets. The system also validates the machine learning model based on the validation dataset, tunes a hyperparameter of the machine learning model, and predicts a recovery value of a recyclable asset using the machine learning model utilizing an extreme gradient boosting algorithm.

Abstract: A method may include receiving data from a device within a network, wherein the data is associated with one or more features of the device, and determining a subset of the features of the device that is associated with a runtime behavior of the device. The method may also perform a univariate analysis on a feature dataset that is associated with the subset of the features of the device, perform a multivariate analysis on the feature dataset that is associated with correlated features in the subset of the features, and generate a device signature based on the univariate analysis and the multivariate analysis.

Abstract: In some examples, a computing device may gather data associated with activities performed by individuals from multiple locations (e.g., code repositories). The computing device may determine data gathered by a data monitor application at individual locations of the multiple locations over a predetermined amount of time. The computing device may filter, based on criteria, the gathered data and perform an analysis of the filtered data using a machine learning algorithm (e.g., an artificial neural network and a logistic sigmoid). The criteria may be selected based at least in part on a job function associated with the particular individual. The machine learning algorithm may create a human resource evaluation of a particular individual of the plurality of individuals recommending an increase in salary, a bonus, or a promotion. The human resources evaluation may include a probability that the particular individual will leave a current job in the organization.

Abstract: An information handling system determines context features for an electronic mail message based on a payload of the electronic mail message. A processor adds the context features to the payload of the electronic mail message to create an updated payload. Based on the updated payload, the processor sets a quarantine indication for the electronic mail message to either a first state or a second state. In response to the quarantine indication for the electronic mail message being in the first state, the processor assigns the electronic mail message to an electronic mail storm. In response to the assigning of the electronic mail message being to the electronic mail storm, the processor quarantines the electronic mail message.

Abstract: A method, system and computer-usable medium are disclosed for executing a comparative analysis of microservice profiles and recommending modifications to a microservice. In at least one embodiment, a plurality of microservice analysis daemons and a plurality of microservices are deployed to nodes of a network. Run-time execution characteristic of the plurality of microservices are monitored and gathered by the analysis daemons for microservices executed at the node at which the microservice analysis daemon is deployed. Microservice profiles are generated using the run-time execution characteristics data and data retrieved from a service registry. Recommendations regarding modification of one or more microservices are made based on the results of a microservice profile comparative analysis.

Abstract: As an example, a server hosting a search engine may receive a search query and determine a searched time interval, a searched object, and a searched event. The server may select, based on the searched time interval, a portion of an object-event bipartite graph that was created using information gathered from social media sites. The server may compare attributes of individual events in the portion with attributes of the searched event to identify a set of relevant events. The server may determine objects associated with the relevant events and compare attributes of individual objects with the attributes of the searched object to identify a set of relevant objects. The search engine may provide search results that include the set of relevant objects ordered according to their similarity to the searched object.

Abstract: A system and method to programmatically generate an integration interface by executing a pre-defined, generic program code in place of the program code or instructions in an interface specification of a software module that is to be integrated with a larger software system. The generic program code functions as a plug-in code for the interface specification, thereby automatically converting the interface specification into the desired integration interface. The plug-in code is supplied with dynamic parameters/values obtained from the analysis of the interface specification, thereby generating a customized integration interface for the software module.

Abstract: In some examples, a computing device may implement a method that includes receiving microservice profile information at a microservice profiler, performing lexical analysis of the microservice profile information (where the lexical analysis produces tokenized information), generating microservice modification information by performing machine learning analysis of one or more inputs (where the one or more inputs comprise the tokenized information), and outputting the microservice modification information from the microservice profiler. The microservice profile information describes one or more characteristics of a microservice. The lexical analysis is performed by a lexical analysis engine of the microservice profiler, and the machine learning analysis is performed by a machine learning system of the microservice profiler.

Abstract: A system and method to programmatically generate an integration interface by executing a pre-defined, generic program code in place of the program code or instructions in an interface specification of a software module that is to be integrated with a larger software system. The generic program code functions as a plug-in code for the interface specification, thereby automatically converting the interface specification into the desired integration interface. The plug-in code is supplied with dynamic parameters/values obtained from the analysis of the interface specification, thereby generating a customized integration interface for the software module.

Abstract: An information handling system determines context features for an electronic mail message based on a payload of the electronic mail message. A processor adds the context features to the payload of the electronic mail message to create an updated payload. Based on the updated payload, the processor sets a quarantine indication for the electronic mail message to either a first state or a second state. In response to the quarantine indication for the electronic mail message being in the first state, the processor assigns the electronic mail message to an electronic mail storm. In response to the assigning of the electronic mail message being to the electronic mail storm, the processor quarantines the electronic mail message.

Abstract: In some examples, a server may determine a specification associated with a software module that is to be integrated with a software system. The specification identifies how the software module interacts with the software system. The server may execute a machine learning module to perform an analysis of the specification. The machine learning module may suggest at least one modification to at least a first portion of the specification and may automatically modify at least a second portion of the specification. The server may convert the specification to one or more application programming interface (API) calls and provide a system interface that includes the one or more API calls to enable the software module to interact with the software system. The API calls may include calls to a data integration API, a file transfer API, a messaging API, a database API, or any combination thereof.

Abstract: Disclosed herein are methods, systems, and processes to optimize message oriented middleware monitoring in heterogeneous computing environments. A message queue managed by a message queue manager for applications executing on a first computing system that is part of multiple heterogeneous computing systems is accessed. Data associated with message queues managed by the message queue manager for the applications is received from the first computing system. An output file that includes the data is generated.

Abstract: A method of providing a message queue service includes a first request being received by a computing device from an application to connect to a messaging queue manager provided by a messaging queue service that manages a messaging queue for at least one application. The application connects to the messaging queue manager by executing a single connect API command that causes the execution of a first plurality of commands in a messaging queue service API that provide for a connection of the application to the messaging queue manager. A second request is received from the application to open a message queue. The message queue managed by the messaging queue manager opens by executing a single open queue API command that causes the execution of a second plurality of commands that provide for the opening of the message queue.

Abstract: Disclosed herein are methods, systems, and processes to optimize message oriented middleware monitoring in heterogeneous computing environments. A message queue managed by a message queue manager for applications executing on a first computing system that is part of multiple heterogeneous computing systems is accessed. Data associated with message queues managed by the message queue manager for the applications is received from the first computing system. An output file that includes the data is generated.

Abstract: Systems and methods for intelligent ticket assignment through self-categorizing the problems and self-rating the analysts may include a client information handling system and a ticketing information handling system that may have a ticketing processor subsystem. The ticketing processor subsystem may receive a problem ticket that may include a problem and associated problem information from the client information handling system. The ticketing processor subsystem may also categorize the problem into a problem category of a learned problem profile. The ticketing processor subsystem may also perform a rank ordering algorithm to order learned analyst profiles of analysts that may have resolved one or more of the learned problems associated with the learned problem profile. The ticketing processor subsystem may also assign one of the analysts of the rank ordered learned analyst profiles to the problem ticket.