A technique for logging tasks in a computer system. Tasks are logged into the computer system by executing at least one task at the computer system. A set of log metadata and a set of log levels of log records of the at least one task may be determined from an execution log output of the at least one task. Execution outputs of a further task may be determined according to the determined set of log metadata and associated set of log levels.

This application relates to apparatus and methods for the monitoring of nodes within datacenters. In some examples, a computing device, such as a node, receives a monitoring file from a monitoring server, where the monitoring file includes a plurality of node health checks. The computing device is configured to execute the monitoring file based on a type of the computing device. Further, and based on the execution of the monitoring file, the computing device is configured to determine that at least one of the plurality of node health checks failed. In response to determining that the at least one of the plurality of node health checks failed, the computing device is configured to generate an alert message identifying the node health checks that failed. Further, the computing device is configured to transmit the alert message to the monitoring server for display.

An Artificial Intelligence (AI)-based automated process is monitored via a process monitoring system that identifies components used in the execution of the sub-processes of the automated process. Various metrics are selected for collection prior to or during the execution of the AI-based automated process. The values of the metrics are collected as step outputs corresponding to the sub-processes. A final output generated upon the execution of the automated process is also collected. The step outputs can be used to determine the reason why the automated process produced a certain final output.

A method and a device for a reaction-free and integrity-protected synchronization of log data between at least one first network and a second network is provided. The log data is copied by means of a monitoring device upon being transmitted from devices to a first log server in the first network. Metadata of the log data is additionally generated in a first managing unit, the metadata including time information, integrity information, origin information, and/or completeness information. The copied log data and the corresponding metadata are transmitted to the second network via a unidirectional coupling unit in a reaction-free manner. The lot data is checked and ordered chronologically in the second network using the metadata. Thus, a synchronized copy of the log data from the first network is promptly provided in the second network.

A selected location of a design view of a graphical user interface (GUI) generated using a design application may be received and used to identify a corresponding location for the selected location within an implementation view of the GUI. By mapping code of the GUI for the corresponding location to the selected location, using a validation language that relates the code to the design language, a difference between the selected location and the corresponding location may be identified. In this way, design validation may occur in a fast, automated, and reliable manner.

A technique for monitoring a computing infrastructure having one or more target devices includes receiving, from a plurality of evaluation services, evaluation results of one or more target devices. The technique further includes extracting, using a different data collector for each of the plurality of evaluation services, data from each of the evaluation results. The technique further includes converting the extracted data to a common format, determining whether an issue or a vulnerability is present in the one or more target devices based on the extracted and converted data, and reporting the issue or the vulnerability.

Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. Machine learning-based classification can be performed to classify logs. This approach is used to group logs automatically using a machine learning infrastructure.

A computer-based system and method for real-time monitoring computer resource usage, including obtaining, by a monitoring application executed by a processor, from a plurality of applications, each application executed by a processor, a report upon the accessing of at least one accessed resource by at least one accessing user; and generating, by the monitoring application based on the report, a map of resources accessed by the plurality of applications. If a notification that a resource has been compromised is obtained, a list of all applications that have accessed the resource may be generated based on the map.

Various embodiments for providing a user interface test and validation system are described herein. An embodiment operates by determining user interface (UI) settings and metadata, and fetching data corresponding to the metadata. A plurality of test procedures corresponding to the UI element are identified. A plurality of states to be present in a rendering of the UI element based on the plurality of test procedures, the metadata, and the data are identified. A rendering of the UI element is validated against the plurality of states. A test report is provided based on the validating.

A method of block chain based data protection may comprise receiving a user block chain instruction to record a address-identified memory location at which a dataset field value containing sensitive personal information is stored pursuant to a customized data integration process modeled via a graphical user interface, creating a block chain associated with the dataset field value, receiving an identification of the address-identified memory location from a customized data integration process remote execution location, and creating a first block storing the identification of the address-identified memory location within the block chain. The method may further comprise receiving a user deletion instruction to delete the dataset field value from the address-identified memory location, automatically generating a runtime engine and machine executable deletion code instructions for deletion of the dataset field value from the address-identified memory location, and transmitting the runtime engine and the machine executable deletion code instructions for execution at the remote execution location.