A control device for an electromechanical system includes at least one group of actuators, of which in each case one actuator is configured to be coupled to a mechanical and/or hydraulic unit and is configured to control an operation of the mechanical and/or hydraulic unit. The control device further includes at least one group of functional modules, which are implemented on at least one computing platform. The at least one group of functional modules includes a plurality of control modules, each respective control module being respectively assigned to and coupled in a communicative manner to a respective actuator, and a coordinating module communicatively coupled to the plurality of control modules. The coordinating module is designed to receive, from each respective control module of the plurality of control modules, fault messages with respect to an operating state of the associated mechanical and/or hydraulic unit and/or the associated actuator.

Information technology service management (ITSM) incident reports are converted from textual data to multiple vectors using an encoder and parameters are selected, where the parameters include a base cluster number and a threshold value. A base group of clusters is generated using an unsupervised machine learning clustering algorithm with the vectors and the parameters as input. A cluster quality score is computed for each of the base group of clusters. Each cluster from the base group of clusters with the cluster quality score above the threshold value is recursively split into new clusters until the cluster quality score for each cluster in the new clusters is below the threshold value. A final group of clusters is output, where each cluster from the final group of clusters represents ITSM incident reports related to a same problem.

Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

Systems and methods provide techniques for more effective and efficient predictive monitoring of a software application framework. In response, embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient predictive monitoring of a software application framework using incident signatures for the software application that are generated by using a natural language processing machine learning framework, a structured data processing machine learning model, and an incident severity level detection machine learning model.

An electronic apparatus includes an electronic apparatus includes: a communication interface; a memory storing at least one instruction; and a processor configured to execute the at least one instruction to: receive, from an external device through the communication interface, state information of the external device, execute a self-diagnosis mode to obtain error information of at least one of the electronic apparatus or the external device based on state information of the electronic apparatus and the state information of the external device, obtain setting information corresponding to at least one of the electronic apparatus or the external device based on the obtained error information, and control at least one of the electronic apparatus or the external device based on the obtained setting information.

An incident management system for enterprise operations is disclosed. The system 100 includes an operational details collection module 110, a data processing module 120, an operational details analysis module 130, an anomaly detection module 140 and an incident recognition module 150 including an incident cause analysis sub-module 155 and an incident cause description sub-module 160. The system 100 collects enterprise operational details from an operational database, analyzes huge volumes of logs, KPIs, traces, and IT asset relationships using proprietary machine learning techniques to identify one or more abnormal patterns, one or more hidden issues, one or more cross-domain performance issues, and one or more unusual system behaviors. Also, the system correlates, in real-time, with a huge volume of logs, KPIs, and IT system topologies to understand the relationship between different symptoms and problems at the machine's speed to arrive at a root cause and impacts. The system further understands the issues from a human recognition perspective using unique IT-specific natural language understanding techniques and generates a human-understandable text summary of the incident and root cause.

An apparatus, method, and computer program product are provided to detect error conditions and otherwise monitor the status of request data object and network response assets and related systems to allow for the efficient movement of network resources and other resources in high-volume network environments. In some example implementations, otherwise unrelated request data objects and their related parameters, along with otherwise unrelated network response asset systems are depicted on a single interface such that pairings between request data objects and network response assets, and other status information can be readily viewed. Some example implementations contemplate the use of location data in connection with error detection and remediation. Some example implementations also contemplate the establishment and use of a communication channel between an interface system and a system associated with a request data object and/or a network response asset upon the detection of an error condition.

Examples include aggregating logs, where each of the logs is associated with a workflow instance. Each log includes information indicative of an event occurring during the workflow instance. Further, examples include assigning, based on user intent of the workflow instance, a workflow name to each log, where the user intent is indicative of an outcome of execution of the workflow instance and assigning an instance identifier to each log, where the instance identifier corresponds to the workflow instance. Further, identifying a subset of the plurality of logs having an identical workflow name and an identical instance identifier, associating a tracking identifier to the subset, and creating an index of processed logs, wherein each processed log in the index includes the tracking identifier. Further, analyzing the index of processed logs based on a set of rules and identifying, based on the analysis, an error in execution of each the workflow instance.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for failure handling. This failure handling method includes determining a sector set failure type associated with at least one failed sector set of a disk; if the sector set failure type indicates that the number of failed sector sets in the at least one failed sector set is greater than a first threshold number, generating an instruction for replacing the disk; and otherwise performing at least one of the following: migrating data from a failed sector set in which the number of failed sectors is greater than a second threshold number to a spare sector set, and performing a failure recovery for a failed sector set in which the number of failed sectors is less than or equal to the second threshold number.

Decentralized cloud service assessment includes using a self-executing data structure, an error confirmation capsule (ECC) generated in response to a cloud service failure experienced by a cloud service client (CSC). One or more technical performance indicia corresponding to the cloud service failure are extracted from the ECC in response to the validating. The one or more technical performance indicia are compared to one or more electronically stored predefined performance norms of a cloud service provider (CSP) associated with the cloud service failure. Based on the comparing, a comparative ranking of the CSP is determined. A graphical user interface display is generated based on comparative rankings of the CSP and one or more other CSPs.