A self-managing database system includes a metrics collector to collect metrics data from one or more databases of a computing system and an anomaly detector to analyze the metrics data and detect one or more anomalies. The system includes a causal inference engine to mark one or more nodes in a knowledge representation corresponding to the metrics data for the one or more anomalies and to determine a root cause with a highest probability of causing the one or more anomalies using the knowledge representation. The system includes a self-healing engine, to take at least one remedial action for the one or more databases in response to determination of the root cause.

Aspects of the disclosure relate to automated error processing. A computing platform may receive historical error/solution information. The computing platform may train, using the historical error/solution information, an artificial intelligence engine to automatically identify solutions for current errors for a plurality of users. The computing platform may identify current errors for a user of the plurality of users. The computing platform may notify the user of the current errors. The computing platform may receive a request to correct an error of the one or more current errors. The computing platform may identify, using the artificial intelligence engine, a solution to the error. The computing platform may automatically perform actions to achieve the solution. The computing platform may send, after performing the actions, commands directing an event processing system to process an event with which the error was associated, which may cause the event processing system to process the event.

A method and apparatus are described. The method includes receiving a set of data streams including data values generated by a sensor associated with the operation of a component in a system at points in time and generating an anomaly data value for the received data values. The method further includes applying a machine learning algorithm to the received data values and a subset of data values previously received to generate expected data values at points in time beyond the current point in time, generating an expected anomaly data value for each of the expected data values, and identifying an operational anomaly for the component at a point in time beyond the current time based on the expected anomaly data value. The apparatus includes an input interface for receiving the data streams and a processor for processing the received data values to identify an operational anomaly as described above.

An interface receives reported information from a system on chip (SOC), where the reported information includes: (1) hardware-reported information that is reported by a hardware functional module included in the SOC and (2) firmware-reported information that is reported by a firmware functional module included in the SOC. A processor receives one or more display settings and generates visual information based at least in part on: (1) the one or more display settings, (2) the hardware-reported information, and (3) the firmware-reported information. The visual information is displayed via a display.

Event records from multiple computing devices are received at a managing unit. Individual event records include an event identifier field including an event identifier identifying a first event associated with a particular computing device, a parent event identifier field identifying a parent event that initialized the first event, and an entity identifier field including an entity identifier identifying the particular computing device. The managing unit generates log records associated with event identifiers included in the event records. The log records include state fields indicating a state of a particular event associated with a particular event identifier. Based on a correlation of the event and log records, the managing unit determines at least two computing devices associated with events resulting in an error state, and identifies parent events that initialized the events with errors. The managing unit generates a report linking the parent events to the events having an error state.

Systems and methods are provided that may be implemented to perform failure analysis and/or self-healing of information handling system storage. In one example, an information handling system may perform self-recovery actions to self-heal system storage issues when there is a OS boot failure due to a failure to detect a system storage drive by determining one or more possible recovery actions based on a current system storage drive status retrieved by an embedded controller (EC) or other programmable integrated circuit of the information handling system. In another example, manufacturing quality control analysis may be performed on boot failure information that is collected at a remote server from multiple failed information handling systems.

A system receives real-time log messages from an executing process that experiences a runtime error. Information such as a filename and line number for the underlying source code may be embedded in the log messages using compiler macros. When the log messages are received, a developer URL may be generated that links a developer workstation directly to the underlying source code file and line number in a source code repository. A support URL may also be generated with a link to a support center and an embedded search string that retrieves resources that are known to address the process error.

In some examples, a system comprises a network interface; a storage device comprising machine-readable instructions; and a processor coupled to the network interface, the processor to access the storage device, wherein execution of the machine-readable instructions causes the processor to: collect crash event data; categorize the crash event data by an application executing when the crash event occurred; identify a crash event corresponding to the crash event data; create an identifier for the crash event; compare the identifier of the crash event to a list of work items, wherein each work item has an identifier; and update the list of work items based on the comparison.

A method of monitoring a closed system, an apparatus thereof and a monitoring device are provided. The method of monitoring the closed system includes: performing a page capturing on a web page of the closed system; searching from a captured page, according to configuration information of data to be monitored of the closed system, a text content corresponding to the data to be monitored; and converting the text content corresponding to the data to be monitored into monitored data which a system monitoring platform is capable of recognizing, and storing the monitored data.

A failure diagnosis device includes a communication unit, a data comparison unit, and an eligibility determination unit. The communication unit acquires first physical quantity data and second physical quantity data of a type different from that of the first physical quantity data that are used for performing failure diagnosis of a home appliance acquired by a sensor unit, and first control information related to the second physical quantity data acquired by the home appliance. The data comparison unit compares the second physical quantity data with the first control information. The eligibility determination unit determines whether or not the first physical quantity data is eligible as data used for the failure diagnosis based on a comparison result obtained by the data comparison unit.