Methods and systems are disclosed herein for using anomaly detection in timeseries data of user sentiment to detect incidents in computing systems and identify events within an enterprise. An anomaly detection system may receive social media messages that include a timestamp indicating when each message was published. The system may generate sentiment identifiers for the social media messages. The sentiment identifiers and timestamps associated with the social media messages may be used to generate a timeseries dataset for each type of sentiment identifier. The timeseries datasets may be input into an anomaly detection model to determine whether an anomaly has occurred. The system may retrieve textual data from the social media messages associated with the detected anomaly and may use the text to determine a computing system or event associated with the detected anomaly.

Described are techniques including a computer-implemented method of determining, by a service processor, that a first set of callouts of a first error log matches a previous set of callouts of a previous error log. The method further comprises combining the first error log with the previous error log in a first group in a service processor log of the service processor. The method further comprises transmitting information related to the first group to a management console communicatively coupled to the service processor.

The present invention provides a explicitly defined method for implementing some of reliabilities for a computing system under development comprising FMEA, systematic error detection said method is based on the exclusive disclosure that is: a computing system functionalities can be fully represented by the data comprising Input Data, Middle Data and Output Data, in which the Output Data represent fully the system functionalities under the input data from the system black-box point of view, the Middle Data represent fully the middle functionalities that are transporting and transforming the Input Data to the Output Data. So, the development activities that are against the functionalities, such as FMEA, systematic error detection will be complete, consistent, accurate and efficient if they are applied only for the data.

A Peripheral Component Interconnect Express (PCIe) interface device coupled to an external device through a link including a plurality of lanes according to the present disclosure includes an EQ controller controlling the PCIe interface device to perform an equalization operation for determining a transmitter or receiver setting of each of the plurality of lanes, and an EQ information storage storing log information indicating a number of equalization operation attempts with respect to each of a plurality of EQ coefficients and storing error information about an error occurring in an LO state with respect to each of the plurality of EQ coefficients, which includes a transmitter coefficient or a receiver coefficient, wherein the EQ controller determines a final EQ coefficient using the log information and the error information.

Systems and methods include monitoring of one or more software applications to determine a value of a first metric associated with instances of a first process, the first process including steps executed by the one or more software applications, determination that the value of the first metric has exceeded a threshold associated with the first process in a first number of ongoing instances of the first process, determination that the first number is greater than a first count limit associated with the first process, and, in response to the determination that the first number is greater than the first count limit, sending of an error message to a user associated with each of the ongoing instances of the first process.

In an ultrasound system and a control method of the ultrasound system, in a case where occurrence of an error is detected, first operation information as at least one piece of operation information with the highest priority among a plurality of pieces of operation information corresponding to a type of the error that has occurred is displayed, and in a case where the number of times of the operation corresponding to the first operation information, which is performed by the user, has reached a predetermined number within a predetermined period after the first operation information is displayed, second operation information as at least one piece of operation information with the highest priority next to the first operation information is displayed.

A method including receiving events from different data sources for a service automatically executing in an enterprise system. A first event is enriched by providing the first event with first metadata that associates the first event with a first application used by the service. The first event is assigned to a time slice associated with the first application. A second event is enriched in a similar manner. A correlation graph of nodes and edges is built using the enriched events, with nodes representing the events and edges indicating relationships between the edges. A third event indicating a fault in the first application associated with the first node is received. The source of the error for the third event is identified using the second updated correlation graph and the time slice. The source of error is then mitigated.

Technologies for detecting and classifying errors detected in pipelined hardware are described. One device includes a hardware pipeline with a set of pipeline stages. Error detection logic can detect an error in the hardware pipeline, and control logic can classify the error in one of the multiple categories based on a type of the error, a position of the first data in a data stream that triggered the error, and a position of a pipeline stage in which the error is detected. The control logic can perform an error-response action based on the error classification of the error.

A method for managing a non-volatile random-access memory (NVRAM)-based storage subsystem, the method including: monitoring, by a slave controller on a NVRAM device of the NVRAM-based storage subsystem, an I/O operation on the NVRAM device; identifying, by the slave controller and based on the monitoring, at least one occurrence of error data; comparing, by the slave controller, a numeric aspect of the at least one occurrence of error data with a threshold setting; reporting, by the slave controller on the NVRAM device and to a master controller of the NVRAM-based storage subsystem, the at least one occurrence of error data in response to the numeric aspect crossing the threshold setting; and ascertaining, by the master controller of the NVRAM-based storage system, a physical location of a defect region on the NVRAM device where the error data has occurred by analyzing the reported at least one occurrence of error data.

A computer-implemented method includes receiving, from a first log agent, a first log collection. The computer-implemented method further includes receiving a first policy, wherein the first policy includes a definition of a first pattern and a definition of a procedure. The computer-implemented method further includes scanning the first log collection against the first policy to determine a match between a portion of the first log collection and the first pattern, with the matching portion of the first log collection being identified as a first data artefact. The computer-implemented method further includes, responsive to identifying the first data artefact, executing the procedure defined by the first policy, wherein the procedure includes: filtering the first log collection to yield a first group of filtered log entries, receiving a first data collection, and sending the first group of filtered log entries and the first data collection to a recipient system.