A method of generating action history data of an application according to an embodiment of the present disclosure is performed by a computing device. The method includes acquiring a first log generated by an application, acquiring a second log generated by a database (DB), matching application log entries included in the first log to DB log entries included in the second log, and generating action history data about actions performed by the application on the basis of a result of the matching.

An information processing system, method, and apparatus reduces maintenance costs and management work and expedites countermeasures. A guide for a new event is selected based on information transmitted from the monitoring target node at which the new event has occurred; whether a countermeasure designated by the guide selected for the new event can be executed or not is judged; under this circumstance, past events having similarity to the new event which has occurred at the monitoring target node are identified; and if countermeasures against a specified last number of the past events among the identified past events have been successful and a countermeasure against the past event which is the latest and is more similar to the new event among the past events identified as the new event has been successful, it is judged that the countermeasure designated by the guide selected by the guide selection unit should be executed.

A method, a system, and a computer program product for analyzing error messages. A first error log generated as a result of an execution of at least one task of a computing system at a first instance is received. The first error log include a plurality of first error messages. A first association rules model is generated using the first error messages. The first association rules model includes a plurality of association rules defining one or more relationships. A second error log, including a plurality of second error messages, generated as a result of an execution of the task at a second instance is received and a second association rules model is generated using the second error messages. Based on the first and second association rules models, at least one error message pattern associated with execution of the at least one task is determined.

Aspects of the disclosure relate to data feed meta detail categorization for confidence. A computing platform may retrieve source data from a source system and identify a first set of patterns associated with the source data. The computing platform may retrieve, from a target system, transferred data associated with a data transfer from the source system to the target system and identify a second set of patterns associated with transferred data. The computing platform may evaluate integrity of the transferred data by comparing the first set of patterns with the second set of patterns. The computing platform may detect whether the first set of patterns falls within an expected deviation from the second set of patterns based on the comparison. The computing platform may send one or more notifications based on detecting that the first set of patterns falls outside the expected deviation from the second set of patterns.

A determination is made whether an incident that affects one configuration item in a plurality of configuration items within an information technology environment impacts at least one event for at least one other configuration item in the plurality of configuration items. In response to determining that the incident does impact at least one event on at least one other configuration item, one or more pre-defined actions to execute on the at least one other configuration item are identified. The identified one or more pre-defined actions are executed on the at least one other configuration item.

A probability calculation result database includes a combination countermeasure table having an error code group including an error code of a repair target apparatus and a plurality error code indicating the relevant error code appeared in the past, as well as a countermeasure content against the error code and the plurality error code, and probabilities that the countermeasure content against both the error code and the plurality error code are taken respectively; a data processing unit which generates a combination table containing a new error code and a new plurality error code from apparatus error information obtained from the repair target apparatus, and repair prediction processing for predicting recommendable countermeasure contents against the new error code and plurality error code on the basis of the combination table and the probability calculation result database; and a result processing unit for providing the results of the repair prediction processing in order.

An improved SDK includes a set of APIs and a crash handler registered with the operating system. Each API is an interface accessible by a computer software application. Up on entrance, each API determines the current thread identifier, and inserts it into a list if it is not already in the list. Each thread identifier corresponds to an API call counter, which is incremented by one at the entrance and decremented by one at the exit point of the API. The SDK also records the identifier of the thread it creates for callback functions. When a crash occurs, the crash handler is executed. It determines that the crash is related to a callback interface if the crash thread identifier matches the callback thread identifier. The crash is determined to be caused by the SDK if the API call counter corresponding to the crash thread identifier is greater than zero.

A controller for use in a memory device includes an error information generator configured to receive error information about an error occurring while a command is being processed at a protocol layer, generate command error information corresponding to the command based on the received error information, and store the generated command error information in a first storage area, and an error information manager configured to store the command error information, stored in the first storage area, in a second storage area in response to an external request.

An operator terminal 2 accesses a web server 1 via a network NW, and acquires error information on an error that has occurred in the access to the web server 1. And then, the operator terminal 2 determines whether or not the occurred error is an error of a specific type on a basis of the error information, and re-accesses the web server 1 in response to the determination that the occurred error is the error of the specific type.

Techniques for predicting failure mode specific reliability characteristics of tangible equipment using parametric probability models are disclosed. In some example embodiments, a computer system receives a model training configuration entered via a user interface, trains a failure curve model for a selected failure mode of a selected equipment model based on the model training configuration at a time indicated by training schedule data, and generates analytical data for the selected failure mode of the selected equipment model using the trained failure curve model. The failure mode corresponds to a specific way in which the equipment model is capable of failing. In some example embodiments, the training of the failure curve model comprises determining a shape parameter and a scale parameter for the failure curve model based on a fitting of failure event data to a continuous probability distribution, and storing the parameters for use in generating the analytical data.