Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for data processing. The method described herein includes determining identification information for an operation, wherein the identification information includes at least one field indicating content of the operation and a field indicating a unique identification of the operation. The method further includes identifying, based on the identification information, log entries for the operation in log files for at least one microservice invoked by the operation. The method further includes determining a log for the operation, wherein the log includes the identified log entries. With the solution for data processing of the present application, it is possible to easily acquire logs for an operation using identification information that includes a field indicating the content of the operation, so as to facilitate targeted analysis of the operation based on the content of the operation.

A data selection device assists selection of suitable training data used for sign detection, and includes: a storage unit configured to store time-series sensor data acquired from a sensor with respect to a failure prediction target device; a data classification unit configured to classify the time-series sensor data into a first data set and a second data set while allowing the first data set and the second data set to overlap each other; a training data selection unit configured to select a subset of the second data set based on a value range of the first data set; a training data evaluation unit configured to calculate an evaluation index indicating a suitability of a failure prediction model as training data based on the selected subset; and a data selection condition search unit configured to search for the value range of the first data set that maximizes the evaluation index.

The invention provides a maintenance recommendation system in which an inspection item is presented timely in the halfway of an inspection, accuracy of failure mode identification is improved, a failure mode is identified at an early stage, meanwhile, a time required for investigating a content of the failure is reduced, and a time from device failure to reset is shortened. The maintenance recommendation system includes: a primary storage unit that stores an input inspection result; a failure mode probability calculation unit that is configured to calculate a probability of a failure mode based on the inspection result stored in the primary storage unit; an inspection item search unit that is configured to extract an inspection item with the minimum inspection score from uninspected inspection items; and a main routine operation unit that is configured to narrow down a failure mode candidate and an inspection item candidate from all inspection items.

A method for redundant control in a distributed automation system, preferably a real-time automation system, for operating a client device of the distributed automation system is discussed. The method includes using the client device to monitor for the occurrence of a fault in communication between the client device and a first computing infrastructure that is part of the distributed automation system and operates the client device. The method may also include using the client device, once the fault occurs, to instruct a second computing infrastructure of the distributed automation system to operate the client device.

Techniques described herein relate to a method for deploying workflows with data management services. The method may include identifying a service update event; identifying a service sub-tree based on a service call graph; generating an update sequence for the service sub-tree; predicting an update window for the service sub-tree using a final estimated updated completion time for the service, wherein the final estimated updated completion time is based on a risk profile; selecting a first service of the service sub-tree based on the update sequence, wherein the first service includes a first standby service instance and a first active service instance; generating a backup of a first portion of a services shared data volume repository associated with the first service; and applying an update to the first standby service instance to obtain a first updated active service instance.

In general, embodiments of the invention relate to a method for generating upgrade recommendations. The method comprising obtaining telemetry data for a target entity, determining, using the telemetry data, at least one of a predicted upgrade time and a upgrade readiness factor for the target entity, generating an recommendation based on the at least one of the predicted upgrade time and the upgrade readiness factor for the target entity, and initiating a display of the recommendation on a graphical user interface of client.

Disclosed here is a system to automatically predict and reduce hardware related outages. The system can obtain a performance indicator associated with a wireless telecommunication network including a system performance indicator or an application log, along with a machine learning model trained to predict and resolve a hardware error based on the performance indicator. The machine learning model can detect an anomaly associated with the performance indicator by detecting an infrequent occurrence in the performance indicator. The machine learning model can determine whether the anomaly is similar to a prior anomaly indicating a prior hardware error. Upon determining that the anomaly is similar to the prior hardware error, the machine learning model can predict an occurrence of the hardware error.

An example of an apparatus including a communication interface to receive an image file is provided. The image file represents a scanned image of a output generated by a printing device. The apparatus further includes an identification engine to process the image file with a convolutional neural network model to identify a feature. The feature may be indicative of a potential failure. The apparatus also includes an image analysis engine to indicate a life expectancy of a part associated with the potential failure based on the feature. The image analysis engine uses the convolutional neural network model to determine life expectancy.

A method for classifying failures can determine consumer devices on-line as being repairable or not to be repaired. The method obtains a training set, the set including information as to total failure and information as to repairable failure. First key information in repairable failure and second key information in total failure are obtained. A first TF-IDF value of each first key information and a second TF-IDF value of each second key information are computed. A first feature bank is created based on the first TF-IDF value and a first threshold value, and a second feature bank is created based on the second TF-IDF value and a second threshold. Target failure is classified by the trained classifier. A failure classification is quickly achieved. An electronic device and a computer readable storage medium applying the method are also provided.

Herein are resource-constrained techniques that plan ahead for resiliently moving pluggable databases between container databases after a failure in a high-availability database cluster. In an embodiment, a computer identifies many alternative placements that respectively assign each pluggable database to a respective container database. For each alternative placement, a respective resilience score is calculated for each pluggable database that is based on the container database of the pluggable database. Based on the resilience scores of the pluggable databases for the alternative placements, a particular placement is selected as an optimal placement that would maximize utilization of computer resources, minimize database latencies, maximize system throughput, and maximize the ability of the database cluster to avoid a service outage.