A computer-implemented method, computer program product, and a technical system for generating triples including providing a plurality of log entries from respective log files, wherein each log entry of the plurality of log entries includes at least one text message, generating at least one template based on the plurality of log entries using unsupervised clustering, wherein the at least one template includes at least one variable part and at least one fixed part, assigning each log entry of the plurality of log entries to one respective template based on the generated at least one template using a similarity measure, extracting the corresponding at least one variable and at least one fixed part of each text message of the plurality of text messages as key/value pairs using the respective assigned at least one template based on the plurality of log entries, and providing the text messages, keys and values as triples.

A computer-implemented method, computer program product, and a technical system for generating triples including providing a plurality of log entries from respective log files, wherein each log entry of the plurality of log entries includes at least one text message, generating at least one template based on the plurality of log entries using unsupervised clustering, wherein the at least one template includes at least one variable part and at least one fixed part, assigning each log entry of the plurality of log entries to one respective template based on the generated at least one template using a similarity measure, extracting the corresponding at least one variable and at least one fixed part of each text message of the plurality of text messages as key/value pairs using the respective assigned at least one template based on the plurality of log entries, and providing the text messages, keys and values as triples.

In general, embodiments relate to a method for identifying hardware component failures, comprising: obtaining system logs that show a transition of device states for a device; using a normalization and filtering module to process and extract relevant data from the system logs and important keywords for the device; creating a device state path for the device from a healthy device state to an unhealthy device state using the extracted relevant data; obtaining the device state path for the device from a storage and a current device state of the device; predicting a next device state of the device based on the current device state using an analysis module; generating a device state chain using the device state path, current device state, and next device state; and identifying root cause of a hardware component failure using the device state chain.

A battery information management system, comprises: a battery; and a server that establishes communication with the battery via a network. The battery includes: a storage unit configured to store maintenance information for maintenance of the battery, user information related to personal data of a user of the battery, and usage history information of the battery; an information communication unit configured to transmit the user information and the usage history information from the storage unit to the server; and a control unit configured to control the storage unit and the information communication unit.

A battery information management system, comprises: a battery; and a server that establishes communication with the battery via a network. The battery includes: a storage unit configured to store maintenance information for maintenance of the battery, user information related to personal data of a user of the battery, and usage history information of the battery; an information communication unit configured to transmit the user information and the usage history information from the storage unit to the server; and a control unit configured to control the storage unit and the information communication unit.

A flight task processing method includes generating and displaying a user prompt according to flight data of a plurality of flight tasks, selecting one of the flight tasks as a target flight task in response to a selection operation with respect to the user prompt, determining the flight data of the target flight task, processing the flight data of the target flight task to obtain control instruction, and automatically controlling an operation of an aerial vehicle according to the control instruction to reproduce the target flight task by controlling the aerial vehicle to fly to a waypoint included in the flight data, controlling a gimbal of the aerial vehicle to face a gimbal orientation included in the flight data while the aerial vehicle is at the waypoint, and controlling a camera carried by the gimbal to acquire an image while the aerial vehicle is at the waypoint.

A flight task processing method includes generating and displaying a user prompt according to flight data of a plurality of flight tasks, selecting one of the flight tasks as a target flight task in response to a selection operation with respect to the user prompt, determining the flight data of the target flight task, processing the flight data of the target flight task to obtain control instruction, and automatically controlling an operation of an aerial vehicle according to the control instruction to reproduce the target flight task by controlling the aerial vehicle to fly to a waypoint included in the flight data, controlling a gimbal of the aerial vehicle to face a gimbal orientation included in the flight data while the aerial vehicle is at the waypoint, and controlling a camera carried by the gimbal to acquire an image while the aerial vehicle is at the waypoint.

An encoder receives an application log file including component values and encodes the component values into lists of preliminary encoded values. The lists of preliminary encoded values are combined into a combined list of preliminary encoded values. An encoder-decoder neural network is trained to encode the combined list of preliminary encoded values into a list of collectively encoded values, to decode the list of collectively encoded values into a list of decoded values, and to optimize a metric measuring the encoder-decoder neural network's functioning, in response to receiving the combined list of preliminary encoded values. The trained encoder-decoder neural network receives combined lists of preliminary encoded values for application log files and encodes the combined lists of preliminary encoded values into lists of collectively encoded values. The lists of collectively encoded values are sent to a detector, thereby enabling the detector to detect an application event associated with the application log files.

Presented are concepts for processing an event log. Once such concept obtains an event log comprising a log of event occurrences for an executed process. It also obtains an events embedding model representative of relationships between a plurality of events of one or more processes. Based on the events embedding model, repeating events in the event log are clustered into one or more groups, and each of the one or more groups are associated with a respective identifier. Repeating events in the event log are then replaced with the identifier associated with the group that the repeating event is a member of.

A computer-implemented method for automating the development of industrial machine learning applications includes one or more sub-methods that, depending on the industrial machine learning problem, may be executed iteratively. These sub-methods include at least one of a method to automate the data cleaning in training and later application of machine learning models, a method to label time series (in particular signal data) with help of other timestamp records, feature engineering with the help of process mining, and automated hyper-parameter tuning for data segmentation and classification.