The invention relates to a system for generating synthetic digital data, comprising: a receiver configured to receive at least one measured signal, in particular an RF signal or a sensor signal, a converter configured to convert the at least one measured signal to a digital dataset representing signal characteristics of the at least one measured signal, at least one trainable neural network encoder, wherein, during a training routine, the neural network encoder is configured to receive the digital dataset and to generate a compressed representation of the digital dataset, a processing unit configured to analyze the compressed representation and to detect a correlation between the digital dataset and the compressed representation, wherein the processing unit is configured to generate decoder input data based on the detected correlation, and a trained neural network decoder which is configured to receive the decoder input data and to generate synthetic digital data representing signal characteristics of the at least one measured signal based on the decoder input data.

A compact data structure generation engine can be used to generate a compact data structure that represents performance data for high-scale networks. The compact data structure representing the performance data can be used to monitor the operation performed on or by a computer system to identify potentially anomalous conditions. In response, a corrective action can be taken to address the issue. This can be useful, for example, in improving the efficiency, effectiveness, and reliability of the computer system during operation.

Machine data reflecting operation of a monitored system is ingested and made available for search by a data intake and query system (DIQS). A monitoring function may search the data ingested by the DIQS to determine instances of notable events in regards to the monitored system and may further determine a defined invokable action message (IAM) associated with a notable event instance. Processing ensues to send an IAM to a communications device used by support personnel. The IAM includes information about an action invocation message (AIM) suitable to cause the performance of an action that possibly remedies or improves an operational condition represented by the notable event. Support personnel engages a user interface representation corresponding to the AIM and the AIM is sent to a remedial node where performance of the action is invoked.

The disclosure relates to an electronic apparatus and a method of controlling the same. The electronic apparatus includes: a communication interface; and a processor configured to receive log data of a plurality of devices connected to a network through the communication interface, acquire operation time information of each of the devices from the received log data, calculate similarity of the operation time between the plurality of devices based on the acquired operation time information, and determine a device group including two or more devices with relatively high calculated similarity among the plurality of devices.

A method of rendering a graphical user interface (GUI) comprising an application topology graph for a microservice architecture comprises generating a plurality of traces from a first plurality of spans generated by instrumented services in the architecture and generating generate a second plurality of spans for uninstrumented services using information extracted from the first plurality of spans. The method further comprises grouping the second plurality of spans with the plurality of traces. Subsequently, the method comprises traversing the traces and collecting a plurality of span pairs from the plurality of traces, wherein each pair of the span pairs is associated with a call between two services. The method also comprises aggregating information across the plurality of span pairs to reduce duplicative information associated with multiple occurrences of a same span pair from the plurality of span pairs. Finally, the method comprises rendering the application topology graph using the aggregated information.

Techniques are disclosed relating to automatically resolving an error in a user interaction with a user page without the user having to disengage from the user page to resolve the error. A monitoring agent may interface with the user page. The monitoring agent may provide an error signal to an error resolution module in response to detecting an error in the user interaction with the user page. The error resolution module may determine a causal factor for the error based on the error signal and contextual data at the time of the error. A resolution flow may be determined based on the causal factor. The resolution flow may be implemented by the monitoring agent contextually within the user page to resolve the error without the user disengaging from the user page.

A system and method for sampling telemetry events are provided. The method includes receiving, by a cloud-based server, a plurality of telemetry events, related to an application, from a plurality of client devices; generating, by the cloud-based server, a sampling model for collecting a telemetry event based on the plurality of telemetry events, where the sampling model defines under what conditions the telemetry event is to be reported by a client device; generating, by the cloud-based server, an instruction for determining whether or not to report an incoming same telemetry event by the client device based on the sampling model; and providing, by the cloud-based server, the instruction to the client device, to allow the client device to determine whether or not to report the incoming same telemetry event based on the instruction.

An information processing system that extracts a specified piece of log data or a specified field that constitutes the specified piece of log data, from a log file in which pieces of log data having different recording formats are mixed, the information processing system includes a format-information recording portion configured to store format information in which a determination condition to identify a recording format of the specified piece of log data is contained, an extraction-condition recording portion configured to store an extraction condition to extract the specified piece of log data or the specified field, and an extraction processing portion configured to extract the specified piece of log data or the specified field, from the log file by using the format information stored in the format-information recording portion and the extraction condition.

The disclosed embodiments relate to a system that produces anomaly-free training data to facilitate ML-based prognostic surveillance operations. During operation, the system receives a dataset comprising time-series signals obtained from a monitored system during normal, but not necessarily fault-free operation of the monitored system. Next, the system divides the dataset into subsets. The system then identifies subsets that contain anomalies by training one or more inferential models using combinations of the subsets, and using the one or more trained inferential models to detect anomalies in other target subsets of the dataset. Finally, the system removes any identified subsets from the dataset to produce anomaly-free training data.

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.