In one embodiment, a method for community content identification is provided. The method includes identifying one or more error messages from software running on a cloud instance. The method further includes determining relevance of content items of community content to the identified error messages. The method further includes assigning weights to the content items of the community content based on their determined relevance, wherein content items having high relevance to the identified error messages are assigned a higher weight than content items determined not relevant to the error messages and displaying content items according to the assigned weights.

Embodiments described herein detect data corruption in a distributed data set system. For example, a system comprises node(s) for processing queries with respect to a distributed data set comprising a plurality of storage segments. A write transaction resulting from a query with respect to a particular storage segment is logged in a log record that describes a modification to the storage segment. A log service provides the log record to a data server managing a portion of the distributed data set in which the storage segment is included, which performs the write transaction with respect to the storage segment. For redundancy purposes, the data server has replica(s) that manage respective replicas of the portion of the distributed data set managed thereby. For backup purposes, snapshots of the replica(s) are periodically generated. To determine a data corruption, a snapshot of one replica is cross-validated with a snapshot of another replica.

A program development support device for supporting development of a safety program to be executed in a safety controller includes: a development module that develops the safety program in response to user operation; a calculation module that calculates an identification value according to data of the developed safety program, in accordance with a function for calculating a random value with respect to an input; and an output module that outputs a document related to the safety program. The output module provides the identification value calculated by the calculation module to all pages of the document which are related to the safety program.

In order to provide a method for fault analysis in an industrial-method plant, for example a painting plant, by means of which fault situations are analysable simply and reliably, it is proposed according to the invention that the method should comprise the following: in particular automatic recognition of a fault situation in the industrial-method plant (101); storage of a fault situation data set for the respective recognised fault situation, in a fault database (136); automatic determination of a cause of the fault for the fault situation and/or automatic determination of process values that are relevant to the fault situation, on the basis of the fault data set of a respective recognised fault situation.

In some embodiments, a processing system includes at least one hardware block configured to change operation as a function of configuration data, a non-volatile memory including the configuration data for the at least one hardware block, and a configuration module configured to read the configuration data from the non-volatile memory and provide the configuration data read from the non-volatile memory to the at least one hardware block. The configuration module is configured to: receive mode configuration data; read the configuration data from the non-volatile memory; test whether the configuration data contain errors by verifying whether the configuration data are corrupted and/or invalid; and activate a normal operation mode or an error operation mode based on whether the configuration data contain or do not contain errors.

A system, method, and computer program product for a block-based backing up a storage device to an object storage service is provided. This includes the generation of a data object that encapsulates a data of a data extent. The data extent covers a block address range of the storage device. The data object is named with a base name that represents a logical block address (LBA) of the data extent. The base name is appended with an identifier that deterministically identifies a recovery point that the data object is associated with. The base name combined with the identifier represents a data object name for the data object. The named data object is then transmitted to the object storage service for backup of the data extent. At an initial backup, the full storage device is copied. In incremental backups afterwards, only those data extents that changed are backed up.

A method of processing test execution logs to determine error location and source includes creating a set of training examples based on previously processed test execution logs, clustering the training examples into a set of clusters using an unsupervised learning process, and using training examples of each cluster to train a respective supervised learning process to label data where each generated cluster is used as a class/label to identify the type of errors in the test execution log. The labeled data is then processed by supervised learning processes, specifically a classification algorithm. Once the classification model is built it is used to predict the type of the errors in future/unseen test execution logs. In some embodiments, the unsupervised learning process is a density-based spatial clustering of applications with noise clustering application, and the supervised learning processes are random forest deep neural networks.

Methods and systems for system maintenance include identifying patterns in heterogeneous logs. Predictive features are extracted from a set of input logs based on the identified patterns. It is determined that the predictive features indicate a future system failure using a first model. A second model is trained, based on a target sample from the predictive features and based on weights associated with a distance between the target sample and a set of samples from the predictive features, to identify one or more parameters of the second model associated with the future system failure. A system maintenance action is performed in accordance with the identified one or more parameters.

A process for monitoring network behaviour of IoT devices, which includes: monitoring a communication network traffic to identify TCP and UDP traffic flows to and from each of one or more IoT devices; processing the identified traffic flows to generate a corresponding data structure representing the identified network traffic flows of the IoT device in terms of, for each of local and internet networks, one or more identifiers of respective hosts and/or devices that had a network connection with the IoT device, source and destination ports and network protocols; and comparing the generated data structure for each IoT device to corresponding data structures representing predetermined manufacturer usage description (MUD) specifications of known types of IoT devices to generate quantitative measures of similarity of the traffic flows of the IoT device to traffic flows defined by the predetermined MUD specifications to identify the type of the IoT device

Techniques disclosed herein relate to managing notifications to a user associated with a computing device. The notifications correspond to a response to an indication of an exception condition on the computing device. The response to the exception condition includes a plurality of steps, including computer-implemented steps in which data objects output a plurality of notifications for the user. These notifications are processed by a notification choreographer and used to prepare a unified status communication. The unified status communication is output to the user and depicts information corresponding to a plurality of the notifications.