An information processing apparatus manages backup data that can be used as installation data and receives a request from an external apparatus. Also, the present information processing apparatus, in a case where the received request is a request for acquiring backup data, generates, by using the backup data, the installation data to be provided based on information associated with the request origin and information associated with the acquisition origin of the backup data. Wherein the installation data is generated by deleting, as necessary, information to be concealed in the managed backup data.

Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

A storage device and operating method thereof includes a storage controller configured to receive a get log page command from a host and transmit, to the host, log data about at least one context selected from among respective contexts of a plurality of components according to the get log page command, and a memory storing the log data, wherein the get log page command includes selection information for selecting at least one component from among the plurality of components.

Systems and methods for data center operational monitoring are disclosed. In at least one embodiment, a root cause for one or more data center component failures is determined based, at least in part, upon data from one or more sensors.

A data analytics system stores a data file that includes an ordered set of data blocks. The data blocks can be parsed out of order. An error management module of the data analytics system detects a parse error occurring during parsing of a data block and generates an error message for the parse error. The error message includes unresolved location information indicating a location of the detected parse error in the data block. The error management module resolves the unresolved location information after determining that one or more additional data blocks preceding the data block in the ordered set have been parsed. The error management module generates resolved location information that indicates a location of the parse error in the data file. The error management module updates the error message with the resolved location information and outputs the updated error message.

An embodiment may involve storage containing incident logs and mappings between incident logs and vector representations generated by a machine learning (ML) model. The embodiment may further involve one or more processors configured to: receive, from a client device, a request corresponding to an additional incident log; transmit, to the ML model, additional values as appearing in the additional incident log, wherein reception of the additional values causes the ML model to generate an additional vector representation of the additional incident log; obtain confidence measurements respectively representing similarities between the additional vector representation and each of the vector representations corresponding to the incident logs; determine, based on the confidence measurements, a set of one or more incident logs that are semantically relevant to the additional incident log; and transmit, to the client device, representations of the one or more incident logs and their corresponding confidence measurements.

A training dataset is used to train an unsupervised machine learning trained model. Corresponding gradient values are determined for a plurality of entries included in the training dataset using the trained unsupervised machine learning model. A first subset of the training dataset is selected based on the determined corresponding gradient values and a first threshold value selected from a set of threshold values. A labeled version of the selected first subset is used to train a first supervised machine learning model to detect one or more anomalies.

A method of calculating a failure probability of a change in one or more source code repositories comprises analyzing at least one commit made to the source code repositories, determining a type of the commit selected from a fixing commit and a new code commit, if the commit is a new code commit, determining a set of areas of source code modified, if the code is a fixing commit, determining which commit of a plurality of new code commits is the causing commit, analyzing the commit message and calculating one or more parameters of the commit message, training a machine learning classifier with the set of data, and using the machine learning classifier to calculate a probability that the commit will cause a failure in the source code repository. Methods and systems for task assignment and test selection are also described.

Aspects of the disclosure relate to application assessment. A computing platform may receive content information and manual input data corresponding to hierarchical content. The computing platform may establish a content tree indicating relationships between pages of the hierarchical content. The computing platform may receive starting/ending pages of the hierarchical content and application assessment commands. Using the content tree and in response to receipt of the application assessment commands, the computing platform may generate error information based on the starting page and the ending page by performing a holistic error analysis of the hierarchical content between the starting page and the ending page, which may include automatically populating manual input fields using the manual input data. The computing platform may send the error information and commands to display an error notification based on the error information, which may cause the administrator computing device to display the error notification.

A device may receive input data identifying metrics associated with components of a system, and may format the input data to generate formatted input data. The device may utilize the formatted input data to generate a topology of the system, and may customize models of nodes of the topology, based on the formatted input data, to generate a customized topology with customized nodes. The device may generate aggregation rules for aggregating anomalies, generated by the customized topology, and may aggregate the anomalies generated by the customized topology, into events, based on the aggregation rules. The device may process the events, with a machine learning model, to generate clustered events from the events, and may configure alerting rules associated with alerting actions, based on the clustered events, to generate configured alerting rules. The device may perform one or more actions based on the clustered events and the configured alerting rules.