An information processing device includes: an operation processor; a storage in which an initial password being an initial setting of an administrator password is prestored; a logger that logs details of operations inputted using the operation processor, in the storage as operation history information; a password entry monitor that confirms whether or not the administrator password has been entered; a password changer that changes, upon a confirmation that a password differing from the initial password has been entered, the administrator password and stores the entered password as a new administrator password; and a log presenter that presents the operation history information when the administrator password has been changed.

This technical solution relates to big data computer processing field, in particular, to the system of automatic quality monitoring of data obtained from different sources in real time.

The technical result is improving quality and accuracy of analysis of data obtained from different sources in real time.

The computer-assisted system of automatic quality monitoring of data obtained from different sources in real time is claimed, comprising: web application module, configured to add new monitoring sources to the system, configure advanced monitoring options, browse through event history and monitoring reports, and also visualize the detected data deviations; integration connector module, configured to connect the system to different sources for obtaining data, and configured to transform these data into common internal format for further uniform processing; machine learning module, which is self-learning to evaluate quality of data obtained in real time, during which: receives from connectors the transformed data from different sources within the specified time period and saves them into total sample; starts initialization of the saved sample monitoring by defining data change scales, while statistics are calculated for each sample indicator in accordance with a specific scale, and algorithm for check initialization is started for each indicator based on the calculated statistics; after completion of learning the learned model parameters are saved in the database; machine learning module uses the learned model parameters for subsequent monitoring new data received according to the specified schedule, while, the machine learning module is continuously relearned with new corrected data, if the current model improperly recognizes dependencies in new data, the module is fully relearned; if some deviations are detected after monitoring of new data received, the notification aggregation module composes a text with errors; information channel integration module sends the text with errors to corresponding users.

A system includes persistent storage containing data used by software applications, and a server application configured to perform operations, including identifying a subset of the data, where the subset is used to generate a full offline payload for a particular software application and that includes user interface (UI) components selected for the particular software application based on the subset of the data. The operations also include monitoring the subset of the data to identify a modification to particular data and, based on identifying the modification, generating a partial offline payload based on the particular data as modified, where the partial offline payload includes a subset of the UI components, and where each UI component of the subset is updated based on the particular data as modified. The operations further include receiving a request for the partial offline payload and, based on receiving the request, transmitting the partial offline payload.

Techniques are disclosed for attribution analysis in analytical workflows. A data processing system (DPS) obtains an overall model comprising one or more sub-models. The DPS selects an output variable of the overall model for which attribution of changes is to be performed, and a plurality of input variables against which changes are to be attributed to. The overall model is initially executed with respect to a data set of values for the plurality of input variables to generate a base result for the output variable. The overall model is iteratively executed based on a condition associated with the plurality of input variables to obtain a new result for the output variable. In each iteration, a value of an input variable is changed with respect to the data set of values and a change in the output variable with respect to the base result is attributed to the corresponding input variable.

Techniques for implementing high availability for persistent memory are provided. In one embodiment, a first computer system can detect an alternating current (AC) power loss/cycle event and, in response to the event, can save data in a persistent memory of the first computer system to a memory or storage device that is remote from the first computer system and is accessible by a second computer system. The first computer system can then generate a signal for the second computer system subsequently to initiating or completing the save process, thereby allowing the second computer system to restore the saved data from the memory or storage device into its own persistent memory.

An information processing device includes: an operation processor; a storage in which an initial password being an initial setting of an administrator password is prestored; a logger that logs details of operations inputted using the operation processor, in the storage as operation history information; a password entry monitor that confirms whether or not the administrator password has been entered; a password changer that changes, upon a confirmation that a password differing from the initial password has been entered, the administrator password and stores the entered password as a new administrator password; and a log presenter that presents the operation history information when the administrator password has been changed.

Systems and methods detect and prevent changes in business applications that modify its state to non-secure and/or non-compliant. A system may include a processor set up to perform: defining a compliant state of a computer software configuration, monitoring a state of the computer software configuration, detecting a change in the state of the computer software configuration, wherein the change causes a changed state, and comparing the compliant state and the changed state, wherein if the changed state conflicts with the compliant state, stopping the change.

Systems, methods, and computer program products are provided for providing automatic diagnostics of API configuration. The method includes receiving a local module log relating to one or more changes to code of a local device that would affect an API configuration. The method also includes identifying one or more expected local module logs based at least in part on one or more previous local module logs associated with the local device. The method further includes comparing the local module log with the one or more expected local module logs. The comparison determines any differences between the local module log and the one or more expected local module logs. The method still further includes generating an API configuration diagnostic action based on determining a difference between the local module log and the expected local module log(s). The method also includes causing an execution of the API configuration diagnostic action.

Systems and methods facilitating automated mocking of computer system deployments are described herein. A method as described herein can include associating, by a first system operatively coupled to a processor, respective properties of a first deployment of a second system on a first computing device with respective automation mapping functions; executing, by the first system, the automation mapping functions in an order defined by dependencies between respective ones of the automation mapping functions, resulting in a series of system modeling tasks and an order associated with the series of system modeling tasks; and performing, by the first system, the series of system modeling tasks in the order associated therewith, resulting in a second deployment of the second system being created on a second computing device that is distinct from the first computing device.

A system, method and apparatus to record data relevant to hardware errors identified by microprocessors. For example, in response to a hardware error, a microprocessor can store first data about the error in registers in the microprocessor and start to execute instructions configured in firmware and/or in an operating system. Execution of the instructions in response to the hardware error causes the microprocessor to: generating second data about the error based at least in part on the first data in the registers; and store the second data at a location not affected by restarting execution of an operating system in the processor. For example, the execution of the instructions can cause the microprocessor to decode the first data to obtain a temperature of the computing device as part of the second data.