A system and process extract software application performance data from disparate ownership sources and make the various source data compatible for comparison data. A software application's performance in the marketplace may be compared to other applications in a same group with comparable data information. A M2M (mobile-to-mobile) technology is an interface layer connection to a backend server that builds machine learning pipelines and may use artificial intelligence to turn massive datasets into identifiable patterns, algorithms and statistical models. This layer is capable of cleaning, aggregating, and organizing data from disparate sources to produce meaningful conclusions to complex problems to inform strategic business decisions.

State of the art predictive maintenance systems that generate predictions with respect to maintenance of High Performance Computing (HPC) systems have the disadvantage that they either are reactive, or the predictions are affected due to quality issues associated with the data being collected from the HPC systems. The disclosure herein generally relates to predictive maintenance, and, more particularly, to a method and system for predictive maintenance of High Performance Computing (HPC) systems. The system performs abstraction and cleansing on performance data collected from the HPC systems, and generates a cleansed performance data, on which a Machine Leaning (ML) prediction is applied to generate predictions with respect to maintenance of the HPC systems.

Techniques are described herein for prediction of an buffer pool size (BPS). Before performing BPS prediction, gathered data are used to determine whether a target workload is in a steady state. Historical utilization data gathered while the workload is in a steady state are used to predict object-specific BPS components for database objects, accessed by the target workload, that are identified for BPS analysis based on shares of the total disk I/O requests, for the workload, that are attributed to the respective objects. Preference of analysis is given to objects that are associated with larger shares of disk I/O activity. An object-specific BPS component is determined based on a coverage function that returns a percentage of the database object size (on disk) that should be available in the buffer pool for that database object. The percentage is determined using either a heuristic-based or a machine learning-based approach.

A system for firewall data log processing, comprising a firewall logging system operating on a first processor and configured to cause the first processor to receive firewall log data and to process the firewall log data on a periodic basis to reduce the size of the firewall log data and a firewall reporting system operating on a second processor and configured to process the reduced size firewall log data to generate a report on a user interface that includes one or more analytics from the reduced size firewall data.

A system for displaying a performance dashboard comprises an input interface, a processor, and an output interface. The input interface is configured to receive log data. The log data comprises a set of process log entries. The processor is configured to determine one or more daemon response times and to determine dashboard information. The dashboard information is based at least in part on the log data and the one or more daemon response times. The output interface is configured to provide the dashboard information.

A method comprises receiving data corresponding to execution of one or more applications, accessing at least one function from a codes as a service source, and training the at least one function based, at least in part, on one or more parameters, wherein the training is performed using a first portion of the data. In the method, a deployment version of the at least one function is generated based, at least in part, on the training, and the deployment version of the at least one function is applied to a second portion of the data to perform at least one service.

A machine learning method executed by a computer, the method includes distributing a first learning model learned on the basis of a plurality of logs collected from a plurality of electronic devices to each of the plurality of electronic devices, the first learning model outputting operation content for operating an electronic device; when an operation different from an output result of the first learning model is performed by a user relative to a first electronic device among the plurality of electronic devices, estimating a similar log corresponding to a state of the learning model in which the different operation is performed from the plurality of logs; generating a second learning model on the basis of a log obtained by excluding a log of a second electronic device associated with the similar log from among the plurality of logs; and distributing the second learning model to the first electronic device.

A request is received to schedule a new software process. Description data associated with the new software process is retrieved. A workload resource prediction is requested and received for the new software process. A landscape directory is analyzed to determine a computing host in a managed landscape on which to load the new software process. The new software process is executed on the computing host.

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 method and system for communicating with IoT devices connected to a vehicle to gather information related to device operation or performance is disclosed. The system makes a copy of at least a portion of the device's non-volatile memory and/or receives IoT device data (e.g., sensor data and/or log files etc.) from an IoT device that recently failed. The system determines which log files and/or sensor data, for example, the IoT device created before and/or after a failure. After gathering this information, the system stores the information, sends it to a storage destination for further analysis and diagnostics to troubleshoot the failure and send a fix or software update to the IoT device. The information can also be placed into secondary storage to comply with regulatory, insurance, or legal purposes.