An aspect of behavior of an embedded system may be determined by (a) determining a baseline behavior of the embedded system from a sequence of patterns in real-time digital measurements extracted from the embedded system; (b) extracting, while the embedded system is operating, real-time digital measurements from the embedded system; (c) extracting features from the real-time digital measurements extracted from the embedded system while the embedded system was operating; and (d) determining the aspect of the behavior of the embedded system by analyzing the extracted features with respect to features of the baseline behavior determined.

Systems and methods are described for facilitating operation of a plurality of computing devices. Data indicative of enumerated resources of a computing device is collected. The data is collected without dependency on write permissions to a file system of the one computing device. A condition of the computing device is determined based on historical data associated with enumerated resources of other computing devices. The identified condition can be updated as updated historical data becomes available. A communication to the computing device may be sent based on the identified condition.

A method, system and computer readable medium for running a legacy application on a non-legacy device. Operating parameters of the non-legacy device when running the legacy application are set based on one or more pre-determined heuristics for adjustment of operating parameters of the newer system when running the legacy application on the non-legacy device from one or more performance metrics and other performance information.

A system and method for real-time or near real-time anomaly detection and root cause automation in production environments or in other environments using shrunk dynamic call graphs are provided. The system includes an instrumentation agent that generates shrunk dynamic call graphs and exceptions/errors by injecting monitoring code or probes or call-tags into monitored application, a data agent that forwards collected data to the analysis engine over a network, an analysis engine that performs continuous clustering using machine learning, anomaly, and root cause detection. The system also includes a reporting module to report the anomaly.

Embodiments relate to a computer platform to support processing of informational logs and corresponding performance data to detect and mitigate occurrence of anomalous behavior. Metrics are extracted from the informational logs and correlated with performance data, and in an exemplary embodiment golden signal metrics. A window or block of the logs is classified as potential candidates or indicators of anomalous behavior, which in an embodiment is indicative of potential failure or service outage. A control signal is dynamically issued to an operatively coupled device associated with the window or block of logs. The control signal is configured to selectively control a state of a physical device or process controlled by software, with the control directed at mitigating or eliminating the effect(s) of the anomalous behavior.

An object of the present disclosure is to provide a control apparatus that controls a plurality of communication systems so that the plurality of communication systems can perform analysis with high accuracy. The control apparatus (30) according to the present disclosure includes a communication unit (31) and a determination unit (32). The communication unit (31) receives, from an analysis apparatus (10) configured to perform machine learning using communication logs collected from a communication apparatus in order to generate a learning model, statistical information about each of the communication logs and information about the learning model. The determination unit (32) determines an analysis apparatus (20) to which the information about the learning model is applied based on the statistical information.

Systems and methods for conducting a test on a third-party testing platform are provided. A networked system causes presentation of a setup user interface to a third-party user, whereby the setup user interface includes a field for indicating an attribute of a publication to be tested. The networked system receives, via the setup user interface, an indication of the attribute, a subject to be tested, and one or more test parameters. The networked system applies the attribute change to a first version of the publication to generate a second version of the publication. The first version is presented to a first subset of potential users and the second version is presented to a second subset of potential users. Interactions with both the first version and the second version are monitored and analyzed to determine results of the test. The results are then presented to the third-party user.

An encoder receives an application log file including component values and encodes the component values into lists of preliminary encoded values. The lists of preliminary encoded values are combined into a combined list of preliminary encoded values. An encoder-decoder neural network is trained to encode the combined list of preliminary encoded values into a list of collectively encoded values, to decode the list of collectively encoded values into a list of decoded values, and to optimize a metric measuring the encoder-decoder neural network's functioning, in response to receiving the combined list of preliminary encoded values. The trained encoder-decoder neural network receives combined lists of preliminary encoded values for application log files and encodes the combined lists of preliminary encoded values into lists of collectively encoded values. The lists of collectively encoded values are sent to a detector, thereby enabling the detector to detect an application event associated with the application log files.

A storage device is disclosed. The storage device may include storage to store data and a controller to manage reading data from and writing data to the storage. The controller may also include a receiver to receive a plurality of requests, information determination logic to determine information about the plurality of requests, storage for the information about a plurality of requests, and sharing logic to share the information with a management controller.

Apparatus and methods an artificial intelligence method of reducing failure in an informational flow of a data stream controlled by an Extract Transform Load process using a machine learning (“ML”) model training system are provided. The method may include deploying a software sensor that periodically captures data points for an extract job executed during an extract phase of the process. The method may also include building a behavior profile concurrently with the receipt of each of the data points. The method may further include comparing the behavior profile to behavior profiles stored in an Adverse Behavior Model database and behavior profiles stored in a Normal Behavior Model database. When the behavior profile is determined to have a threshold number of match points matching the behavior profile to behavior profiles in the Adverse Behavior Model database, the method may include increasing a target database storage capacity.