A method of monitoring a closed system, an apparatus thereof and a monitoring device are provided. The method of monitoring the closed system includes: performing a page capturing on a web page of the closed system; searching from a captured page, according to configuration information of data to be monitored of the closed system, a text content corresponding to the data to be monitored; and converting the text content corresponding to the data to be monitored into monitored data which a system monitoring platform is capable of recognizing, and storing the monitored data.

Systems and methods are disclosed herein for recommending solutions to execution errors of software packages. An error message related to compilation or execution of computer code is received and, based on a vector representation of the error message and vector representations corresponding to message threads from one or more collaborative messaging systems, a set of message threads that match the error message is identified. Furthermore, a known error message that matches the set of message threads is identified and updated computer code is generated based on the known error message. The updated computer code is then provided to a client device.

Systems and methods for cache optimization are disclosed. A request for a user interface is received from a first user device. The request includes a user key. An interface key corresponding to an interface template of the requested user interface is generated from the user key. The interface template of the requested user interface is loaded. The interface template includes one or more edge side include (ESI) identifiers in the interface template. An element key corresponding to a first ESI element associated with a first of the one or more ESI identifiers is generated from the user key. The first ESI element is loaded and positioned at a location within the interface template identified by the first of the one or more ESI identifiers. A complete user interface is provided to the first user device. The complete user interface includes the interface template having the first ESI element positioned therein.

A system includes a processing system and a memory system. The memory system stores instructions for identifying a cloud application in a cloud environment as a non-disposable application and monitoring a plurality of instances of the non-disposable application running in the cloud environment. The instructions when executed by the processing system further result in determining that a number of the instances of the non-disposable application should be modified based on one or more demand predictions by an artificial intelligence scaler, adjusting the number of the instances of the non-disposable application running in the cloud environment based on the one or more demand predictions, and modifying an allocation of one or more resources of the cloud environment associated with adjusting the number of the instances of the non-disposable application.

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 malware monitoring method includes: obtaining a malware sample; extracting operational parameters corresponding to the malware sample; configuring an emulator application corresponding to the malware sample using the operational parameters; executing a plurality of instances of the configured emulator application; collecting output data from each of the plurality of instances; and generating indicators of compromise (IOCs) based on the collected output data.

A graph model of a graphical user interface (GUI) may be generated by processing usage data of the GUI where the usage data comprises sequences of GUI pages and actions between GUI pages. The nodes of the graph model may be determined by obtaining GUI pages from the usage data, identifying dynamic GUI elements in the GUI pages, generating canonical GUI pages by modifying the GUI pages using the dynamic GUI elements, and creating graph nodes using the canonical GUI pages. The edges of the graph may be determined by processing actions from the GUI data that were performed by users to transition from one GUI page to another GUI page. The graph model of the GUI may be used for any appropriate application, such as determining statistics relating to the GUI or statistics relating to individual users of the GUI.

The present disclosure relates to a method of operating a database system. The database system comprises: a database; a first compute node comprising a first database proxy; and a second compute node comprising a second database proxy. The method comprises receiving and processing, at the first database proxy, a first plurality of access requests to access the database; receiving and processing, at the second database proxy, a second plurality of database access requests to access the database; monitoring for a failure event associated with the first database proxy; and, in response to the monitoring indicating a failure event, initiating a failover procedure between the first database proxy and the second database proxy. The failover procedure comprises: redirecting the first plurality of access requests to the second database proxy; and processing, at the second database proxy, the first plurality of access requests.

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.

An analysis function imparting device (10) includes a virtual machine analyzing unit (121) that analyzes a virtual machine of a script engine, a command set architecture analyzing unit (122) that analyzes a command set architecture that is a command system of the virtual machine, and an analysis function imparting unit (123) that performs hooking for imparting multipath execution functions to the script engine, on the basis of architecture information acquired by the analysis performed by the virtual machine analyzing unit (121) and the command set architecture analyzing unit (122).