154 results about "Event (computing)" patented technology

Certain aspects provide techniques and apparatus for executing queries in a computing system using machine learning models. An example method generally includes receiving a plan to satisfy a request in the computing system and event log data associated with execution of the plan. The plan generally specifies a first plurality of function calls at a first level of granularity. Using a plan refinement machine learning model, a refined plan is generated when the event log data indicates that execution of the generated plan results in one or more execution errors and the one or more execution errors are solvable. Generally, the refined plan specifies a second plurality of function calls at a second level of granularity, the second level of granularity being finer than the first level of granularity.

A computer-implemented method, computer program product and computing system for defining a formatting script for use with a Generative AI Model; receiving a plurality of notifications of a security event, wherein each of the plurality of notifications includes a computer-readable language portion that defines one or more specifics of the security event, thus defining a plurality of computer-readable language portions; processing at least a portion of each of the plurality of computer-readable language portions using the Generative AI Model and the formatting script to summarize each of the computer-readable language portions and generate a plurality of event summaries; and′ processing at least a portion of each of the plurality of event summaries using the Generative AI Model and the formatting script to summarize the plurality of event summaries and generate a summarized human-readable report.

Certain aspects provide techniques and apparatus for executing queries in a computing system using machine learning models. An example method generally includes receiving a plan to satisfy a request in the computing system and event log data associated with execution of the plan. The plan generally specifies a first plurality of actions to be performed by the computing system at a first level of granularity. Using a plan refinement machine learning model, a refined plan is generated when the event log data indicates that execution of the generated plan results in one or more execution errors and the one or more execution errors are solvable. Generally, the refined plan specifies a second plurality of actions to be performed by the computing system at a second level of granularity, the second level of granularity being finer than the first level of granularity.

A data platform monitors a compute environment by performing multi-stage heuristic analysis of event data representing a plurality of events occurring within the environment. The platform utilizes multiple event analyzers, each configured according to a distinct analysis heuristic, to evaluate different subsets of the event data and generate corresponding output signals. A higher-level event analyzer applies a further heuristic to the multiple output signals to generate a composite alert signal, indicating whether the combination of analyzed events collectively represents a security intrusion or other anomalous condition of sufficient severity to warrant alerting. Based on the composite alert signal, the platform performs an alert-based operation, such as generating a user-facing alert, initiating an automated mitigation, or updating a contextual model of system behavior. By combining the analytical outputs of heterogeneous heuristics, the disclosed architecture enhances the accuracy and contextual relevance of automated intrusion detection within complex computing environments.

A system and method for ingesting delayed logs for cybersecurity detection is presented. The method includes detecting a resource deployed in a computing environment, the resource configured to generate a local log on a disk of the resource; periodically fetching the local log from the disk, the local log including a plurality of event records written by at least a software application executed on the resource; applying a control on the fetched local log; and detecting a cybersecurity issue on the resource based on a result of applying the control.

A targeted event monitoring and alert service may be implemented by a computing system. The system can provide a live dashboard on a graphical user interface for a respective user. The live dashboard can provide (i) real-time, localized contextual information of an event for a location corresponding to the respective user, and (ii) a set of real-time risks to the property of the respective user. The computing system executes an engagement monitor comprising a machine learning computer model to (i) monitor the respective user's interactions with the live dashboard, and (ii) dynamically adapt content flows of the live dashboard based on learned response information corresponding to the respective user's interactions with the targeted event monitoring and alert service.

A computing system comprising one or more computing devices can access a file comprising documentation that includes a textual description and a source code snippet written to comply with a programming language syntax. The computing system can identify the source code snippet. The computing system can generate an executable based on the source code snippet. The computing system can initiate a test process that accesses the executable, the test process causing the executable to cause an event. The computing system can determine that the event is a valid event or an invalid event.

Embodiments include methods and devices for media application function (AF) exposure functionality. Various aspects may include receiving an access configuration message for a service or application provider, the access configuration message including an indication of one or more event types and parameters, one or more exposure levels, and aggregation rules. Various embodiments may include an AF server subscribing other computing devices to event types and parameters for the service or application based on the exposure level of the other computing device.

The invention provides a load diagnosis and processing method and device, electronic equipment, a medium and a program product, which can be applied to the technical field of cloud computing. The method comprises the following steps: acquiring a performance index sequence of at least one candidate application process, and in response to meeting a high-load triggering condition, determining a target process to be diagnosed and generating an abnormal event identifier; obtaining thread resource occupation information of the target process in response to the abnormal event identifier, determining an abnormal thread based on the thread resource occupation information, and obtaining execution stack information corresponding to the abnormal thread; thread features are extracted based on the thread resource occupation information and the execution stack information, and a root cause classification label is determined based on the thread features; and generating processing suggestion information for the target application process based on the root cause classification label.

The invention belongs to the technical field of artificial intelligence middleware and cloud computing task arrangement, and discloses an agent arrangement system and method based on a graph structure and a response type programming technology. Comprising a graph structure construction and compiling unit, a node operation unit, a routing and confluence control unit, a graph signal and event management unit and a response type operation and execution regulation and control unit, and an execution path has integrity and verifiability before deployment through analysis of a service graph, topology verification and confluence supplementation of parallel branches; the nodes trigger execution according to graph signals and write products into a result data area or a shared data area, and the routing and confluence control determines a convergence mode of signal transmission and parallel branches according to a connection predicate and a confluence rule. The event management unit generates a structured event for a node operation state, and the operation regulation and control unit schedules through a leading edge queue and in-transit counting and feeds back a back pressure signal to the upstream when a load changes, so that stable propulsion of a graph structure is realized.

Embodiments are directed to security analysis agents. Events associated with a computing environment may be provided. Prompt fragments may be determined based on the events. A prompt may be generated for a large language model (LLM) based on a prompt template and the prompt fragments such that the prompt fragments may be included in the prompt and provided to the LLM. Actions for evaluating the events may be determined based on the LLM response. These actions may be executed to evaluate the events. Portions of the response that correspond to the prompt fragments may be determined. A performance score may be determined for each prompt fragment based on its corresponding portion of the response such that the prompt may be modified to exclude a portion of the prompt fragments that have a performance score less than a threshold value.

A system and method for cross-entity correlation of software containers in a cloud computing environment is presented. The method includes detecting in a log an event record related to a software container platform utilized in a cloud computing environment; extracting from the event record a software container identifier and a principal identifier; cross-referencing the software container identifier with a unique software container identifier list based at least on the principal identifier; associating in a security database the event record and the unique software container identifier; and applying a control based on the event record and the unique software container identifier.

A computer-implemented method for a digital security system receives unlabeled event data associated with a computing environment, clusters via an unsupervised machine learning model the unlabeled event data into clusters of unlabeled event data where unlabeled event data in one cluster are more similar to each other than to unlabeled event data in other clusters, selects a respective subset of unlabeled event data for each cluster of unlabeled event data, translates via a large language model artificial neural network each unlabeled event datum in each respective subset of unlabeled event data into a description for the unlabeled event datum, and applies a label via a labeling algorithm to at least one unlabeled event datum in a respective cluster responsive to and representative of the respective description for the unlabeled event datum in the respective subset, thereby transforming the at least one unlabeled event datum to a labeled event datum.

Aspects related to reducing application carbon footprints using predictive load balancing and adaptive process scaling are provided. An adaptive scaling platform may train a delegation model to output event pools based on input of event processing traffic information. The platform may receive event processing traffic information. The platform may generate an event pool using the delegation model. The event pool may comprise tasks required to fulfill event processing requests corresponding to the event processing traffic information and indicators of applications corresponding to the plurality of tasks. The platform may receive event processing information from a server. The platform may cause, based on the event processing information, an application to identify thread requirements. The computing platform may allocate resources based on the thread requirements. The computing platform may delegate tasks to the threads based on the allocating. The platform may update the delegation model based on monitoring the threads.

The invention provides a game server state monitoring method and system based on cloud computing, and the method comprises the steps: obtaining a player interaction event record and a server resource consumption index of a game server cluster in a preset monitoring period, constructing a multi-dimensional data association structure, carrying out the behavior mode analysis of the player interaction event record, and carrying out the analysis of the behavior mode. Generating a virtual economic flow map and scene interaction thermodynamic distribution, and performing feature extraction on server resource consumption indexes in the virtual economic flow map and the scene interaction thermodynamic distribution to obtain fragmentation features of a memory access sequence and response delay features of a network session; inputting the virtual economic flow atlas, the scene interaction thermodynamic distribution and fragmentation features and the response delay features into a pre-constructed cross-dimension correlation model to perform server load state evaluation, generating a load state evaluation result, generating a resource scheduling instruction according to resource bottleneck positioning information in the load state evaluation result, and performing resource scheduling on the server according to the resource scheduling instruction. And adjusting a computing resource allocation strategy of the game server cluster. According to the method and the device, the accuracy and the effectiveness of server resource scheduling are effectively improved.

A computer-implemented method may be used to establish trustworthiness of an Edge Computing Device. The method may include, at one or more storage devices, storing expected values pertaining to aspects of the Edge Computing Device. The method may further include, at one or more hardware processing devices, receiving the expected values, receiving an Event Log from the Edge Computing Device, extracting actual values from the Event Log, and comparing the actual values with the expected values to determine whether the actual values match the expected values. The method may further include, at one or more communication devices, responsive to determining whether the actual values match the expected values, transmitting an indication of whether the Edge Computing Device is trustworthy.

Aspects of the present disclosure are directed to systems and methods of dynamically generating individualized or personalized times for providing messages over networked environments. The computing system may obtain, for a user device, an event dataset identifying a plurality of interaction times corresponding to a plurality of interactions over an instant time window by a user with an application on the user device to address a condition of the user. The computing system may apply the event dataset to a machine learning (ML) model. The computing system may generate based on applying the event dataset to the ML model, a defined time at which a message is to be provided to the user device during a subsequent time window. The computing system may provide for presentation on the user device to address the condition of the user, the message in accordance with the defined time.

The invention belongs to the field of GPU computing performance testing, and provides a GPU flow scheduling performance measurement method and device.The method comprises the steps that in a pre-established basic execution environment, N null-kernel functions are submitted to a reference flow in sequence, and the reference flow executes the received null-kernel functions and measures the reference execution duration; alternately submitting the N null kernel functions to the first test flow and the second test flow, introducing a cross-flow synchronization event, and measuring the total execution duration; and according to the reference execution duration and the total execution duration, calculating to obtain synchronization time consumption between the streams. According to the method, the influence of a tracking tool and the like on the performance of the measured object is eliminated, the method has obvious advantages in accuracy and stability, high-precision and low-interference GPU scheduling performance measurement is realized, and a quantitative basis is provided for performance optimization of a system during GPU driving and operation.

A method for transmitting event data (21), in particular operator event data and / or status event data, from a field device (2) to a remote computing unit (3), comprising the steps of: generating the event data (21) by means of the field device (2) based on an event; providing an availability signal (22b) indicating the availability of the event data (21) for transmission (32) via a digital communication interface (15) to the remote computing unit (3); and providing the event data (21) for transmission via the digital communication interface (15) to the remote computing unit (3), wherein the event data (21) is provided separately from the availability signal (22b). The method comprises a computer program product, a field device (2) for executing the foregoing method, and a system (1) with the field device (2).

Systems and methods are provided for receiving a selection to add an event invite media overlay to a media content item, receiving content to be added to the event invite media overlay, the content corresponding to an event, and adding to the event invite media overlay, the content corresponding to the event to generate a custom event invite media overlay. The systems and methods further comprise causing display of the custom event invite media overlay on the media content item, receiving at least one user to which to send an invite to the event, and sending, to a second computing device associated with the at least one user, an invite to the event, the invite comprising the custom event invite media overlay and the media content item.

The embodiment of the application relates to the computer technical field, and provides a task execution log processing method and system and a computing device, the method is applied to the computing device carrying an operation and maintenance system, the method comprises the following steps: in response to that a task execution request is received by an execution engine unit, a callback event in a task execution process is monitored through a log processing plug-in; when the callback event is monitored, task execution data is recorded; the task execution data is encapsulated as structured log information; the structured log information is output in real time through a log output unit to replace output content of the log output unit. Based on the scheme, the computing device can embed the log processing plug-in in the operation and maintenance system, capture the callback event generated in the task execution process in real time, obtain the data in the task execution process as the log in the task running, so that the operation and maintenance system can realize the real-time structured output of the log without modifying the architecture, and blind areas of the operation and maintenance system in the task execution process are avoided.

Detection of anomalous computing environment behavior using glucose is described. An anomaly detection system receives glucose measurements and event records during a first time period. Missing events that are missing from the event records during the first time period are identified by processing the glucose measurements using an event engine simulator. An anomaly detection model is generated based on the missing events during the first time period. Subsequently, the anomaly detection system receives additional glucose measurements and additional event records during a second time period. Missing events that are missing from the additional event records during the second time period are identified by processing the additional glucose measurements using the event engine simulator. Anomalous behavior is detected if the identified missing events that are missing from the event records during the second time period are outside a predicted range of missing events of the anomaly detection model.

The present disclosure relates to and provides a method and apparatus for pushing events, and a computing device, a computer-readable storage medium and a computer program product. The method comprises: on the basis of a target model, determining a category of relationship between a first event and each event in a set of candidate events, wherein the category of relationship at least comprises one of identical, similar and unrelated. The method further comprises: on the basis of the determined category of relationship, using the first event to update the set of candidate events. The method further comprises: on the basis of the updated set of candidate events, pushing a plurality of recommended events.

A method to deploy a plurality of event-driven application components of an event-driven application in a distributed computing environment is described. The method includes automatically analyzing application source code of the event-driven application, using one or more processors, to identify relationships between the plurality of event-driven application components. Thereafter, a set of rules are applied to, based on the automatic analysis, generate assignment data recording assignments of event-driven application components to a plurality of computational nodes in the distributed computing environment. The set of rules is also applied to determine component requirements for each of the plurality of event-driven application components required to support execution at an assigned computational node in the distributed computing environment.

In one example, a system can operate an event ingest valve to pause event streaming to an event processing application executing on a first node of a distributed computing environment. The system can then access state data, of the event processing application, stored in a local memory of the first node. The system can generate a snapshot of the state data of the event processing application. After generating the snapshot, the system can shut down the event processing application on the first node. The system can then provide the snapshot to a second node of the distributed computing environment, the second node being configured to start the event processing application using the state data. After the event processing application is started on the second node, the system can operate the event ingest valve to resume the event streaming to the event processing application executing on the second node.

Aspects of the present disclosure are directed to systems, methods, and computer readable media for executing actions for events associated with use of applications. A computing system can identify data associated with an application to be evaluated for at least one of a plurality of events associated with a use of the application. The computing system can determine, based on applying the free text to a machine learning (ML) architecture, a value indicating a likelihood of occurrence of an event associated with the use of the application. The computing system can provide to a generative ML model, a model input based on the free data and the value. The computing system can execute an action corresponding to characterizing the event.

A computing platform may receive event processing requests. The computing platform may integrate, into the event processing requests, domain knowledge. The computing platform may reduce, using temporal sequence compression, the integrated event processing requests into high-signal segments. The computing platform may generate, using capsule-based representation learning, capsule structures representing the high-signal segments. The computing platform may access at least one unauthorized activity detection model. The computing platform may analyze, using the at least one unauthorized activity detection model, the event processing requests, which may include selectively retrieving, for the at least one unauthorized activity detection model and using contextual indexing, a subset of relevant capsule structures from the capsule structures. The computing platform may output, using the at least one unauthorized activity detection model, a unauthorized activity output for the event processing requests. The computing platform may initiate, based on the unauthorized activity output, one or more security actions.

Systems and methods are provided for receiving a selection to add an event invite media overlay to a media content item, receiving content to be added to the event invite media overlay, the content corresponding to an event, and adding to the event invite media overlay, the content corresponding to the event to generate a custom event invite media overlay. The systems and methods further comprise causing display of the custom event invite media overlay on the media content item, receiving at least one user to which to send an invite to the event, and sending, to a second computing device associated with the at least one user, an invite to the event, the invite comprising the custom event invite media overlay and the media content item.

A computing platform may receive, from an initiating user device, an event processing request. The computing platform may generate, using RTI, a first 3D representation of the event processing request. The computing platform may send, to a recipient user device, RTI transformation information indicating transformation actions to be performed, using RTI, to produce a second 3D representation of the event processing request. The computing platform may generate, using the first 3D representation and the second 3D representation, a complete 3D representation. The computing platform identify whether the complete 3D representation is validated. Based on identifying that the complete 3D representation is validated, process the event processing request.

Certain aspects provide techniques and apparatus for executing queries in a computing system using machine learning models. An example method generally includes receiving a plan to satisfy a request in the computing system and event log data associated with execution of the plan. The plan generally specifies a first plurality of actions to be performed by the computing system at a first level of granularity. Using a plan refinement machine learning model, a refined plan is generated when the event log data indicates that execution of the generated plan results in one or more execution errors and the one or more execution errors are solvable. Generally, the refined plan specifies a second plurality of actions to be performed by the computing system at a second level of granularity, the second level of granularity being finer than the first level of granularity.