Abstract: A server receives a first hypertext transfer protocol (HTTP) from a client device that requests first and second items of content associated with a webpage and applies a defer directive to the second item. The server retrieves, from a database, via a single worker thread, first data associated with the first item and transmits a first message comprising the first data associated with the first item. The server retrieves, from the database, via the worker thread, second data associated with the deferred item and transmits a second message comprising the second data associated with the second item.

Abstract: A source computational instance may be configured to operate a software application and store a first set of content related to the software application. A target computational instance may be configured to operate the software application and store a second set of content related to the software application. The software application operated by the target computational instance may apply transformation rules to received content before storing the received content in the second set of content. The source computational instance may be further configured to receive selected content chosen from the first set of content and an indication of the target computational instance, and transmit the selected content to the target computational instance. The target computational instance may be further configured to receive the selected content, apply the transformation rules to the selected content, and store the selected content within the second set of content.

Abstract: An embodiment may involve: transmitting, by a non-production computational instance and to a central computational system, a configuration for a service provided by the central computational system, wherein the non-production computational instance is arranged to test the configuration; appending, to the configuration at the non-production computational instance, a synchronization identifier to indicate that the configuration has been synchronized with the central computational system; receiving, by a production computational instance and from the non-production computational instance, a copy of the configuration; reading, by the production computational instance, the synchronization identifier from the copy of the configuration; determining that the synchronization identifier is not reflected as part of a synchronization history maintained at the production computational instance; and, in response to determining that the synchronization identifier is not reflected in the synchronization history, transmitting

Abstract: At least service task data and application utilization data are collected. The collected data is analyzed to determine a utilization pattern. Based on the determined utilization pattern, a computer automation opportunity is automatically identified. An automatically generated recommendation of an automation solution among a plurality of automation solution candidates is provided for the identified computer automation opportunity.

Abstract: An embodiment may involve a plurality of configuration items and an unmatched configuration item, wherein the unmatched configuration item is associated with a first set of attribute values and a first vulnerability, wherein the first vulnerability is associated with a first set of field values.

Abstract: An embodiment may involve a database containing a first user profile that specifies a first preferred language of a first user and a second user profile that specifies a second preferred language of a second user. The embodiment may also involve one or more processors configured to: receive, from the first user and within a chat session, a first set of messages in the first preferred language; cause the first set of messages to be translated into the second preferred language; provide, to the second user and within the chat session, the first set of messages as translated; receive, from the second user and within the chat session, a second set of messages in the second preferred language; cause the second set of messages to be translated into the first preferred language; and provide, to the first user and within the chat session, the second set of messages as translated.

Abstract: A request to perform a prediction using a machine learning model of a specific entity is received. A specific security key for the machine learning model of the specific entity is received. At least a portion of the machine learning model is obtained from a multi-tenant machine learning model storage. The machine learning model is unlocked using the specific security key and the requested prediction is performed. A result of the prediction is provided from a prediction server.

Abstract: A software architecture within a public cloud network may include units of: (i) a plurality of computational instances respectively related to managed networks, (ii) a plurality of servers configurable as load simulators, (iii) administrative components configured to deploy and update the software architecture, and (iv) shared infrastructure services, wherein the units of the software architecture are implemented on virtual machines of the public cloud network and are connected to but logically isolated from one another by way of different access controls or policies. A provider network, coupled to the software architecture by way of network gateways within the shared infrastructure services, may be configured to deliver the configuration, software packages, and database schema to the infrastructure-as-code platform.

Abstract: An embodiment involves persistent storage and one or more processors. The persistent storage may contain a plurality of hardware models specifying types of computing devices, wherein the hardware models include attributes representing manufacturer names, product names, and model numbers of the computing devices. The processors may be configured to: obtain, from the persistent storage, a hardware model; calculate a hash value over attributes of the hardware model; compare the hash value to hash values in a curated list, the curated list including normalized hardware models that map the hash values to normalized manufacturer names, normalized product names, or normalized model numbers; determine that the hash value matches a particular hash value for a normalized hardware model; and update, in the persistent storage, the hardware model to include at least one of a normalized manufacturer name, a normalized product name, or a normalized model number associated with the normalized hardware model.

Abstract: An embodiment may involve persistent storage containing a machine learning trainer application configured to apply one or more learning algorithms. One or more processors may be configured to: obtain alert data from one or more computing systems; generate training vectors from the alert data, wherein elements within each of the training vectors include: results of a set of statistics applied to the alert data for a particular computing system of the one or more computing systems, and an indication of whether the particular computing system is expected to fail given its alert data; train, using the machine learning trainer application and the training vectors, a machine learning model, wherein the machine learning model is configured to predict failure of a further computing system based on operational alert data obtained from the further computing system; and deploy the machine learning model for production use.

Abstract: A computing system includes persistent storage configured to store representations of software applications installed on computing devices, and a software application configured to perform operations, including retrieving, from the persistent storage, a first plurality of representations of a first plurality of software applications installed on a particular computing device and a second plurality of representations of a second plurality of software applications installed on a reference computing device. The operations also include determining a device fingerprint of the particular computing device based on the first plurality of representations and a reference device fingerprint of the reference computing device based on the second plurality of representations, and comparing the device fingerprint to the reference device fingerprint.

Abstract: 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.

Abstract: Machine generated data entries are received. The machine generated data entries are clustered into a plurality of different clusters that each includes a different subset of the received machine generated data entries. For each of the plurality of different clusters, content of the corresponding subset of the received machine generated data entries belonging to the corresponding cluster of the plurality of different clusters is analyzed to determine a corresponding pattern of the corresponding cluster. A new machine generated data entry is received. The new machine generated data entry is matched to one of the determined patterns. The new machine generated data entry is assigned to one of the plurality of different clusters corresponding to the matched pattern.

Abstract: Content to be summarized is received and analyzed using an extractive summarizer to determine a reference extractive summary of the content. The content is further analyzed using a plurality of different abstractive summarizers to determine candidate abstractive summaries of the content. Each of the candidate abstractive summaries is compared with the reference extractive summary to determine corresponding evaluation metrics. Based at least in part on the evaluation metrics, one of the candidate abstractive summaries is selected as a selected summary to be provided.

Abstract: Systems and method for improving query performance by querying an appropriate database engine based on the operation of the query request is provided. In one aspect, this approach involves querying a row-oriented database, querying a column-oriented database, or blacklisting the query request. In particular, updating the column-oriented database involves delete and insert operations. By maintaining updated databases and querying appropriate database engines, the response time of a query request may be improved.

Abstract: A computational instance may contain a plurality of vulnerable items and a plurality of change requests, wherein at least some of the vulnerable items are assigned to a vulnerability group, the vulnerable items represent hardware or software components that exhibit known vulnerabilities, and the change requests represent additions, removals, or modifications of the hardware or software components. The computational instance may further include one or more computing devices configured to: generate a representation of a first graphical user interface containing data related to the vulnerability group, generate a representation of a second graphical user interface that allows specification of change request options, generate a representation of a third graphical user interface with data entry fields for a corresponding change request that are auto-populated based on the data related to the vulnerability group, and add a corresponding change request to the plurality of change requests.

Abstract: A plurality of session signatures and a plurality of inference flags are stored in association with each other, each session signature representing a combination of flag values, each flag value signifying a key event. A plurality of session events are logged, each session event issuing from one of the remote client devices and having a corresponding session ID and associated data. The session events are grouped by their corresponding session IDs to create a plurality of session records, each session record including flag values for a plurality of key events. A session signature is identified for each session record from the stored session signatures based on the key event flag values of the session record, and a value of an inference flag associated with the identified session signature is obtained from a storage device. A predetermined operation is performed based on the obtained value of the inference flag.

Abstract: Disclosed herein are applications and platforms for streamlined production of charts and reports from databases containing time series datasets and/or timestamped data. The applications described herein may employ dynamically retrieve data from data centers to quickly provide options to the user. The platform further allows configuration of the charts and/or reports by providing lists of metrics that may be available in the selected data, and lists of transforms that may be applied to the data. The application may employ dynamic user interfaces, which provide processing of the received time series datasets in a data agnostic manner. As a result, the systems and methods described herein may allow generating charts and reports using a streamlined workflow.

Abstract: An embodiment may involve a server-side log collected by a server device, where the server-side log includes a set of entries indicating a unique identifier, and wherein the unique identifier is assigned to a work item of a server-based application executed by the server device. The embodiment may further involve one or more processors configured to: receive, from a client device disposed upon a network, a client-side log, wherein the client-side log includes operational data related to usage of a client-based application executed by the client device; identify, from the operational data, the client-based application and one or more activities performed by the client-based application; determine that the one or more activities are related to the unique identifier; based on the one or more activities, determine an action that can be taken to improve efficacy of the server-based application; and write, to the persistent storage, a representation of the action.

Abstract: A modified configuration management database (CMDB) system is disclosed in which detected configuration item (CI) vulnerabilities are stored as less-resource-intensive detection objects, rather than as more-resource-intensive vulnerable item (VI) objects. The system includes a vulnerability response (VR) server that enables promotion rules to be created and periodically applied to the stored detections. When the conditions of a detection promotion rule are satisfied by information relating to a stored detection, a new VI may be created and related to the stored detection within the CMDB. The disclosed promotion rules can be configured to ensure that VIs are only automatically created for high-risk or relevant detections, substantially reducing the number of VIs to be created, stored, and managed. As such, the disclosed system substantially reduces resource consumption and improves the efficiency and operation of the CMDB.