Abstract: An example embodiment may involve: obtaining a representation of an access control list (ACL), wherein the ACL includes an entry that defines user capabilities with respect to a computing resource; determining a user class based on the entry and one or more rules, wherein the one or more rules are based on whether the computing resource is a database table for a task-based application, and wherein the one or more rules are based on whether the computing resource is read accessible or write accessible; and providing, for display on a graphical user interface, an indication of the user class.

Abstract: Metrics that characterize one or more computing devices are received. A value associated with a performance of the one or more computing devices based on the received metrics is determined. A first scheduling parameter based on the value is determined, wherein the first scheduling parameter is associated with a first discovery process that is associated with at least a portion of the one or more computing devices. Execution of the first discovery process is directed according to the first scheduling parameter.

Abstract: Origin text content to be analyzed using natural language processing is received. A two-dimensional item sequence representation for at least a portion of the received origin text content is generated. Using one or more processors, one or more evaluation metrics are determined based on an analysis of the two-dimensional item sequence representation. A reduced version of the origin text content is automatically generated based on the one or more evaluation metrics to assist in satisfying a constraint of a natural language processing model. The reduced version of the origin text content is used as an input to the natural language processing model.

Abstract: Configuration management persistent storage contains entries representing configuration items and relationships between pairs of the configuration items. Event management persistent storage contains entries representing alert records. Change request persistent storage contains change records respectively referring to changes made to the configuration items.

Abstract: A system could include persistent storage containing application components. A plurality of software applications could be installed on the system. The software applications could be respectively associated context records that include references to application components that provide some behavior or data for the software applications. The system could also include processors configured to perform operations. The operations could include receiving a request to generate a topology map for a software application and identifying, based on a context record for the software application, a subset of application components that provide some behavior or data for the software application. The operations could further include determining relationship types between pairs of application components and generating a topology map for the software application.

Abstract: An example embodiment may involve persistent storage containing definitions of (i) assignments of bots to endpoints, (ii) software packages for execution by the bots, (iii) schedules for the bots to execute the software packages, and (iv) processes that associate the bots with the software packages and the schedules. This embodiment may also involve processors configured to: receive, from a computing device, a request for bot deployment, wherein the computing device includes a bot runtime; identify, in the processes, a bot assigned to an endpoint that is associated with the computing device, wherein the bot is associated with a software package and a schedule; and transmit, to the computing device, data including a representation of the bot, a copy of the software package, and a copy of the schedule, wherein reception of the data causes the bot to execute, using the bot runtime, the software package in accordance with the schedule.

Abstract: A natural language understanding (NLU) framework includes a lookup source framework, which enables a lookup source system to be defined having one or more lookup sources. Each lookup source of the lookup source system includes a respective source data representation that is compiled from respective source data. For example, a source data representation may include source data arranged in a finite state transducer (IFST) structure as a set of finite-state automata (FSA) states, wherein each state is associated with a token that represents underlying source data. Different producers can be applied during compilation of a source data representation to derive additional states within the source data representation from the source data. Certain states of the source data representation that contain sensitive data can be selectively protected through encryption and/or obfuscation, while other portions of the source data representation that are not sensitive may remain in clear-text form.

Abstract: In various embodiments, a process for determining metrics including resource expenditures of a digital service includes discovering a plurality of configuration items of a computing infrastructure. The process includes identifying a subset of the plurality of configuration items utilized to provide a digital service, obtaining a plurality of resource expenditures respectively associated with at least a portion of the plurality of configuration items, and associating a subset of the plurality of resource expenditures with the subset of the plurality of configuration items. The process includes aggregating the subset of the plurality of resource expenditures to generate a metric of the digital service.

Abstract: An embodiment may involve a database including a table, wherein the table contains database records and a schema by which the database records are arranged. The embodiment may further involve one or more processors configured to: obtain a query that references the table and one or more elements of the schema; copy at least part of the table into a second table; transform, using a deterministic one-way function, the schema of the second table and the database records within the second table; transform, using the deterministic one-way function, the query into a second query; and write, to memory, at least part of the second table and the second query.

Abstract: A controller computing device may include one or more processors and memory containing controller data representing controller computing device capabilities. The one or more processors may be configured to transmit on a first instance of a request-response protocol, a controller request, including the controller data, to an agent computing device. The controller computing device may then receive, from the agent computing device, an agent request on a second instance of the request-response protocol. The agent request may include agent data representing agent computing device capabilities. The controller computing device may store the agent data in the memory, and transmit on the second instance of the request-response protocol, to the agent computing device, a controller response acknowledging receipt of the agent request.

Abstract: A method includes obtaining a topic of a document and an information source associated with the document. The method also includes generating, using a generative machine learning (ML) model, the document based on the topic and the information source. The method additionally includes identifying a query that is associated with the topic and determining, using a validation model, that the query is not addressed by the document. The method yet additionally includes, based on determining that the query is not addressed by the document, generating an updated document using the generative ML model based on the topic, the information source, and the query. The method further includes determining, using the validation model, that the query is addressed by the updated document, and outputting the updated document.

Abstract: Systems and methods are provided for dynamic selection of anomaly detection options for particular metric data. Metric data corresponding to one or more configuration items of an information technology (IT) infrastructure is collected. A selected anomaly detection action option that applies to the metric data is identified. An action is performed using the metric data, based upon the selected anomaly detection action option. A dashboard graphical user interface (GUI) display results of the action.

Abstract: Embodiments are provided that include generating, by an inter-modal predictor, predicted second-mode information for a first text-based record based on first-mode information of the first text-based record. Embodiments also include generating a first value by evaluating a first loss function that is based on a first difference between the second-mode information of the first text-based record as predicted and as observed; updating the inter-modal predictor based on the first value; generating, by the updated inter-modal predictor, predicted second-mode information for a second text-based record in the database based on first-mode information of the second text-based record; generating a second value by evaluating a second loss function that is based on a second difference between the second-mode information of the second text-based record as predicted and as observed; and training an operational model based on the second value.

Abstract: An example embodiment may involve a main database; a main memory; and one or more processors configured to: retrieve, by a data collector application, records from the main database, wherein the data collector application includes an embedded database; aggregate, by the data collector application, values in the records relating to a key performance indicator (KPI) to form partial KPI data stored in one or more blocks of the main memory; determine, by the data collector application, that utilization of the main memory exceeds a pre-defined threshold; in response to the utilization of the main memory exceeding the pre-defined threshold, write, by the data collector application, the partial KPI data to a row of the embedded database; and release, by the data collector application, the one or more blocks of the main memory used to store the partial KPI data.

Abstract: Example embodiments relate to parallelized exception handling for large datasets. One example embodiment includes a method. The method includes retrieving one or more sets of entries to be analyzed. The method also includes selecting an available computing resource. In addition, the method includes causing the available computing resource to perform an analysis of each entry within the respective set of entries to identify previously defined exceptions. Further, the method includes causing, for each entry within the respective set of entries, a list of exceptions associated with the respective entry to be created or updated based on the analysis. Additionally, the method includes causing, for each entry within the respective set of entries, metadata associated with the respective entry to be updated when the list of exceptions associated with the respective entry indicates that no exceptions are associated with the respective entry.

Abstract: The present disclosure relates to a data augmentation system and method that uses a large pre-trained encoder language model to generate new, useful intent samples from existing intent samples without fine-tuning. In certain embodiments, for a given class (intent), a limited number of sample utterances of a seed intent classification dataset may be concatenated and provided as input to the encoder language model, which may generate new sample utterances for the given class (intent). Additionally, when the augmented dataset is used to fine-tune an encoder language model of an intent classifier, this technique improves the performance of the intent classifier.

Abstract: Persistent storage may contain a list of discovery commands, the discovery commands respectively associated with lists of network addresses. A discovery validation application, when executed by one or more processors, may be configured to: read, from the persistent storage, the list of discovery commands and the lists of network addresses; for each discovery command in the list of discovery commands, transmit, by way of one or more proxy servers deployed external to the system, the discovery command to each network address in the respectively associated list of network addresses; receive, by way of the one or more proxy servers, discovery results respectively corresponding to each of the discovery commands that were transmitted, wherein the discovery results either indicate success or failure of the discovery commands; and write, to the persistent storage, the discovery results.

Abstract: An online cloud application platform for global navigation of multiple online cloud-based applications is provided. The online cloud application platform is configured to provide a plurality of online cloud-based applications. While providing a current application among the plurality of online cloud-based applications, an event identifying a function request decoupled from a static navigation route is received via a cross-application routing handler. Based on a set of dynamically modifiable configuration data of the online cloud application platform, which application among the plurality of online cloud-based applications to handle the function request is dynamically determined. Based on the set of dynamically modifiable configuration data of the online cloud application platform, a corresponding dynamically determined cross-application navigation route to handle the event is determined.

Abstract: Configuration item data from information technology resources of an air-gapped network are collected for an information technology configuration management database. The collected configuration item data is filtered using a specified item data property including by automatically identifying a collected information of interest about a discovered information technology asset among the collected configuration item data and irreversibly modifying the collected information of interest about the discovered information technology asset included in the collected configuration item data. Based on a type of content that has been modified in the collected information of interest, a new automatic rule indicating the type of content to be avoided during a future data collection is determined. At least a portion of the filtered collected configuration item data is stored on a portable physical storage medium within the air-gapped network.

Abstract: A method includes obtaining a discovery pattern that indicates a plurality of operations associated with a corresponding computing resource type of a plurality of computing resource types. The method also includes identifying a variable parameter value associated with execution of the discovery pattern with respect to a computing resource of the corresponding computing resource type, and determining an error value by using a machine learning model to process the variable parameter value. The error value indicates a likelihood that execution of the discovery pattern, when associated with the variable parameter value, with respect to the particular computing resource results in a corresponding error type. The method further includes receiving, based on the error value, an input comprising one or more of (i) an instruction to execute the discovery pattern or (ii) a modification applicable to the variable parameter value.