Abstract: An embodiment is a method performed by one or more electronic device to manage deployment of a configuration object for a machine learning application in a multi-tenant on-demand model serving infrastructure. The method includes receiving an indication that a configuration object for the machine learning application is ready to be deployed, obtaining the configuration object for the machine learning application in response to receiving the indication, and causing a router component to instantiate a router instance for the machine learning application, wherein the router component instantiates the router instance for the machine learning application based on the configuration object for the machine learning application and the router instance for the machine learning application is configured to route incoming scoring requests associated with the machine learning application to a scoring container that provides scoring functionality for the machine learning application.

Abstract: Techniques are disclosed for processing record writes in a database system having a log owner and one or more log tailers. The log owner maintains a transaction log, and the log tailers replay the transaction log to recreate a state of the log owner. A log tailer may receive a write request to perform a record write operation to write a base record to a database as part of a database transaction. The log tailer may determine additional records to write as a result of having to write the base record. The log tailer buffers the base record and additional records in a memory buffer and issues them as a single batch to the log owner to update the transaction log. The log owner may update the transaction log and return a response to the log tailer that indicates whether the log owner successfully updated the transaction log.

Abstract: Embodiments described herein provide A method for training a neural network based model. The methods include receiving a training dataset with a plurality of training samples, and those samples are encoded into representations in feature space. A positive sample is determined from the raining dataset based on a relationship between the given query and the positive sample in feature space. For a given query, a positive sample from the training dataset is selected based on a relationship between the given query and the positive sample in a feature space. One or more negative samples from the training dataset that are within a reconfigurable distance to the positive sample in the feature space are selected, and a loss is computed based on the positive sample and the one or more negative samples. The neural network is trained based on the loss.

Abstract: A computing system may process entity name variations. The entity name variations may be transformed into vector representations. The vector representations may be stored in a database. The entity name variations may be clustered into groups of similar entity name variations based on a similarity metric between the vector representations. A canonical identifier for each group of similar entity name variations may be stored in the database. Each canonical identifier identifying a distinct entity.

Abstract: A computing device displays first data describing a dataset. At least a portion of the first data is encoded with metadata that links the first data to data values and/or data fields of the dataset. The computing device receives a user interaction with a first affordance. The user interaction specifies a first portion of the first data, which includes at least a first data field of the dataset. In response to receiving the user interaction, the computing device retrieves metadata corresponding to the first portion of the first data, and generates second data describing the dataset according to (i) the at least the first data field and (ii) data values of the at least the first data field specified in the metadata, corresponding to the first portion of the first data. The computing device concurrently displays the first data and the second data describing the dataset.

Abstract: Systems, devices, and techniques are disclosed for transaction coordination across services. A service including virtual threads receives a transaction that is part of a multi-part transaction. The service determines the transaction does not already exist in transaction state data. The service updates the status of the transaction in the transaction state data to pending. The service processes the transaction. The service updates the status of the transaction in the transaction state data to completed. The service uses receives at and endpoint a request for the status of the transaction. The service responds to the request for the status of the transaction. The service sends a request for status to an endpoint another service that is processing another transaction of the multi-part transaction. The service receives a response from the another service of a status of completed.

Abstract: A computer services environment may include web servers providing access domains and a network ingress paths receiving application-layer request messages. The application-layer request messages may each be received from a respective source via a respective ingress path and may be directed to a domain. The computing services environment may also include an orchestration engine configured to determine and implement mitigation policies corresponding with the ingress paths based on a classification of a subset of the plurality of application-layer request messages as being sent from a subset of the sources associated with a distributed denial of service attack. The mitigation policies may include rules to prevent a subset of subsequent application-layer request messages from the subset of the sources from reaching one or more components of the computing services environment.

Abstract: A method for data processing is described. The method includes receiving an indication of a reference string from a cloud client of a content generation service. The method further includes generating multiple candidate strings associated with the reference string based on using a machine learning model to calculate similarity metrics between the reference string and the multiple candidate strings, where the machine learning model is trained using a dataset of annotated strings. The method further includes selecting a quantity of the candidate strings based on filtering the multiple candidate strings according to the similarity metrics. The method further includes causing the quantity of candidate strings to be displayed at the cloud client. The method further includes receiving feedback associated with the quantity of candidate strings and a selection of at least one candidate string displayed at the cloud client.

Abstract: Systems, methods, and devices are disclosed herein that provide security for requests sent to services in service meshes. A computing platform may be implemented using a server system. The computing platform is configurable to cause receiving a request from a service in a cloud-based computing environment, and identifying a chain of trust embedded in a portion of the request, the chain of trust being generated by one or more security entities in the cloud-based computing environment, the chain of trust identifying results of one or more security verification operations performed on the request. The computing platform is further configurable to cause determining if the chain of trust is a valid chain of trust based, at least in part, on one or more security policies, and sending the request to another entity in the cloud-based computing environment in response to determining the chain of trust is a valid chain of trust.

Abstract: Database systems and methods are provided for managing relationships between records in a database. An exemplary method involves identifying a virtual container for a first record at the database system based on one or more fields of the first record and updating the virtual container to associate the first record with one or more additional records. In response to an operation with respect to at least one of the records, the method binds an instance variable of a virtual container based on a relationship between respective values associated with the respective records, automatically initiate an action at the database system with respect to the one or more respective records satisfying the relationship constraint, and update a graphical user interface (GUI) display at a client device over a network to include graphical indication of the action at the database system responsive to the bound value for the instance variable.

Abstract: Techniques are described for securing secrets in software build workflows. In some implementations, build instructions call for execution of a first program module and a second program module, where the first program module has been approved to make a privileged request, but the second program module has not. The first program module can be stored in a trusted repository, separately from the second program module. When the first program module is loaded for execution, a cryptographic signature can be validated to determine that the first program module is authentic and as a condition for passing a privileged credential to the first program module. The second program module has no access to the privileged credential. Instead, when the second program module is loaded for execution, a determination can be made whether the second program module makes any privileged requests. Any privileged requests from the second program module will not be fulfilled.

Abstract: Methods, storage systems and computer program products implement embodiments of the present invention for data access that include identifying a set of tables in a database to be accessed by an application, and identifying first and second application programming interface (API) calls having different, respective access properties for accessing records in the tables via an API. Respective numbers of the records in the tables are counted by a processor, and the set of tables are partitioned into first and second subsets responsively to the respective numbers. The records in the first subset of the tables are accessed by having the application convey the first API call to the API, and the records in the second subset of the tables are accessed by having the application convey the second API call to the API.

Abstract: A computer-implemented method is disclosed for predicting a future usage of a cloud-based computing resource based on a previous usage of the resource by users, and predicting an anomaly event at the resource. The method also includes identifying a top contributing user responsible for the anomaly event, throttling an access of the top contributing user, evaluating a speed of data requests received from the top contributing user, and maintaining a utilization level of the resource within a predetermined target range. The method further includes dynamically controlling the speed of data requests based on the evaluation of the speed of data requests and a controlling speed of data request recommended by a first artificial intelligence model. The recommendations of the first artificial intelligence model may be validated by a human reasoning based model configured to monitor and mitigate a risk associated with a counter-intuitive recommendation of the first artificial intelligence model.

Abstract: Techniques are disclosed for managing metadata of a distributed database system in a hybrid manner. A computer system may receive, from a computing device, a request to access a set of data stored in nodes of a distributed storage system that is a caching layer of the system. The system retrieves metadata for a set of data specified in the request, including accessing a reversemap storing a reverse-ordered copy of original metadata stored in a metadata store of the system, where the reversemap is stored on durable storage of the system. Based on retrieving the metadata from the reversemap, the system accesses nodes of the distributed storage system, where the reversemap specifies the nodes of the distributed storage system that store the set of data. The system transmits, to the computing device, information indicating a result of accessing data stored in nodes of the distributed storage system.

Abstract: Techniques are disclosed for managing contention and improving transaction efficiency in distributed database systems through snapshot advancement. A system receives a query defining a first database transaction associated with a first snapshot of a database, where the transaction includes reading and updating a database value. The system evaluates the query against predefined criteria, such as accessing a single database row and deterministically locating the database value, to determine eligibility for snapshot advancement. Upon identifying contention over the database value between the first transaction and a second transaction, the system dynamically advances the transaction to a second snapshot of the database, avoiding a restart. This process involves granting snapshot advancement permission, waiting for a lock held by the second transaction to be released, and using the subsequent snapshot to continue execution.

Abstract: A multi-tenant system receives a request to build a software artifact for deploying on one or more target datacenter entities of the datacenter configured on the could platform. A software artifact comprises executable instructions associated with a service configured for execution on one or more cloud platforms. The system generates a release configuration that includes reusable release components and is used as a template by the release orchestration system to orchestrate release of the software artifact. The release configuration may specify one or more stagger groups, or a group of target datacenter entities that can be rolled-out in parallel.

Abstract: Using container information to select containers for executing models is described. A system receives a request from an application and identifies a version of a machine-learning model associated with the request. The system identifies a set of each serving container corresponding to the machine-learning model from a cluster of available serving containers associated with the version of the machine-learning model. The system selects a serving container from the set of each serving container corresponding to the machine-learning model. If the machine-learning model is not loaded in the serving container, the system loads the machine-learning model in the serving container. If the machine-learning model is loaded in the serving container, the system executes, in the serving container, the machine-learning model on behalf of the request. The system responds to the request based on executing the machine-learning model on behalf of the request.

Abstract: Techniques are disclosed relating to determining whether to utilize an alternative query plan for a subsequent execution of a database query. A computer system accesses a trace record detailing an execution of the database query in accordance with a first query plan selected by a query optimizer. The computer system may determine, based on the trace record, whether the execution of the database query satisfies a set of performance criteria. In response to determining that the execution of the database query does not satisfy the set of performance criteria, the computer system may determine whether one or more alternative query plans for the query include a second query plan that is predicted to be more performant than the first query plan. The computer system may provide an instruction to utilize the second query plan in place of the first query plan for the subsequent execution of the database query.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. A communication process flow management service may display a set of actions associated with a communication process flow configured to manage communications for a tenant. To update a segment of entities on which the communication process flow is to be executed, a user may navigate to a separate browser tab from that of the communication process flow management service, and may update the segment using an entity segmentation service. The communication process flow management service may receive an indication from an entity segmentation service that a segment of entities is updated and display a notification or a toast popup indicating the segment update to a user interface.

Abstract: A computing system may process entity name variations. The entity name variations may be transformed into vector representations. The vector representations may be stored in a database. The entity name variations may be clustered into groups of similar entity name variations based on a similarity metric between the vector representations. A canonical identifier for each group of similar entity name variations may be stored in the database. Each canonical identifier identifying a distinct entity.

Abstract: Techniques are disclosed relating to determining query plans for execution by database systems. In various embodiments, a query optimizer determines a first query plan to implement a query requesting data from a database system. The determining includes selecting one of a plurality of query plans evaluated based on a cost analysis and caching plan fragments of the unselected query plans. The database system can then determine a second query plan for the query by replacing a plan fragment in the first query plan with one of the cached plan fragments of the unselected query plans.

Abstract: A method and apparatus for autonomous configuration-based release orchestration. A first engine obtains stagger configuration data that includes an indication of container clusters in each stagger and a stagger order, selects a current stagger based on the order, and attempts to deploy the feature release to the current stagger by causing an app config update to be sent to a second engine within each container cluster of the current stagger, and receives an indication of success or failure of the attempted deployment of the feature release to the current stagger. Responsive to the indication of success or failure, the first engine performs one of a plurality of actions that include attempting to deploy the feature release to a next one of the staggers according to the order responsive to the indication indicating success, and causing a roll back of the current stagger responsive to the indication indicating failure.

Abstract: Techniques for providing a collaborative document in association with a virtual meeting on a communication platform are discussed herein. Users may communicate within the communication platform via text-based messages, which may include links, documents, videos, etc. Users may request a virtual meeting with audio and/or video capabilities. Responsive to a request, users within the communication channel may be invited to the virtual meeting and the collaborative document. The collaborative document can enable messaging and sharing functionalities attributable to a virtual space. The collaborative document may be populated with communications previously exchanged within the communication channel, such that links, documents, videos, content, etc. that occurred or were shared within the communication channel may be indicated within the collaborative document to provide context to the virtual meeting.

Abstract: Techniques and mechanisms described herein relate to a database system. A digital asset referenced in one or more database tables within an on-demand database system accessible to a plurality of entities may be identified. The digital asset may be owned by a first one of the plurality of entities. A smart contract that includes a token identifying the digital asset may be generated. The smart contract may be recorded within a distributed trust ledger. The token may be owned by a first account within the distributed trust ledger. The on-demand database system may include a correspondence linking the first entity with the first account. The on-demand database system may be updated to include a reference linking the digital asset with the token.

Abstract: Techniques are disclosed relating to database configuration settings overrides. In some embodiments, a database system stores a set of default configuration settings that control operation of the database system. The database system receives a query requesting data from the database system, and metadata about the query. The database system determines, based on the query and the metadata, that a configuration settings override has been specified for the query, where the configuration settings override indicates that one or more of the default configuration settings are to be replaced with one or more configuration settings specific to the query. In response to the determining that a configuration settings override has been specified, the database system executes the query using the one or more specific configuration settings.

Abstract: Apparatus and method for dynamic and persistent data sharing between cloud services. To address limitations in existing systems, resource provisioning and application deployment pipelines are configured with calls to share data using a configuration service. In some implementations, the configuration service includes an API, which is called by one or more stages of the resource provisioning pipeline to write configuration data in key-value pairs (e.g., data related to resource provisioning). One or more stages of the application deployment pipeline are configured with calls to access the configuration data. The stages then use the configuration data for application deployment. The configuration service manages the key-value store using versioning, repeatability, and immutability, to ensures that a configuration generated by any pipeline execution can be automatically re-used by another pipeline, at execution time.

Abstract: A computer system monitors for a change in a first data output generated by execution of a predefined dataflow. In accordance with a determination that the first data output has changed and the first data output meets triggering criteria, the computer system triggers execution of a predefined second dataflow distinct from the first dataflow. The execution of the second dataflow is dependent on the change in the first data output generated by the first dataflow. In accordance with a determination that the first data output has changed and the first data output does not meet triggering criteria, the computer system forgoes triggering execution of the second data flow. In accordance with a determination that the first data output has not changed, the computer system forgoes triggering execution of the second dataflow.

Abstract: Techniques described herein support component maintenance that accounts for a similarity between characteristics of different components. To identify components with similar characteristics, one or more techniques described herein support generation of a data structure (e.g., a tree) that represents a component, where each characteristic may be represented in a different leaf node. The system may generate a similarity score, or value (e.g., as a percentage), between multiple components based on comparing individual nodes of a tree representing each component, respectively. If the similarity score satisfies a threshold, then the system may display, at the UI, a message indicating to a user to preferentially implement one component over another.

Abstract: A conversational artificial intelligence (AI) point-of-sale (POS) system uses generative AI to help a store associate complete tasks in a retail environment. This POS system combines four technologies: (1) speech recognition; (2) natural language processing; (3) acting in response to verbal command in the context of a retail store and a POS device; and (4) providing bi-directional and fully conversational responses. The AI, which runs in an app on a smartphone, tablet, or other mobile device, uses a machine learning (ML) model, such as a small language model (SLM) or large language model (LLM), that is trained against support tickets and other product documentation. The AI understands dependencies (e.g., the tills should be reconciled before closing the store) and predict appropriate responses to conversational inputs.

Abstract: Examples include a system and computer-implemented method to receive a notification from an application programming interface (API) of creation of a just in time (JIT) grant, the JIT grant defining a request for a user to be authorized to access a cluster according to a JIT policy; determine if access to the cluster by the user is authorized according to the JIT policy; grant access to the user to the cluster when access is authorized according to the JIT policy; and send a notification to the API that access by the user to the cluster is granted.

Abstract: Systems, devices, and techniques are disclosed for automation with composable asynchronous tasks. A prompt may be received at a computing device. Using a first large language model (LLM) composable asynchronous tasks may be determined from the prompt. One of the composable asynchronous tasks may use a second LLM. The composable asynchronous tasks may be performed. Performing one of the composable asynchronous tasks may include generating a first output with the second LLM based on the prompt and validating the first output of the LLM. Performing another of the composable asynchronous tasks may include generating a second output using the first output.

Abstract: Methods, systems, and devices for data processing are described. A federation management service may receive a user input indicating multiple data sources as federation inputs into federated application programming interface (API). The one or more data sources may have a first metadata input that defines a target schema for a computed field and a second metadata input that defines a transformation for computing a value for the computed field from a data source schema to the target schema for the computed field. A runtime service of the federation management service may compute the value of the computed field using the second metadata input. The federation management service may generate the federated API from the data sources, where the federated API includes the computed field.

Abstract: Disclosed are some implementations of systems, apparatus, methods and computer program products for processing consent information. A data consolidation system obtains, from each of two or more computing systems, a corresponding set of data objects such that two or more sets of objects are obtained. Each data object is associated with a corresponding individual and includes consent information indicating whether the individual consents to an action in relation to corresponding contact information of the individual. Consent information is extracted, for at least one individual, from the sets of data objects such that two or more sets of consent information are collected, the sets of consent information including a first set of consent information corresponding to a first computing system and a second set of consent information corresponding to a second computing system.

Abstract: A dashboard runtime component includes (1) a visualization component configured to render a visual representation of data items retrieved from a data source and (2) a query execution component associated with at least the visualization component. The query execution component is configured to retrieve the data items from the data source.

Abstract: Techniques for generating tasks from message data are discussed herein. A user profile may view content displayed within a communication-based virtual space. The user profile may request that the communication platform generate a task based on a content item displayed therein. That is, the user profile may be viewing a communication-based virtual space and select a message to use as the basis for creating a task. The communication platform may display one or more lists with which the user profile can associate the task. The user profile may select a list from the one or more of the lists and based on the selection, the communication platform may generate a task associated with the list. The communication platform may cause the task to be displayed via a user interface of a mobile device used by the user profile.

Abstract: A method by a router component in a multi-tenant on-demand serving infrastructure to route scoring requests to scoring containers. The method includes receiving a scoring request, determining a machine learning application associated with the scoring request, determining whether a router instance for the machine learning application exists, and responsive to a determination that a router instance for the machine learning application does not exist, obtaining a configuration object for the machine learning application and instantiating the router instance for the machine learning application based on the configuration object for the machine learning application. The method further includes invoking the router instance for the machine learning application to route the scoring request associated with the machine learning application to a scoring container that provides scoring functionality for the machine learning application.

Abstract: A multi-tenant system receives a request to generate a first workspace according to at least a subset of tenant data stored in a plurality of data spaces. The system determines a collection of components of the first workspace and determines a respective data and/or metadata source for each component of the collection of components. The respective data and/or metadata source corresponds to a data space of the plurality of data spaces. The system receives, via the plurality of data spaces, data and/or metadata for each component of the collection of components and stores the data and/or metadata of the first workspace to each component. The system also stores, in a multi-tenant junction table, relationships between the first workspace, the collection of components of the first workspace, and respective objects from the plurality of data spaces.

Abstract: Techniques for providing an interactable meeting transcript associated with a meeting via a virtual space of a communication platform are described herein. In at least one example, a user requests to conduct an audio or video meeting associated with a virtual space of the communication platform. The communication platform receives data representative of the meeting, generates a meeting transcript associated with the meeting based on the data, and causes presentation of the meeting transcript via the virtual space. In at least one example, the communication platform receives, from a user of the virtual space, an indication of a selection of a section of the meeting transcript, and causes an input interface to be presented, enabling the user of the virtual space to comment on the section of the meeting transcript. In response to receiving the comment, the communication platform updates a file associated with the meeting to include the comment.

Abstract: A computing device receives user interaction with a portion of a data visualization that is displayed on the computing device. In response to receiving the user interaction, the computing device generates a first prompt that includes parameters specifying a visualization type of the data visualization, a data array corresponding to data marks of the data visualization, and a title of the data visualization. The computing device inputs the first prompt into a large language model (LLM), and obtains from the LLM a text narrative for the portion of the data visualization. The device generates a second prompt in accordance with the text narrative, inputs the second prompt into the LLM, and obtains from the LLM a title for the text narrative. The device generates a self-contained story comprising the text narrative and the title.

Abstract: Techniques are disclosed relating to the protection of secrets within a software development lifecycle. Developers can use an encryption service to encrypt a secret to be used by an application within a package. The secret can be associated with the application, and then encrypted and included in a package that is signed and passed through a software automation pipeline to a data center that hosts the production server for the application. The application executing on the production server can request that the secret be decrypted by a decryption service after package verification. A developer can also specify, in a manifest file, a set of secrets needed for applications executing in the same data center. The manifest file may be passed from the software development environment to the data center, where the specified secrets are created and used by the applications without ever residing or being accessible outside the data center.

Abstract: A machine learning model hosted on a cloud platform may be used to proactively predict if a maintenance procedure should be performed for a vehicle. In some examples, to support the prediction, the machine learning model may be connected to a different cloud platform that includes a customer relationship management (CRM) system and receives data from sensors of the vehicle. As such, the cloud platform with the CRM data may transmit the CRM data and the sensor data of the vehicle to the cloud platform hosting the machine learning model to aid in generating the maintenance procedure predictions. Further, the maintenance procedure predictions may also include the generation of a prediction score associated with a maintenance procedure. In some examples, the prediction score may satisfy a prediction score threshold, thus a notification may be transmitted to a computing device that indicates the maintenance procedure to be performed for the vehicle.

Abstract: A database with virtual partitioning. A computer system used to implement the database receives a request for a database operation to be performed on data stored in a portion of the database, where the data has a group of logical partitions (e.g., tenants of a multi-tenant database), and where the portion of the database is not further physically partitioned. The computer system identifies a virtual partitioning scheme for the database operation, the virtual partitioning scheme defining sub-groups of the group of logical partitions. The computer system then performs the database operation for the sub-groups defined by the virtual partitioning scheme. Multiple virtual partitioning schemes can be active at the same time, and virtual partitioning schemes can be created dynamically in some instances.