Abstract: Methods, systems, and devices for data management are described. A method may include obtaining a new snapshot of a relational database of a source computing environment, comparing the new snapshot of the relational database to a prior snapshot of the relational database, updating, in response to the comparison indicating that data of a row of a relational database table changed between the prior snapshot and the new snapshot, a field of a second versioning control column of a corresponding row of a backup database table of the set of backup database tables to indicate a version of the new snapshot, and adding, in response to the comparison indicating that the data of the row of the relational database table changed, an additional row to the backup database table, where a field of a first versioning control column of the additional row includes an indication of the version of the new snapshot.

Abstract: Example methods, apparatus, systems and articles of manufacture are disclosed to determine tags for unknown media using multiple media features. Disclosed examples extract features from portions of the unknown media. Disclosed examples weight the features based at least partially on respective recognition technologies used to extract the features to determine corresponding weighted features. Disclosed examples search a database of pre-tagged media with a combination of the weighted features to generate a list of suggested tags for the unknown media, the list of suggested tags including relevancy scores for respective ones of the tags in the list. Disclosed examples assign a tag from the list of suggested tags to the unknown media based on a comparison of the relevancy score for the tag to a threshold.

Abstract: Methods and apparatuses for providing a permissions-aware search and knowledge management system that incorporates user suggested results, document verification, and intelligent user activity tracking across group hierarchies to improve the quality and relevance of search results are described. The permissions-aware search and knowledge management system may enable content stored across a variety of local and cloud-based data stores to be indexed, searched, and displayed to authorized users. The identification and ranking of relevant documents corresponding with a user's search query may take into account user suggested results from the user and others assigned to the same group as the user, whether the underlying content of a search result was verified by a content owner as being up-to-date, the amount of time that has passed since the underlying content was verified by the content owner, and the recent activity of the user and related group members.

Abstract: A clustering model is used to partition a set of historical opportunity profiles representing historical opportunities into a plurality of opportunity profile clusters. A target opportunity profile that includes a plurality of target-opportunity parameters is obtained, and an opportunity profile cluster is identified that meets a matching criterion with the target opportunity profile. The plurality of historical opportunity profiles of the identified opportunity profile cluster are partitioned based on an outcome indicator associated respectively with each of the plurality of historical opportunity profiles, providing a first subset of historical opportunity profiles and a second subset of historical opportunity profiles. An insight generation model is applied to the target opportunity profile along with the first subset and/or the second subset to generate one or more insights for the target opportunity profile.

Abstract: A method, system, and computer system for performing an operation with respect to a target table are disclosed. The method includes performing first and second jobs, obtaining one or more other resulting files based at least in part on unmatched rows, and obtaining a set of processed files based at least in part on performing a post-processing operation with respect to the set of resulting files. The set of processed files has less files than the set of resulting files. Performing the first job includes determining a set of matching target table files and storing target table information indicating for each of the set of matching target table files, a particular set of rows having matching rows. Performing the second job includes performing a matching action based on matched rows and obtaining the second job resulting file(s).

Abstract: An update to a data set is received at a distributed database system. The data set is stored with or cached for accessing different partitions of a database. A new version identifier of the data set is written to indicate the update to the data set using a corresponding log record written to respective update logs of the different partitions of the database. The new version identifier is then used as part of subsequent validation operations for access requests to the database.

Abstract: A method facilitating soft deletion and restoration of clustered file systems includes, in response to receiving an instruction to temporarily disable a computing cluster, creating, by a system including at least one processor, snapshots of volumes associated with a virtual machine (VM) of the computing cluster; generating, by the system, mapping data representative of a mapping between snapshot identifiers, of the snapshots of the volumes associated with the VM, and an instance identifier of the VM; and, in response to the mapping data being determined to have been successfully generated, deleting, by the system, the VM.

Abstract: The present disclosure relates to systems and methods for generating user-specific outputs using modular AI agents and hybrid data representation engines. At least one processor may receive user data inputs and task configuration data from multiple sources, where the task configuration data specifies a user-specific task. The processor may process the received data to generate raw input data and transmit it, along with the task configuration data, to a control plane configured to determine processing parameters for modular AI agents. The modular AI agents may include a feature selection agent to extract user interaction features, a numerical transformation agent to convert numerical values into text formats, and a summarization agent to generate a data representation with task-specific information. A hybrid data representation engine may generate user-specific outputs, which may be evaluated using a large language model, stored in a persistent storage layer, and provided via APIs to downstream systems.

Abstract: Described are examples for managing event data from multiple event data sources including executing, by a processor, a query for event data in a discoverable event stream, wherein the event data is stored as timeseries data in the discoverable event stream such that a property corresponding to an object has multiple different values at multiple different time instances, determining, based on a timeseries function, a subset of the multiple different values for the property in instances of the event data that correspond to the multiple different time instances to return for the query, and returning the subset of the multiple different values for the property for the query.

Abstract: Implementations of a system for downscaling commodity flow data may include a first commodity flow database at a first level of geographic/temporal granularity, a second commodity flow database including commodity data for a plurality of commodities at a second level of geographic/temporal granularity and a group of high speed multiple core processor clusters where each core of may be assigned to process data associated with a commodity from the second commodity flow database and data of the first commodity flow database. The system may also include a downscaling module to generate downscaled and a quality check module to generate quality checked data; and a merging module configured for merging downscaled data and quality checked data in parallel. The system may also include a database module configured for writing data asynchronously according to a first-in first-out queue system in a commodity flow database within a period of hours.

Abstract: The present disclosure provides a system for providing virtual assistant for generating customized query for retrieving data from a database. The system comprises a processor and a memory storing program instructions, which, when executed by the processor, causes the processor to provide one or more index of categories of the data, for efficient retrieval of the data from the database based on a customer's query. The processor is also configured to provide one or more few-shot prompts for use by the customer for generating an output in a predetermined format corresponding to the customer's query. The processor is configured to receive a query from the one or more customer, wherein said query is a text and is based on at least one of index of categories and the few-shot prompt. The processor, based on indexes and few-shot prompt processes the query using natural language processing to extract one or more textual content from the query and sends the query to the database for retrieval of the data.

Abstract: A method for backing up data includes: determining a storage tier for storing the data; determining a data access frequency of the data, where the data access frequency indicates a frequency at which the data is accessed; and taking information of the storage tier and the data access frequency of the data as metadata, and sending the metadata and the data to a backup server. By sending the information including the storage tier for storing the data and the data access frequency as the metadata to the backup server, the computing device is enabled to recover the data by using the information in the metadata during the data recovery process, so that the data tiering layout can be maintained in the storage region to maintain the storage service performance, and thus a seamless storage service is provided to improve the user experience.

Abstract: Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and/or request bodies, structuring user-generated content, and/or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and/or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

Abstract: Provided is a method including obtaining a prompt, determining a prompt embedding vector representing the prompt in an embedding space, modifying the prompt embedding vector using a trained model configured to adjust prompt embedding vectors to decrease proximity to vectors of blocks in a data set from which data is retrieved to augment generation by the generative AI model, determining that the modified prompt embedding vector is within a threshold distance to vectors in the embedding space corresponding to one or more blocks in the data set, selecting the one or more blocks in the data set, generating a response using the generative AI model based on the selected one or more blocks in the data set, quantifying an amount of influence of the respective block on corresponding text in the generated response, and providing the response and a representation of the quantified amount of influence as an output.

Abstract: A storage manager maintains metadata for a plurality of graph components including, for each given graph component, a memory-state indicator that indicates whether the given graph component is stored in memory. The storage manager identifies a set of graph components required to execute a graph processing operation and identifies, based on the metadata, a first subset of the set of graph components that are stored in the memory and a second subset of the set of graph components that are not stored in the memory. The storage manager loads the second subset of graph components into memory and initiates execution of the graph processing operation using the set of graph components in memory.

Abstract: A system and method for continual learning in a provider network. The method is configured to implement or interface with a system which implements a semi-automated or fully automated architecture of continual machine learning, the semi-automated or fully automated architecture implementing user-configurable model retraining or hyperparameter tuning, which is enabled by a provider network. This functions to adapt a model over time to new information in the training data while also providing a user-friendly, flexible, and customizable continual learning process.

Abstract: A method of generating and storing metadata in a data processing system includes defining metadata sets of process definition metadata based on a process definition of the data processing system. The metadata sets include a first set of metadata corresponding to a processing step of the data processing system, a second set of metadata corresponding to a processing step successor, and a third set of metadata corresponding to a data object that is produced or consumed by the processing step in the data processing system. The method further includes executing one or more steps of the data processing system according to the process definition and generating runtime metadata during an execution of the one or more data processing system steps. The method further includes storing the runtime metadata and forming a metadata data store that integrates the runtime metadata and the metadata sets of process definition metadata.

Abstract: A device and a method for collecting, analyzing and integrating log data of an in-vehicle infotainment (IVI) system are disclosed. The method for collecting, analyzing and integrating log data of an IVI system includes identifying model information of a vehicle to be a target of digital forensics, hardware information and software information related to the IVI system of the vehicle, collecting target data for the digital forensics based on the model information, the hardware information and the software information, and storing the collected target data in an integrated database.

Abstract: Systems and methods are provided for replicating data between different services used to update databases. One example system includes a database and a computing device coupled in communication with the database, wherein the computing device is configured to perform a create, update or delete operation on the data included in the database and to post an entry representative of the operation to an outbox data structure. The computing device is also configured to retrieve the entry from the outbox data structure; publish the entry as a first message to a message bus, thereby informing a different data system of the operation in the database; receive a second message from the message bus indicative of a create, update or delete operation at a database included in a different data system; and direct a service of the system to perform the create, update or delete operation of the second message.

Abstract: Systems and methods for using prompt dissection to prompts one or more machine learning models for a set of one or more documents are disclosed. Exemplary implementations may: store a set of document segments from the set of one or more documents; present a user interface to obtain a compound query that includes a first subquery and a second subquery; create subquery vectors that semantically represent the subqueries; determine a subset of a set of semantic vectors based on comparisons with the subquery vectors; create a combination of the individual document segments that are associated with the subset; provide one or more prompts to the one or more machine learning models, using the combination of the individual document segments as context; present replies from the one or more machine learning models, and/or perform other steps.