Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a meta-prompting model to generate and store modified system prompts for agent models. For example, the system utilizes an agent model to produce a generated task output from a system prompt corresponding to a task input. The system utilizes a meta-prompting model to compare the generated task output with an expected task output and determine one or more task output differences. The system utilizes the meta-prompting model to generate a modified system prompt for the agent model to produce a task output aligning with the expected task output according to the task output differences. In some embodiments, the system iteratively adjusts the modified system prompt. In some embodiments, the system stores the modified system prompt in a system prompt repository for later use with the agent model for a particular task.

Abstract: A storage manager for an information management system determines whether one or more predetermined conditions have been met for transferring metadata of previously performed backup jobs stored in a first management database. A backup job may correspond to a backup operation of a primary storage device of a first client computing device. In response to a determination that one or more of the predetermined conditions have been met, the storage manager may transfer metadata for a second plurality of backup jobs to a second management database of a recovery manager. The recovery manager may receive a request to restore data to the primary storage device of the first client computing device based on the metadata of the second plurality of backup jobs. A media agent managed by the recovery manager may then restore the requested data to the primary storage device of the first client computing device.

Abstract: System, method, and various embodiments for a tabular data integrity and query processing system are described herein. An embodiment operates by receiving a query to be executed against a knowledgebase. One or more keywords are identified from the query, and a vector search is performed against the knowledgebase based on the one or more keywords, the knowledgebase including documents that have been divided into a plurality of chunks. A subset of chunks related to generating an answer for the query are identified based on the vector search, the subset including a first chunk with a table ID. A table image corresponding to the table ID is identified. A prompt is generated instructing a language model to generate the answer to the query based on the subset of chunks, including the first chunk and the table image. The answer is provided.

Abstract: Systems and methods for natural language query processing and visualization. In embodiments, a structure associated with a dataset and a natural language question are obtained and provided to an AI model to request, from the AI model, a query that may be used for retrieving data from the dataset responsive to the natural language question. The query is received form the AI model and executed against the data in the dataset to retrieve data responsive to the natural language question. In embodiments, the data responsive to the natural language question is analyzed to determine one or more structural characteristics of the data responsive to the natural language question, and a graphical visualization of the data responsive to the natural language question is generated based on the one or more structural characteristics of the data responsive to the natural language question.

Abstract: Techniques are described for incrementally determining checksums for a snapshot. An example method comprises identifying, by a data platform implemented by a computing system, a plurality of leaf nodes and a plurality of intermediate nodes in tree data corresponding to a snapshot of a storage system at a particular time, wherein the intermediate nodes each comprise one or more pointers identifying one or more of the leaf nodes, and the leaf nodes each include an indication of file system data of the storage system. The method includes determining, by the data platform, a checksum for each of the leaf nodes, determining, by the data platform, a checksum for each intermediate node based on the checksum of the one or more leaf nodes identified by the pointers of the intermediate node; and storing, by the data platform, the checksum for each of the leaf nodes and each of the intermediate nodes.

Abstract: A method may include, based on a recovery policy associated with the primary database, creating a first snapshot of the primary database at a primary computing system, replicating the first snapshot of the primary database to a secondary computing system, wherein a backup database of the primary database is hosted on the secondary cluster, in response to the primary database being unavailable, applying the first snapshot of the primary database to the backup database, creating, according to the recovery policy associated with the primary database, a snapshot of the backup database, in response to the primary database having restored availability, creating, according to the recovery policy associated with the primary database, a second snapshot of the primary database.

Abstract: Methods, systems, and devices for data management are described. A data management system (DMS) may support backup and recovery of a filesystem. The DMS may, as part of the backup, obtain a snapshot of the filesystem. The DMS may mount the snapshot within a user space of a node of the DMS. The user space may be isolated from other processes that execute on the node of the DMS. The DMS may generate, within the user space based on the mounted snapshot, index files that index the filesystem. In response to a subsequent request to access one or more files, the DMS may retrieve the one or more files from the snapshot mounted in the user space based on the index files.

Abstract: A generative document system creates generative search results documents using generative artificial intelligence (AI) models and dynamically determines which one of the generative search results documents to provide in response to a search query. For example, in response to receiving a search query, the generative document system obtains search link results (e.g., website links and corresponding grounding information) for the search query and utilizes this information with multiple generative AI models to generate various types of generative search results documents. Additionally, the generative document system generates and utilizes a generative document arbitration model to determine, based on the search link results, which of the generative search results documents to provide in response to the search query.

Abstract: Techniques are provided for performing a storage operation targeting objects stored across multiple storage tiers of a cloud storage environment. A volume may be backed up as objects stored across the multiple storage tiers of the cloud storage environment, such as a standard storage tier directly accessible to the storage operation, an archival storage tier not directly accessible to the storage operation, etc. The storage operation may target the objects, such as where the storage operation is a directory restore operation to restore a directory of the volume. The storage operation can be successfully implemented such as to restore the directory even though objects of the storage operation are stored across the multiple storage tiers of the cloud storage environment.

Abstract: Embodiments of the disclosed technologies include parsing a query into a first query portion and at least one second query portion, matching an embedding of the at least one second query portion with an embedding that corresponds to a portion of a document of a document set, mapping the portion of the document to a first node of a graph; by a generative artificial intelligence model, constructing a graph query based on at least the first node, executing the graph query on the graph to identify a second node of the graph, extracting a path from the graph, and configuring the path for output at a device.

Abstract: Novel techniques are described for maintaining file consistency between different cloud infrastructure regions during cross-region file storage replication. The novel techniques comprise upon detecting a file deletion status (i.e., a file directory entry version marked as deletion) in a source file system during a replication cycle, retrieving file name from a valid file directory entry of a prior snapshot to construct a temporary file directory entry containing necessary information for a target file system to reconstruct a duplicate file directory entry version marked as deletion. The temporary file directory entry is transferred from the source file system to the target file system during the same replication cycle.

Abstract: The present invention discloses report generation systems and methods for gathering, reviewing, and extracting investigation data and generating, sending, and storing reports. In some embodiments, the system uses at least one processor. In some embodiments, the processor is configured to access investigation evidence and data, parse the contents of the investigation evidence, transmit the investigation evidence and data to a centralized repository, generate at least one investigation report using a machine learning algorithm, and securely store the investigation report in a central repository. In some embodiments, the method for creating and storing reports further includes accessing investigation evidence and data, parsing content of the investigation evidence, transmitting the investigation data to a centralized repository, generating at least one investigation report using a machine learning algorithm, and securely storing the investigation report in the centralized repository.

Abstract: The present disclosure relates to a system and method for storage configuration based on an industrial internet of things (IoT) data center.

Abstract: This disclosure describes an implementation of a computing system that utilizes accelerator hardware configured to perform parallel computation of matching pairs of a join operation of a database program. This computation is performed at least in part by, for each thread that determines a matching pair, computing an offset in an output tuple by adding a global rank of a respective block, an intra block rank of a respective warp, and an intra warp rank of a respective thread. This computation is further performed by storing index values for a primary key and a foreign key in a matching pair at the computed offset location in an output tuple, and outputting the output tuple.

Abstract: A technology search and repurposing system comprising a query processing system, one or more intelligent systems, a response generator, machine learning (ML) powered navigator, and a curated database. Together these elements describe a system that doubles as a system to produce new technology-problem designs and as a system/method to curate a unique kind of data. The data itself may be used to build a new kind of AI that makes it possible to search technology by intent.

Abstract: A distributed logging system for different components in a cluster network system. A distributed log has a shared storage architecture and provides durability by maintaining multiple copies of the log data, and availability is facilitated through a sealing protocol that ensures that system failure will result in reconfiguration with a new node. The distributed log is fault tolerant so that a loss of a node will not lose data. A consensus algorithm is used for different clients to propose commands to the leader of the consensus engine, and the leader chooses the commands for each log slot. The log is replicated by the leader to all the followers and the protocol ensures that no loss of any committed entries when the new leader takes over.

Abstract: Described techniques enable compression and decompression of sort keys during sort/merge operations. A winning offset code value may be received from a merge bracket used to merge a plurality of sorted sequences of sort keys by comparing pairs of the sort keys until a winning sort key is determined. The sort keys may be represented by corresponding offset code values determined from relative values of pairs of sort keys, and the winning offset code value represents the winning sort key of the merge bracket. A removed portion of the winning sort key may be determined from the winning offset code value. A reduction value characterizing a size of the removed portion may be determined, and, from the winning offset code value, a retained portion of the winning sort key to retain may be determined. The reduction value and the retained portion of the winning sort key may then be stored.

Abstract: An overlay system is provided that includes a storage element and processing circuitry coupled thereto. The processing circuitry receives a stimulus associated with the overlay system and creates an executable graph-based model that implements an external datastore in the storage element, based on the stimulus. The processing circuitry creates, in the executable graph-based model, a plurality of node groups that implements a plurality of schemas associated with the external datastore, with a first node group created for a first schema. The processing circuitry creates, in the first node group, a first set of active nodes that implements a first database object associated with the first schema. The processing circuitry creates, in the first node group, a second set of active nodes that implements a first set of database records associated with the first database object. The processing circuitry executes one or more operations in the executable graph-based model.

Abstract: A graphical user interface receives natural language input from a user. A modular thread analytics exploration system uses context determination, dynamic context enrichment, and the natural language input to generate a solution recipe with a language model. The system prompt the language model with evaluation guides to improve the accuracy of the model output. The solution recipe includes steps (i) that are used to generate code and (ii) that are used to generate natural language explanations. The system generates code with a language model. The system processes the generated code in a sandbox and self-debugs the generated code as necessary. The output from the steps is presented in the graphical user interface.

Abstract: An illustrative method is disclosed for efficiently copying a source directory to a target directory. For example, the method may include receiving, by a storage system, a request to copy a source directory to a target directory; generating, by the storage system in response to the request, directory version metadata for the target directory; and mapping, by the storage system in response to the request, the target directory to metadata of the source directory such that the target directory reuses the metadata of the source directory within a target namespace identified by the directory version metadata for the target directory.