Abstract: This technology relates to intelligently generate a search result. A method, computing device, and non-transitory computer readable medium include identifying a plurality of factors relating to a dominant feature of a stored object, and retrieving data associated with the stored object from a storage. For each of the plurality of factors, quantifying a significance of influence the factor on the dominant feature of the stored object, and determining whether the factor is a target factor influencing the dominant feature of the stored object. A generative artificial intelligence processing model is trained with the target factors and associated significances. In response to receiving a search input regarding the stored object, executing the trained generative artificial intelligence processing model to generate and then display a search result.

Abstract: A space-efficient change journal for a storage system is provided. The storage system includes a memory having computer readable instructions stored therein. The system further includes a processor configured to access a log structure merge (LSM) tree-based metadata index having metadata for the storage system. The LSM tree-based metadata index includes indices placed in a plurality of indexing layers and one or more indices are merged within the indexing layers in response to updates to metadata, or as a background task. The processor is configured to identify one or more indices of the LSM tree-based metadata index as entries of a change journal of the storage system. The processor is further configured to maintain the change journal of the storage system based upon the identified entries.

Abstract: A method for performing vector similarity search (VSS) includes: performing, based on a set of thresholds, a sparse partitioning of n-dimensional hyperspace into a plurality of grid bins to generate a hyperspace partition map, in which (i) only grid bins to which vectors mapped to are managed by the hyperspace partition map and (ii) each grid bin of the plurality of grid bins points to a storage location in a storage; ingesting a vector; using the hyperspace partition map to identify a grid bin of the plurality of grid bins for the vector and to identify coordinates of the grid bin; mapping the vector to the grid bin; and hashing the coordinates to generate a storage location key associated with a second storage location, in which the vector is stored to the second storage location using the storage location key.

Abstract: Database systems and methods are provided for providing record data to an application extension associated with a native application at a client device. One method involves a cross-platform service receiving, from the native application, a query for record data associated with a record at a database system on behalf of the application extension, determining a data source for retrieving the record data based at least in part on a configuration of the native application, converting the query into a transformed query having a different format associated with the identified data source, retrieving the record data from the identified data source using the transformed query, and providing the record data to the application extension via the native application, wherein the application extension is configurable to generate a component of a graphical user interface display using the record data.

Abstract: Provided are a system and method for obtaining, according to current and historical URL placement(s) on search engine results pages (SERPs) for identified keywords, predicted URL performance in SERP positioning. Such performance can, using URL titles and snippets appearing on SERPS, be assessed for URLs filtered to meet targeted (i.e., non-branded, informational, transactional) classifications. When a machine learning model receives input, including, for example, (a) historical trajectories of SERP positions for filtered URLs matched for identified keywords, (b) URL classification as informational or transactional, and (c) URL work type categorization (e.g. optimization, new Content, or no action), positioning for an analyzed URL on one or more SERPs returned for query according to one or more identified keywords can be predicted for a predetermined time horizon.

Abstract: A follower system and a leader system store records for a plurality of users. Each of the follower system and the leader system stores at least a record for a user. Each of the follower system and the leader system stores a private log and a public log. The public log is periodically synchronized across the follower system and the leader system. The private log of each system is maintained independent of the other system. The follower system transmits information stored in the public logs to the leader system for synchronizing the public logs of the follower system and the leader system in response to updating the public logs with information describing the second set of transactions.

Abstract: In some implementations, there is provided a method including receiving a request to provide a local database system with smart data access to a database table stored at a remote database system; executing, by the local database system, a series of one or more fetches, each of which obtains a chunk of the database table stored at the remote database system, such that a corresponding result set for each fetch causes the remote database system to fetch and materialize a corresponding chunk of the database table rather than the database table in its entirety; and reading, by the local database system, a first chunk obtained from the database table stored at the remote database system to form, at least in part, the local copy at the local database system.

Abstract: A data management apparatus selects a trail from management data. The management data includes, for an operation item regarding a system to be operated, data representing a workflow representing one or a plurality of works constituting a business of a system operation and associated with the operation item, and data representing a regulation matter associated with the operation item for a standard or a law. A trail is associated with the workflow. The data management apparatus performs deletion determination that is determination as to whether or not the selected trail can be deleted. The deletion determination includes a determination as to whether or not the current date and time exceeds the holding expiration time of the selected trail.

Abstract: Disclosed are various approaches for facilitating verifier integrations using computer models. An exemplary system of the present disclosure comprises a computing device comprising a processor and a memory; and machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least: receive an unstructured claim query for a data document from an entity computing device, wherein the data document is structured in accordance with a data document schema; parse the unstructured claim query to identify attributes that are then converted to a machine-understandable representation of the unstructured claim query; prepare, using a predictive model that is trained to understand data documents that are structured in accordance with the data document schema, a query response to the machine-understandable representation of the unstructured claim query by evaluating contents of the data document; and transmit the query response to the entity computing device.

Abstract: A method for improving a query performance of a query of a database application using rewrite includes introducing a database query into a database application to obtain a query result, analyzing the database query to identify index columns and a predicate column; identifying associated columns by determining if any associations exist between the index columns and the predicate column, calculating a confidence score value for each of the associated columns responsive to the associated columns and the predicate column, generating a list of associated columns with the confidence score values, generating a rewritten query by rewriting the database query based on the list of associated columns and the confidence score values and validating the rewritten query.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an execution plan for a request that involves accessing and synthesizing data assets from siloed data repositories. In one aspect, a method comprises receiving a first request, generating a request execution plan comprising (i) a sequence of second requests and (ii) a respective indicator of a relationship between a corresponding response for each second request and an overall response to the first request by processing the first request using a request planner artificial intelligence (AI) neural network agent, sequentially, assigning and providing each second request in the sequence of second requests to a particular retriever AI neural network agent in accordance with metadata corresponding with a particular data repository, obtaining the corresponding response to the second request, and generating the overall response to the first request using a synthesizer AI neural network agent.

Abstract: Systems and methods for translating natural language to SQL on a custom enterprise data warehouse powered by Generative AI. With an embodiment of the present invention, a natural language question may be converted to a meaningful and accurate database query, e.g., SQL query, relevant to tables existing in an enterprise data warehouse. An embodiment of the present invention is directed to a comprehensive approach of transforming a natural language query to a focused SQL query using domain specific data models across firmwide metadata systems and data systems. In response to a user query, an embodiment of the present invention performs metadata analysis, targeted data retrieval and then SQL generation. An embodiment of the present invention may apply data warehousing standards and guidelines followed in the enterprise and provide a plug-and-play type architecture and solution that is scalable to large warehousing and other systems.

Abstract: Aspects of the present disclosure address systems, methods, and devices for tracking object dependencies in a cloud database system. An object dependency created between a referencing object and a referenced object is detected. Based on detecting the object dependency, a dependency record is generated. The dependency record includes dependency information describing the object dependency between the reference object and the referenced object. The dependency record is stored in a database of dependency records.

Abstract: Embodiments of the disclosed technologies include receiving a first query including at least one first query term and configuring at least one prompt to cause a large language model to translate the at least one first query term into a set of functions that can be executed to obtain at least one second query term and generate and output a plan that is executable to create a modified version of the first query based on the at least one second query term. The plan is obtained by applying the large language model to the at least one prompt as configured. The plan is executed to determine the at least one second query term and create the modified version of the first query. The modified version of the first query is executed to provide, via the user interface, a response to the first query.

Abstract: Embodiments of this application provide an audio generation method, a related apparatus, and a storage medium, to provide a better audio generation solution for a user. In embodiments of this application, a text is obtained, a song clip corresponding to the text is obtained through matching, and the song clip is used as audio corresponding to the text. In this way, the text can be expressed in a manner of the song clip.

Abstract: An information processing device sets: a first argument of a first table join instruction to a second table join instruction as the first argument of the second table join instruction; a second argument of the first table join instruction to the first table join instruction as the first argument of the first table join instruction; a first output data table generated by the first intermediate table generation instruction to the first table join instruction as the second argument of the first table join instruction; and a second output data table generated by the first table join instruction after changing the first and second arguments of the first table join instruction to the second table join instruction as the second argument of the second table join instruction.

Abstract: Functionality in a network device is specified by an application installation file that describes programmable devices used to implement the functionality. Profiles for programmable devices generated from the application installation file and stored on the network device. A profile database stores profiles associated with functionality specified in previously received application installation files. A profile associated with a selected functionality is selected to implement the selected functionality, including loading one or more bitfiles identified in the selected profile to program the programmable devices associated with the selected functionality.

Abstract: A computing platform may be configured to (i) obtain an input dataset, (ii) construct a graph from the input dataset, (iii) for a given node within the constructed graph, generate a first type of embedding vector using a first embedding technique (e.g., a shallow embedding technique) and a second type of embedding vector using a second embedding technique that differs from the first embedding technique (e.g., a deep embedding technique), and (iv) use the first and second types of embedding vectors for the given node and a data science model to render a given prediction for the given node.

Abstract: Described herein is an apparatus and method for training a machine learning model. An apparatus may include a computing device configured to receive a corpus of data containing entries corresponding to a plurality of subjects; identify a plurality of entries within the corpus, corresponding to a first subject of the plurality of subjects and representing medical history of the first subject; determine a plurality of temporal attributes of the plurality of entries; generate a plurality of tokens as a function of the plurality of entries; generate a chronological data structure segment ordering one or more of the plurality of entries and the plurality of tokens, as a function of the plurality of temporal attributes; and train a multimodal machine learning model on a training dataset including the chronological data structure segment.

Abstract: The present invention relates to a method for constructing a database schema model by a user by means of a computer, comprising the steps of: providing a set of collections and/or optionally one or more relationships linking at least two of said set of collections; editing one or more of said set of collections, each of which associated with a schema definition displayed by a single table on an entity relationship diagram on a graphical interface on said computer and comprising at least one object and/or one field, said schema definition editable via a tree diagram on said graphical user interface on said computer; automatically generating by means of said computer said database schema model for a database system or a REST API; characterized in that, said set of collections comprises at least one collection comprising a nested object editable via said tree diagram with two levels or more.