Abstract: An electronic device includes: a memory including a storage; and a processor operatively connected to the memory, wherein the processor is configured to: based on a request to delete a file, identify use attribute information about the file requested to be deleted; based on information related to an extension of the file requested to be deleted that being stored in the memory, update the information related to the extension of the file requested to be deleted, based on the use attribute information; and configure an attribute of the extension of the file requested to be deleted, based on the updated information related to the extension of the file requested to be deleted and a hotness condition of the storage.

Abstract: A query modality recommendation system provides recommendations to use a particular query modality based on one or more categories of search results for a search query. Upon receiving a search query in a first query modality at a search engine, the query modality recommendation system determines to recommend use of a second query modality based on one or more categories of the search results. For example, the first query modality may be a textual query and the second query modality may be an image query. In aspects, recommending use of the second query modality comprises comparing a first search performance of the one or more categories for the first query modality in historical search queries to a second search performance of the one or more categories for the second query modality in the historical search queries.

Abstract: Systems and methods are configured to generate a set of potential responses to a prompt using one or more data models with data from at least a plurality of data domains of an enterprise information environment that includes access controls. A deterministic response is selected from the set of potential responses based on scoring of the validation data and restricting based on access controls in view of profile information associated with the prompt. These enterprise generative AI systems and methods support granular enterprise access controls, privacy, and security requirements, and provide traceable references and links to source information underlying the generative AI insights. These systems and methods enable dramatically increased utility for enterprise users to access information, analyses, and predictive analytics associated with and derived from a combination of enterprise and external information systems.

Abstract: Implementations of this specification provide query optimization methods, apparatuses, and systems for secure multi-party databases. In an implementation, a method includes: receiving a current query associated with a plurality of target database of a multi-party database system, generating a plurality of execution plans for the current query, determining, for each execution plan, a respective cost computation formula of a plurality of cost computation values for computing an execution cost of jointly executing the execution plan by the plurality of target databases, receiving a secure computation result from each of a plurality of query engines corresponding to the plurality of target databases, and determining an optimal execution plan having a lowest cost value in the plurality of cost computation formulas based on the secure computation result.

Abstract: Disclosed are systems and methods that enable robust, universal authentication and network access control capabilities for networks having a variety of computing device types and end users that access and operate on the network. The systems permit verification of end user identity, confirmation that computing devices have sufficient resources to reliably operate on the network, and configuration of computing devices with required software applications. The systems and methods further enable real-time, intelligent monitoring of the tasks and functions performed by system end users. The systems provide an immersive experience that allows end users to teleport into the environment of other end users, including viewing another end user's computer desktop, listening to ongoing telephone conversations, viewing and analyzing contemporaneous images or video of an end user and the end user's work environment, and interacting with the end user to provide precision coaching and assistance.

Abstract: To find enriching contextual information for an abbreviated domain name, a data enrichment engine can comb through web content source code corresponding to the abbreviated domain name. From textual content in the web content source code, the data enrichment engine can identify words with initial characters that match characters of the abbreviated domain name to thereby establish a relationship there-between. This relationship can facilitate more accurate and efficient domain name classification. The data enrichment engine can query a WHOIS server to find out if candidate domains having initial characters that match the characters of the abbreviated domain name are registered to the same entity. If so, keywords can be extracted from the candidate domains and used to find more relevant domains for domain risk analysis and detection. Candidate domains determined by the data enrichment engine can be provided to a downstream computing facility such as a domain filter.

Abstract: A method and system for intelligently organizing one or more groups of relevant files may include retrieving a user data signal including user-specific data, analyzing the user data signal to identify a parameter relating to file relevance, identifying one or more relevant files in a storage medium based on the parameter at least one of a user category property, a lifecycle stage property, a relevant activity property, or an activity level property of one or more files in the storage medium, organizing the one or more relevant file into the one or more groups of relevant files, and providing for display data relating to the one or more groups of relevant files.

Abstract: Various embodiments of the disclosure relate to an electronic device, and a method for operating content using the electronic device, wherein the electronic device comprises: a wireless communication circuit; a touch display; a memory; and a processor operatively connected to the wireless communication circuit, the touch display, and the memory. The processor is configured to: receive tag information on various types of content and update a database, obtain tag information having a specific keyword; control the touch display to display a search result for the various types of content corresponding to the obtained tag information; receive selection of at least one content from among the various types of content; receive action information for the selected at least one content; and perform an action corresponding to the received action information.

Abstract: Methods, systems, and computer-readable media for unified search and navigation for cloud-based services are disclosed. A unified search system receives textual input entered using a search interface. The search interface is displayed in a user interface concurrently with a management console for a cloud provider network. Based (at least in part) on the textual input, the system performs a search using one or more indexes usable to determine service names and descriptive information for the services. The system generates search results comprising a portion of the service names and a portion of the descriptive information. The search results are displayed using a search results overlay that is displayed concurrently with at least a portion of the management console. The system receives additional input entered using the overlay and comprising a selection of a particular search result. Responsive to the selection, additional information associated with the particular search result is displayed.

Abstract: A first backup of source data is performed. A second backup of the source data is determined to be performed at least in part by determining a plurality of objects that have changed since the first backup and determining a corresponding backup type for each of the plurality of objects that have changed since the first backup. Based on the determined corresponding backup types, a second backup of the source data is performed including by performing an incremental backup of a first portion of the plurality of objects that have changed since the first backup and a full backup of a second portion of the plurality of objects that have changed since the first backup.

Abstract: Proxy-based scaling may be performed for databases. A proxy may be implemented for a database that can establish a connection between the proxy and a database engine to perform a database queries received from a client at the proxy. A scaling event may be detected for the database responsive to which the proxy may establish a connection with a new database engine which may, in some embodiments, have different capabilities or resources that address the features or criteria that triggered the scaling event. Session state may be copied from the database engine to the new database engine so that the new database engine may be able to provide access to the database on behalf of requests received from the client through the proxy.

Abstract: A method for ranking a set of objects includes: receiving the set of objects to rank, a relevance score for each object, and a set of objective functions; based on the relevance scores for the objects, defining a decision space having n decision variables using a polytope, where n is the number of objects to rank and where vertices of the polytope represent permutations of exposures provided to the objects in the set by corresponding rankings; determining a Pareto-set for the set of objective functions; based on a Pareto-optimal point in the Pareto-set, determining a distribution over rankings for the objects in the set using the decision space, where a proportion is associated with each ranking in the distribution; selecting a sequence of rankings for the objects in the set based on the distribution in accordance with their proportions; and outputting the selected sequence of rankings of the objects.

Abstract: Various embodiments described herein relate to a contextualized time series database and/or contextualized time series data consumption. In this regard, a request to generate contextualized time series data related to one or more assets is received. The request includes a user identifier indicating an identity of a user associated with the request. In response to the request, telemetry data associated with the one or more assets is contextualized, based on metadata and a set of data dimensionality filters associated with the user identifier, to generate the contextualized time series data. Furthermore, the contextualized time series data is allocated within a datastore configured for the user identifier to facilitate obtaining one or more insights with respect to the contextualized time series data.

Abstract: A computer-implemented method includes receiving input data that is organized into a set of rows and a set of columns. The method includes maintaining a machine learning header model that is trained on tabular data with header rows. The method includes supplying the input data as input to the machine learning header model to generate header row identification data that identifies a set of header rows that is a subset of the set of rows. The method includes maintaining a machine learning column model that is trained on tabular data. The method includes supplying the header row identification data and features of the input data to the machine learning column model to generate column label data that applies a set of defined labels to the set of columns. The method includes generating output data that is organized into rows and columns.

Abstract: A new type of table join operation, outer semi join (OSJ), is provided, which can be used by an optimizer layer and an execution layer of a database management system (DBMS). OSJ combines the semantics of both left outer-join and semi-join. The concept of an anti-join marker (AJM) is also introduced, which specifies whether a matching row was not found between joined tables for each result row in an OSJ operation. The OSJ operation supports unnesting of a class of disjunctive ANY, ALL, EXISTS, NOT EXISTS, IN, and NOT IN subqueries for execution plan optimization. The disjunction may contain filter predicates. For unnesting, OSJ avoids the need of using a distinct operator on the right table and also supports using inequality (e.g. >, >=, <, <=) in connecting or correlating conditions of subqueries, rather than being limited to equality only.

Abstract: A query acquisition unit (610) acquires pose information (hereinafter, described as query pose information) that is information to be a query and indicates a pose of a person. A search information acquisition unit (620) acquires a plurality of pieces of search pose information. The search pose information is pose information about an image (hereinafter, described as a target image) to be a search target, and is stored in a database by a plurality of target images. A selection unit (630) selects, from among the plurality of pieces of search pose information, two or more pieces of the search pose information whose degree of similarity to the query pose information satisfies a reference. The selection processing becomes substantially processing of selecting a target image similar to an image (hereinafter, described as a query image) associated with the query pose information.

Abstract: The present disclosure is directed to systems and processes for adaptively optimizing query execution. To improve efficiency and reduce costs of executing queries, machine learning may be utilized to automatically choose or adjust the processing blocks used to execute or process queries based on the nature of the cloud processing outputs and client preferences, among other considerations. A machine learning engine may group queries together into topics based on characteristics of the queries. The machine learning engine may receive execution statistics for the topics and, using the execution statistics, identify which topics enhance or optimize query execution. The machine learning engine may determine which topics may be executed by which processing blocks, at which times of day, and on which platforms, among other considerations in order to enhance or optimize cost or processing efficiency according to client-specified parameters.

Abstract: A flexible annotation framework normalizes auxiliary information from diverse sources, ranks the information for an individual search result, and provides a lightweight or full display of the auxiliary information in an annotation for the search result. An annotation thus displays information not typically part of the details included in the search result. An example method comprises, for at least one item in a search result page, identifying at least one annotation of a first annotation type in an annotation data store that references the item, identifying at least one annotation for a second annotation type in an annotation data store that references the item, ranking the annotation of the first annotation type and the annotation of the second annotation type and providing the highest ranked annotation as part of a search result for the item in the search result page.

Abstract: This invention is directed to an on-board data storage method and system. The on-board data storage method includes obtaining, in a current time period, various pieces of time sequence data to be processed written into the on-board database; for each piece of the time sequence data to be processed, determining a node to be stored in the on-board database and a partition to be stored in the node according to the piece of the time sequence data to be processed, and writing the piece of the time sequence data to be processed into a corresponding time sequence in the determined partition to be stored; and writing each piece of the time sequence data written into each partition to be stored into each corresponding data bucket in memory.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate capturing relative importance of relational entities for building database embedding models are provided. According to an embodiment, a system can comprise a processor that executes computer-executable components stored in a computer-readable storage medium. The computer executable components can comprise a user input component that can receive user-specified schema information for weight-based model training. The computer executable components can comprise a relational component that can identify one or more importance levels for one or more entities of a relational table based on the user-specified schema information. The computer executable components can comprise a training component that generates a training document having entity occurrences corresponding with the one or more importance levels.