Abstract: A system and method include determining, by a point of sale (POS) device, that new data is available at the POS device, in response to determining that new data is available, sending the new data to a cloud server, in response to determining that the new data is successfully transmitted to the cloud server, generating a synchronization request indicating availability of new data at the cloud server, identifying one or more kitchen display systems (KDS) to send the synchronization request to, and sending the synchronization request to each of the identified KDS. Each identified KDS retrieves the new data from the cloud server in response to receiving the synchronization request, and at least one of the KDS performs an action based on the new data in response to retrieving the new data. Each KDS follows a similar procedure when new data for the POS device is available at the KDS.

Abstract: Systems and methods for analyzing data stored using a data model. The system can receive a user selection of a first object type indicating to perform filtering operations on a first set of data objects, generate a list of object types linked to the first object type based on an ontology, receives a user selection of a second object type, generate a list of properties of the second object type based on an ontology, receive a user selection of a first property from the list of properties, perform a data query determining values associated with the first property, receive a user selection of a first value, and displays information of a subset of data objects being a portion of the first set of data objects that are linked to data objects in the second set of data objects that have a first property value of the first value.

Abstract: Systems and methods are disclosed for implementation of a scalable and efficient scaling algorithm for clustering data objects within a data set, as well as implementation of the algorithm to control content generation and selection. A data set can be initially divided randomly into subsets. A base clustering algorithm, such as k-means clustering, can be applied in parallel to each subset to identify a set of local centroids representative of local clusters within each subset. The local centroids can be aggregated from across subsets and the base clustering algorithm can be applied to the aggregated local centroids to result in global clusters. Data objects associated with each global cluster (such as by being clustered within a local cluster corresponding to a global cluster) represent a global clustering of the data set. These global clusters can form the basis for additional iterations of local and global clustering, further refining global clustering.

Abstract: A database-aware storage server provides instant creation of snapshots without the need to create an intermediate test master database. During the snapshot creation time, the source database stays read-write and completes ongoing reads and writes. The database-aware storage server allows creation of layers of writable snapshots in a hierarchy. All these multiple databases share common data blocks. Any new writes performed by the database post snapshot are stored in blocks of sparse files. This promotes space sharing and reduces the total amount of space used by all these related databases. The allocations for the source and all new snapshot databases share the same common pool of storage. The newly created snapshot databases can access the data store directly without going through an intermediate layer.

Abstract: A method for processing a query for data stored in a distributed database includes receiving, at an edge device, the query for data stored in the distributed database from a query device. The method includes causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database. The method includes detecting, by the edge device, that summary data has been stored on the dynamic ledger. The method includes generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger. The method includes transmitting, to the query device, the approximate response.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for generating a response to a query input in a generative artificial intelligence model. An example method generally includes receiving a plurality of sets of tokens generated based on an input prompt and a first generative artificial intelligence model, each set of tokens in the plurality of sets of tokens corresponding to a candidate response to the input prompt; selecting, using a second generative artificial intelligence model and recursive adjustment of a target distribution associated with the received plurality of sets of tokens, a set of tokens from the plurality of sets of tokens; and outputting the selected set of tokens as a response to the input prompt.

Abstract: A method of managing decoupled front-end and back-end processes is disclosed. The method comprises receiving a first result of user interaction with a first front-end interface; determining that the first result represents a validation of a data item entered via the first front-end interface; mapping the data item in a validated form to a back-end object; causing storing the data item in a database system in association with the back-end object; receiving a second result of user interaction with a second front-end interface; determining that the second result represents a state transition corresponding to executing a query entered via the second front-end interface against the database system; mapping the state transition to a set of back-end commands; causing executing the set of back-end commands over the database system of back-end objects.

Abstract: The present application discloses a virtual column construction method based on data lake and a data query method. The present disclosure includes: first, automatically performing expression statistical analysis on a large quantity of statements to be analyzed in a data lake, so as to obtain an expression to be used; next, automatically constructing a virtual column construction request corresponding to the expression to be used, according to virtual column construction description information corresponding to the expression to be used, so that the virtual column construction request is used to request construction of a virtual column that can represent the expression to be used; then, according to the virtual column construction request, constructing a virtual column corresponding to the expression to be used, to enable the virtual column to represent the expression to be used.

Abstract: An online system maintains a product catalog including products from various retailers, from which users can purchase products. Each of the products are associated with attributes such as a size value and a size unit of measure (UOM). The online system identifies products with erroneous product attributes using taxonomy attribute value homogeneity. The online system performs an inference task in conjunction with the model serving system or the interface system to infer a correct size value and size UOM of the product. The online system evaluates the accuracy of the inferred size value and size UOM of the product. Responsive to determining that the inferred data is accurate, the online system updates the product catalog with the corrected product attribute information.

Abstract: A computer-implemented method for managing data in a computing environment. In one aspect, a method includes receiving a declarative input that indicates an outcome for data handling, identifying, from the declarative input, a predefined data filter configuration and a predefined data propagation configuration, filtering incoming data according to the predefined data filter configuration to generate filtered data, and replicating the filtered data to a data storage according to the predefined data propagation configuration.

Abstract: A social media maintenance system and its associated method comprise generating a dedicated bot based on the attributes of the social platform to which a user-designated channel for maintenance belongs. Subsequently, the dedicated bot joins the designated channel as a member of the channel and proceeds to crawl all member data within the channel to store and establish a member list. Furthermore, an associated bot is generated based on the attributes of the social platform. The associated bot performs a contextual establishment process with each member based on the member data in the member list. Through this contextual establishment process, member contextual information is obtained and used to create a messageable member list, which is stored. The associated bot then engages in private messaging with the messageable members based on the stored messageable member list.

Abstract: A system may obtain, via a network and from a plurality of sources, structured data and unstructured data for a product migration to a new product. The system may standardize the structured data and the unstructured data to obtain standardized data. The system may generate a data model by processing the standardized data. The system may utilize a machine learning model to determine migration scenarios for the product migration and to forecast outcomes for the migration scenarios based on the data model. The system may provide a plurality of refined predictive insights to a strategy optimization component. The system may configure a group of computing devices, from a plurality of computing devices, for the product migration.

Abstract: Provided is a control device which extracts summary information regarding an outline of a predetermined volume from volume mapping information regarding a configuration of a predetermined volume at a time point when each of backup images of a plurality of generations is created, and verifies whether a volume configuration based on a backup image of a specific generation corresponding to specific volume mapping information of a predetermined volume corresponding to a specific backup generation selected from the backup images of the plurality of generations on the basis of the summary information matches a volume configuration based on a current backup image of the predetermined volume, and when both the volume configurations match each other as a result of the verification, verifies and recovers backup data on the basis of the backup image of the specific generation.

Abstract: Various example embodiments described herein provide for systems, methods, devices, instructions, and the like for using AI model cascades to filter data on a data system, such as a database system, based on an artificial intelligence model prompt (e.g., user-provided prompt). In particular, various example embodiments enable a database system to use cascaded AI models and adaptive bounds to optimize data filtering operations based on an artificial intelligence model prompt (also referred to herein as just a prompt) while balancing computational cost and accuracy, which can be useful in processing large-scale data queries.

Abstract: A system receives a message that includes a change event associated with a source dataset for replication to a destination in a cloud environment. The system accesses a replication tag associated with the message. The replication tag includes a tag element that is mapped a destination key that identifies a destination dataset, corresponding to the destination, where the change event is to be replicated. The system determines that the tag element of the replication tag is mapped to the destination key. Responsive to determining that the tag element is mapped to the destination key, the system directs the message to the destination to replicate the change event to the destination dataset.

Abstract: A distributed system includes multiple servers that various client devices access. Each server maintains a copy of a data object for access to the data object regardless of which server is accessed. Each server may modify a local copy of the data object, with the modifications propagated to other servers. A server generates a local version vector that includes pairs of a server identifier from whom modifications were received and a server-specific version identifier for the modifications. When a client device retrieves the data object, the client device transmits a client version vector for the data object including pairs of server identifiers and server-specific version identifiers from which the client device received version events for modifications to the data object. The server compares the local version vector to the client version vector to determine whether to transmit a local copy of the data object to the client device.

Abstract: An apparatus is described that includes an execution unit to execute a first instruction and a second instruction. The execution unit includes input register space to store a first data structure to be replicated when executing the first instruction and to store a second data structure to be replicated when executing the second instruction. The first and second data structures are both packed data structures. Data values of the first packed data structure are twice as large as data values of the second packed data structure. The execution unit also includes replication logic circuitry to replicate the first data structure when executing the first instruction to create a first replication data structure, and, to replicate the second data structure when executing the second data instruction to create a second replication data structure.

Abstract: A system is configured to receive messages from an external device hosting a database. A replicator processes a selected message using one of a plurality of jobs that analyze the target data in the selected message to determine target databases to record data associated with the selected message. The job stores the associated record data in the appropriate target databases, and a message processing rate is determined for that job. Using the message processing rate for all of the jobs, the total number of messages that the jobs are able to process in a predetermined period is determined. The current number of pending messages is also determined. An efficiency parameter is determined by subtracting the total number of messages that the jobs are able to process from the current number of pending messages. An additional job is started when the efficiency parameter is less than a predetermined threshold.

Abstract: Systems and methods are provided for generating a listing of equivalent snapshot objects referenced by a set of object-based snapshots representing data of a corresponding block-storage volume at a point-in-time. Generating the listing comprises: retrieving a set of main manifests corresponding to the set of object-based snapshots, retrieving a set of co-pair manifests corresponding to the set of object-based snapshots, each co-pair manifest corresponding to an object-based snapshot and including a listing of encrypted snapshot objects with equivalencies to unencrypted snapshot objects, identifying data duplication in the set of main manifests based on listings of encrypted snapshot objects with equivalencies to unencrypted snapshot objects found in the set of co-pair manifests, and removing identified data duplication in the set of main manifests.

Abstract: One or more genetic family trees are formed that connect a plurality of individuals based on measures of genetic relatedness between the individuals. A plurality of the individuals are grouped into a cluster that comprises those individuals who are genetically related to (1) a target individual according to first data and (2) each other according to second data. The first data comprises genetic relatedness measures for a plurality of the individuals relative to the target individual, and the second data comprises genetic relatedness measures between a plurality of the individuals. The cluster is deconstructed into a tree structure based on the first data, and family tree(s) are iteratively formed based on pairs of adjacent nodes from the tree structure until the nodes are positioned in family tree(s) based on the second data. The family tree(s) can be connected to the target individual based on the first data.