Abstract: A system receives a replication credential associated with a request to replicate a dataset to a destination partition of a cloud environment. The replication credential includes a destination identifier that identifies the destination partition as a destination for replicating the dataset. The system accesses a replication tag associated with the dataset that defines a replication policy for the dataset. The replication tag includes a destination key that identifies the destination as being a permissible destination for replicating the dataset in accordance with the replication policy. The system determines that replication is permissible based on successfully validating that the destination key corresponds to the destination partition identified by the destination identifier of the replication credential. Responsive to determining that replication is permissible, the system initiates a set of one or more operations to replicate the dataset to the destination partition.

Abstract: Systems and techniques are disclosed for determining and provisioning and data resources for use in migrations data processing systems. Source data processing system metadata may be used to estimate data storage requirements for a migration and subtask parameters may be used to determine processing requirements. Migration resources may be determined based on these requirements and provisioned to perform migration operations. A migration may be monitored for resource utilization and resources allocated to the migration may be adjusted to increase migration efficiency and performance.

Abstract: The subject technology receives first data. The subject technology transforms first data to a wide format, the wide format comprising a second table. The subject technology splits a set of rows into a set of shards of rows. The subject technology, for each shard from the set of shards, sends a particular user defined table function (UDTF), including a particular shard of rows, to a different execution node to perform a set of operations for determining a rolling correlation over a window size. The subject technology provides a set of output values of each particular UDTF corresponding to each shard from the set of shards in a second UDTF. The subject technology sends the second UDTF to a particular execution node to perform an aggregate operation. The subject technology receives a value of the rolling correlation from the particular execution node based on the aggregate operation.

Abstract: Techniques are disclosed relating to orchestrating locking between database nodes of a database system. A database node can determine that an execution of a database transaction at the database node involves acquiring a lock. The database node acquires, from a separate lease manager node, a lease object that permits the database node to create the lock for the database transaction. As a part of provisioning that lease object to the database node, the lease manager node ensures that a lease object for creating locks that conflict with the lock is not held by another database node. The database node creates the lock for the database transaction based on the acquired lease object. As a part of creating that lock, the database node ensures that the lock does not conflict with a lock held by another database transaction executing at the database node.

Abstract: The method of assessing the validity of factual claims determines if a purported factual claim, such as a statement made on social media, in the news, etc., is “true”, “false” or “unknown” through comparison with corresponding ranked factual claims stored in a comparison database. The comparison database is assembled by storing a plurality of sets of digital content items, where each of the sets of digital content items has a unique topic. Factual claims within each set are ranked by the number of agreements between individual items within each set. A query item to be assessed is input, and at least one purported factual claim to be assessed is extracted. This purported factual claim is then compared against a predetermined number of highest-ranking factual claims within the corresponding set. A validity score is then generated based on the number of agreements and the number of disagreements from the comparison.

Abstract: As described herein, a system, method, and computer program are provided for providing shard-aware bulk requests for a Cassandra No-SQL database system having a plurality of distributed nodes with replication. A request to perform a database operation on the Cassandra No-SQL database system is received from an application. Token ranges of partition keys included with the request are determined. The request is grouped with a plurality of additional database operation requests to form a bulk request, where requests are grouped by a target node in the Cassandra No-SQL database system such that the target node holds token ranges for all requests included in the bulk request. The bulk request is communicated to the target node of the Cassandra No-SQL database system for processing of the requests included in the bulk request.

Abstract: Systems and methods of searching content using one or more machine learning models are disclosed. In response to a user query request, a user query is received at a first user interface. One or more search terms are determined using a query parser based on the user query. One or more semantically similar queries are determined based on the one or more search terms. In accordance with a determination that the user query and the one or more semantically similar queries have not been previously searched, search for information related to the one or more search terms from one or more databases using the one or more machine learning models. An output to the user query is generated based on the information. The output includes at least a portion of the information and one or more citations associated with the information. The output is displayed at the first user interface.

Abstract: A data query apparatus, method, and storage medium include a database system that determines a data collection and a calculating model corresponding to a received query task request, according to the number of bytes occupied when the data collection or the calculating model is stored, or the number of bit widths occupied when the data collection or the calculating model is transmitted, the database system determines a transmission rate of the data collection or the calculating model. The query task request is transmitted to a model node or a data node with a lower transmission rate, and the data node or the model node with a higher transmission rate is dispatched to perform data calculation.

Abstract: Pipelining a binary search algorithm of a sorted table is performed by breaking a single sorted table into multiple smaller tables, such that a binary search can produce a result every clock cycle. A process includes storing a plurality of tables that collectively form a single sorted table that includes addresses and entries corresponding to each address where the entries are sorted, each of the plurality of tables include a subset of the addresses and corresponding entries; and performing a binary search where each iteration of the binary search searches one of the plurality of tables such that a complete search traverses through all of the plurality of tables and such that a plurality of searches are performable concurrently pipelined through each of the plurality of tables.

Abstract: In order to facilitate dynamic graphing of entity networks based on activity, systems and methods include a processor receiving entity-specific data records and a plurality of entity-related activity records for a plurality of entities, where each entity-specific activity record includes activity data regarding at least one activity associated with an entity. The processor generates graph nodes for an entity activity graph based on the plurality of entity-specific data records, where each graph node of the plurality of graph nodes represents the particular entity and then generating an activity data structure, including the graph nodes and edges between the graph nodes, where the edges represent characteristics of the activities between graph nodes based on the entity-related activity record.

Abstract: Coordinating processing of audio queries is provided. A system receives a query. The system provides the query to a first digital assistant component and a second digital assistant component for processing. The system receives a first response to the query from the first digital assistant component, and a second response to the query from the second digital assistant component. The first digital assistant component can be authorized to access a database the second digital assistant component is prohibited from accessing. The system determines, based on a ranking decision function, to select the second response to the query from the second digital assistant component. The system provides, responsive to the selection, the second response from the second digital assistant to a computing device.

Abstract: Systems, methods, and computer-readable storage media for using bots. One method includes providing a bot corresponds with a triggering event and receiving an input indicative of a selection of the bot. The method further includes accessing user data comprising at least one user preference of a user and identifying a selection of bot options based on analyzing the user data, wherein the selection of bot options corresponds with at least two third-parties, and wherein the identified selection of bot options comprises at least one good or service from parties of the at least two third-parties. The method further includes presenting the selection of bot options, receiving a selection of an option from the selection of bot options, and accessing user payment information and populate one or more fields corresponding with the selection of the option.

Abstract: Typically, in-depth domain knowledge is required to efficiently query a data system. Accordingly, embodiments are disclosed for a no-code method for implementing data operations, such as extract, transform, load (ETL) operations on third-party platforms, such as enterprise resource planning (ERP) platforms. Disclosed embodiments may provide a graphical user interface that enables a user to construct a blueprint of a data operation using drag-and-drop operations on a virtual canvas. The blueprint may be translated into ETL instructions that are deployed on a proxy service for simplified access via a uniform resource locator (URL). When the URL is accessed, the proxy service may execute the ETL instructions to communicate with a domain-specific engine on a third-party platform to obtain the results of the data operation. These results may also be periodically cached according to time-to-live intervals to reduce overall retrieval times.

Abstract: A system and method for non-invasively monitoring and troubleshooting a massive parallel processing environment that may include a digital ecosystem having a data lake. The system may include a data transporter, a data ingester, a data chef, and a separate message producer at each of the data transporter, data ingester, and data chef. Log data from the message producers may be transmitted to a memory associated with a processor for organization of the log data by topic, such as a Kafka topic, into instrumentation data that may indicate performance of different portions of the system. The system may further include a message consumer, such as Kafka message consumer, to enable the presentation of the instrumentation data to be interactively displayed to a user on one or more electronic dashboards for monitoring and troubleshooting of performance issues.

Abstract: Embodiments of the present disclosure provide a data query method based on on-line analytical processing, an electronic device and a storage medium. The method includes: receiving a first query request, and determining a request type of the first query request; executing a query in a row-based storage portion of an event table of a database in response to the request type being a preset type; and generating a query result based on retrieved target data, wherein the event table includes a column-based storage portion and the row-based storage portion, and the column-based storage portion includes a plurality of data rows; and the row-based storage portion includes a coding column, and each piece of coded information in the coding column includes row data belonging to a same data row in the column-based storage portion.

Abstract: A system and methods for efficiently and consistently creating and delivering content tailored to a user's specific needs and channel of distribution. The system generates coherent, precise, and logical content that is in full compliance with the relevant policies, rules, regulations, and laws of users of the system. The content created and delivered by the system may be in any form, such as, but not limited to a video, a script, a press release, a social media post, and a keynote speech.

Abstract: Computer-readable media, methods, and systems are disclosed for validating data associated with schemas. A user defines the object model of at least one asset and a first schema is generated in accordance with the defined object model, and a unique fingerprint is generated. Data is collected from one or more devices in accordance with the object model. The collected data is serialized, and a second schema is generated. The second schema is ordered in accordance with the first schema and a unique fingerprint is generated. The fingerprint of the first schema is compared to the fingerprint of the second schema to provide an efficient review process for determining whether the schemas are equal, and the associated data may be validated. A fingerprint cache may be updated with fingerprints associated with a plurality of schemas, as well as version history of each schema, to provide an efficient review process.

Abstract: The present disclosure relates to system and method for consolidating data attributes for optimizing data processing. The method comprises defining objectives associated with data along with data outcome expected after processing of data from a sample dataset through processor. The data outcome is predicted based on metadata associated with the data from the sample dataset. The method further comprises identifying metadata insights for classifying metadata into a plurality of metadata types through metadata crawler, generating an experienced based metadata by filtering metadata with incomplete information from the metadata according to the objectives and data output expected through the processor. The experience-based metadata comprises one or more metadata types from the plurality of metadata types and generating consolidated sample data by using the experienced based metadata through the processor.

Abstract: Systems, methods, and computer-program products for entity resolution are disclosed. Entity resolution embodiments include receiving tree data from each of a pair of entities, extracting and/or aggregating feature vectors or metric functions therefrom, and generating similarity scores between the pair of entities. The similarity scores may be weighted using machine-learned weights. The weighted similarity scores are used to generate a combinatorial probability score accounting for combined likelihoods of field values between the pair of entities. A classification of the pair of entities is performed based on the combinatorial probability score, with a genealogical database modified based on the classification.

Abstract: An online system leverages stored interactions with items made by users after the online system received queries to determine display of items satisfying the query. For example, the online system trains a model to predict a likelihood of a user performing an interaction with an item displayed after a query was received. As different items receive different amounts of interaction from users, limited historical interaction with certain items may limit accuracy of the model. The online system generates embeddings for previously received queries and uses measures of similarity between embeddings for queries to generate clusters of queries. Previous interactions with queries in a cluster are combined, with the combined data being used for determining display of items in response to a query.