Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine-readable instructions that when executed by the processor, may cause the processor to receive a request for result data from a requestor and determine queries to create the result data. The processor may determine a subset of a data set based on the queries. The subset of the data set may be displayed in small multiples by the requestor. The processor may output the subset of the data set as the result data to the requestor. In some examples, the processor may receive a request for additional result data from the requestor. The processor may determine a second subset of the data set to be displayed in the small multiples and output the second subset of the data set as the additional result data to the requestor.

Abstract: A method includes: creating, by a computing device, a virtual consolidation layer; replicating, by the computing device, snippets that are results of a query; copying, by the computing device, the replicated snippets to the virtual consolidation layer; performing, by the computing device and by a machine learning model, matching of particular ones of the snippets in the virtual consolidation layer; and producing, by the computing device, a search result comprising the matched snippets.

Abstract: Systems and methods for a media guidance application that generates results in multiple languages for search queries. In particular, the media guidance application ranks search results according the language model associated with the search result.

Abstract: A computer receives a visual specification, which specifies a data source, visual variables, and data fields from the data source. Each visual variable is associated with either data fields (e.g., dimension and/or measures) or filters. The computer obtains a data model encoding the data source as a tree of related logical tables. Each logical table includes logical fields, each of which corresponds to either a data field or a calculation that spans logical tables. The computer generates a dimension subquery for the dimensions and the filters. The computer also generates, for each measure, an aggregated measure subquery grouped by the dimensions. The computer forms a final query by joining the dimension subquery to each of the aggregated measure subqueries. The computer subsequently executes the final query and displays a data visualization according to the results of the final query.

Abstract: The method receives a visual specification, which specifies a data source, visual variables, and data fields from the data source. Each visual variable is associated with data fields and each data field is either a dimension or a measure. From an object model of the data source, the method identifies a minimal subtree that includes all of the dimension data fields and constructs a query from the minimal subtree. The method executes the query against the data source to retrieve a set of tuples, each tuple comprising a unique ordered combination of data values for the dimension data fields. For each tuple, the method forms an extended tuple by appending aggregated data values corresponding to each measure data field. The method then builds and displays a data visualization according to the data fields in the extended tuples and according to the visual variables to which the data fields are associated.

Abstract: In one embodiment, a device, in communication with a plurality of data lake sites, receives a federated data lake query. The device determines a plurality of data lake operator sets that each correspond to one of the plurality of data lake sites, wherein each of the plurality of data lake operator sets is used to establish a respective data pipeline for the federated data lake query. The device selects a particular data lake site of the plurality of data lake sites as a destination for data pipelines that are established for the federated data lake query. The device sends the plurality of data lake operator sets that each correspond to one of the plurality of data lake sites to cause the plurality of data lake sites to send query results to the particular data lake site using the data pipelines, wherein the particular data lake site stitches the query results.

Abstract: The systems and techniques include storing topology from each of a plurality of source tools as a plurality of source subgraphs in a graph database using a source schema that mirrors a source topology model for each of the plurality of source tools. Each of the plurality of source subgraphs in the graph database is transformed to a plurality of destination subgraphs using a destination schema and transformation rules that mirror a destination topology model for each of a plurality of destination tools. The plurality of destination subgraphs are stored in the graph database. The topology is delivered to each of the plurality of destination tools by traversing the plurality of destination subgraphs in the graph database and invoking application programming interfaces (APIs) for each of the plurality of destination tools in the destination subgraphs in the graph database.

Abstract: Systems and methods for implementing a multi-factor financial ontology framework are disclosed. In exemplary embodiments, a computer-implement method executing on a computer receives a natural language query from a user device and parses the query to determine a set of natural language terms associated with financial product criteria. The computer system maps the natural language terms to data nodes that implement the financial ontology framework and identifies responsive financial products from those data nodes. The computer system transmits the data corresponding to the responsive financial product to the user device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating data defining a graph. In one aspect, a method comprises: sequentially generating a respective edge set for each node in the graph, wherein for each of a plurality of nodes after a first node, generating the edge set for the node comprises: receiving a context embedding for the node that summarizes a respective edge set for each node that precedes the node; generating, based on the context embedding for the node: (i) a respective edge set for the node, and (ii) a respective embedding of the edge set for the node; generating a context embedding for a next node in the ordering of the nodes using the embedding of the edge set for the node; and adding the set of edges defined by the edge set for the node to the graph.

Abstract: Described are techniques for managing data locations of derived storage objects. The techniques include accessing a primary metadata tree providing a lookup path using a virtual address to locate data for a primary object in physical storage pointed to by a leaf node of the primary metadata tree and providing a sub-tree from a leaf node of the primary metadata tree associated with the primary object, the sub-tree providing lookup paths using virtual addresses for derived objects. Wherein a derived object is a view of the primary object at a specific point in time, with copy leaf nodes of the sub-tree pointing to stored data of the derived objects. The techniques further include maintaining an ancestry graph associated with the sub-tree with the ancestry graph linking derived object identifiers to provide an ancestral chain lookup to identify derived object identifiers of the closest stored physical data of a derived object.

Abstract: Automatic data modeling in a low-latency data access and analysis system includes identifying an analytical-object in response to first data expressing usage intent, generating an analytical model generation data-query for the analytical-object, obtaining a trained analytical model generated in accordance with the analytical model generation query and trained using results data obtained in accordance with the analytical-object, generating a resolved-request representing second data expressing usage intent and indicating a request for results data obtained using the trained analytical model, generating an analytical model results data-query for obtaining the results data in accordance with the trained analytical model and the analytical-object, and outputting data for presenting a visualization of the results data obtained by executing the analytical model results data-query, wherein a first portion of the results data corresponds with the analytical-object and a second portion of the results data corresponds

Abstract: Mechanisms are provided to implement a visual analytics pipeline. The mechanisms generate, from an input database of records, a chronology-aware graph data structure of a plurality of records based features specified in an ontology data structure. The chronology-aware graph data structure has vertices representing one or more of events or records based features corresponding to events, and edges representing chronological relationships between events. The mechanisms execute a chronology-aware graph query on the chronology-aware graph data structure to generate a filtered set of vertices and corresponding features corresponding to criteria of the chronology-aware graph query.

Abstract: Systems and methods that are adapted for automatic curation of query responses are disclosed herein. An example method includes obtaining user action metrics corresponding to responses provided in reply to a query for a target resource, the query having a search term, determining a portion of the responses having user action metrics with statistical significance, generating a list of curated responses based on the portion of the responses, and providing the curated responses in reply queries having the search term.

Abstract: Operating a low-latency database analysis system with phrase translation may include obtaining a locale-specific phrase localization rule and a canonical phrase localization rule for a phrase, generating a locale-specific index and a locale-specific finite state machine for the locale using the localization definition data and a canonical finite state machine, generating a resolved-request by obtaining a locale-specific token representing locale-specific input data by traversing the locale-specific index, obtaining a canonical token associated with locale-specific token, obtaining a locale-specific phrase by traversing the locale-specific finite state machine, obtaining a canonical phrase corresponding to the locale-specific phrase, the canonical phrase including the canonical token, generate a data-query based on the canonical phrase, obtaining results data responsive to the data expressing the usage intent by executing a query corresponding to the data-query by an in-memory database of the low-latency datab

Abstract: An approach is disclosed for optimizing jobs, which involve effective data retrieval across multiple data sources, via an externalized query pattern. The approach receives a query. The approach allocates computing power to one or more computing nodes to process a job for the received query. The approach determines whether the job for the received query corresponds to an identifier indexed in a search engine. The approach retrieves, in response to determining the job for the received query corresponds to the indexed identifier, attribute data corresponding to the indexed identifier from the search engine. The approach provides a response to the received query request, in which the response includes the attribute data corresponding to the indexed identifier.

Abstract: A particular option selection may be provided by an online service platform (OSP) to a client regarding one or more predefined services that the OSP can be configured to perform. These may include automatic activities option selection(s) that may trigger the OSP to automatically start one or more activities upon one or more thresholds being crossed. The OSP may apply digital rules to the dataset(s) of the client to determine whether or not to automatically start or stop activities, including producing respective resources, upon a threshold being crossed, to determine a requested resource and then push, send, or otherwise cause to be transmitted a notification regarding the produced resource or other activities.

Abstract: Distributed pseudo-random subset generation includes obtaining a data-query indicating a first table having a first column including unique values, a second table having a second column including unique values, a join clause joining the first table and the second table on the first column and the second column, and a limit value, pseudo-random filtering the first table to obtain left intermediate data and left filtering criteria, pseudo-random filtering the second table to obtain right intermediate data and right filtering criteria, obtaining intermediate results data by full outer joining the left intermediate data and the right intermediate data, obtaining results data by filtering the intermediate results data using most-restrictive filtering criteria among the left filtering criteria and the right filtering criteria, and outputting the results data, wherein outputting the results data includes limiting the cardinality of rows of the results data to be at most the limit value.

Abstract: Embodiments of the invention identify entities stored within or across a number of data stores and identify relationships between the entities. A relationships graph is generated that represents the entities and the identified relationships between entities, the relationships graph comprising nodes in the relationships graph to represent one or more entities and edges between any two nodes in the relationships graph to represent the identified relationships between the one or more entities represented by each of the two nodes. The relationships graph is stored in a graph store. A graph query is received against selected nodes and edges in the graph store. One or more data store queries are generated therefrom, to be executed against respective selected ones of the one or more entities and their respective identified relationships based on the graph query and the graph store. The one or more data store queries are applied to selected one or more of the number of data stores.

Abstract: Mechanisms for constructing and querying a database are described. Constructing the database includes accessing the database comprising multi-component data and constructing an augmented succinct trie (AST) as an index of the database. The database includes sorted data blocks. Construction of the AST is based on the multi-component data. The AST includes indications of boundaries of the data components of the multi-component data. Querying the database includes accessing a key comprising multiple data components and augmenting the key data with data component boundary information. The AST is searched using the augmented key and a search result provided as a value corresponding to the key in the database.

Abstract: Techniques described herein allow for accurate translation of natural language (NL) queries to declarative language. A syntactic dependency parsing tree is generated for an NL query, which is used to map tokens in the query to logical data model concepts. Relationship-type mappings are completed based on relationship constraints. Final mappings are identified for any relationship tokens that are associated with multiple candidate mappings by identifying which candidate mappings have the lowest cost metrics. An NL query-specific query graph is generated based on the mapping data for the NL query and the logical data model. The query graph represents an NL query-specific version of the logical data model where grammatical dependencies between NL query words are translated to the query graph. A query graph is annotated with information, from the mapping data, that is not represented paths in the query graph. The query graph is used generate a computer-executable translation of the NL query.