Executing a query in a disaggregated cluster. A query is received at the disaggregated cluster. A query graph is created based on the query that identifies a hierarchy of vertices, where each vertex is associated with a set of data responsive to at least a portion of the query. The compute nodes process the query graph by first identifying all tables, files, and objects stored on the storage nodes whose access is required to retrieve data that satisfy the query. Next, the compute nodes selectively assign the identified tables, files, and objects to a leaf vertex of the query graph to optimize retrieving data from the storage nodes. Thereafter, the compute nodes process the retrieved data sets associated with each vertex using separate threads of execution for each vertex of the query graph such that leaf vertices are performed in parallel. The compute nodes then provide a result set.

A system can include one or more processing modules and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processing modules and perform receiving, from an electronic device, a search query from a user of a plurality of users; processing first data; and facilitating displaying a set of items. Processing the first data can comprise determining one or more keywords by capturing the one or more keywords during a time window; creating a feature set of second data associated with at least a portion of the plurality of users; determining a set of items of the item set as being based at least in part on an item vector representation and a keyword vector representation; determining a respective purchase probability associated with each item of the set of items of the item set; ranking the set of items.

Methods and systems for generating a response to a query using an AI engine. One method includes translating a received query into a profile comprised of at least a query type and a priority. The method further includes collecting, based on the profile, a first set of data items from a first data file storing data categorized as general public data and a second set of data items from a second data file storing data categorized as personal private information and applying an AI correlation engine to the second set of data items to determine PPI-agnostic relevancy indicators for PPI included in the second set of data items. The method further includes masking, based on the query cluster profile, the second set of data items by replacing the PPI included in the second set of data items with the PPI-agnostic relevancy indicators.

Techniques described herein allow a user of an RDBMS to specify a graph algorithm function (GAF) declaration, which defines a graph algorithm that takes a graph object as input and returns a logical graph object as output. A database dictionary stores the GAF declaration, which allows addition of GAFs without changing the RDBMS kernel. GAFs are used within graph queries to compute output properties of property graph objects. Output properties are accessible in the enclosing graph pattern matching query, and are live for the duration of the query cursor execution. According to various embodiments, the declaration of a GAF includes a DESCRIBE function, used for semantic analysis of the GAF, and an EXECUTE function, which defines the operations performed by the GAF. Furthermore, composition of GAFs in a graph query is done by supplying, as the input graph argument of an outer GAF, the result of an inner GAF.

Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries on top of the relational engine by avoiding any change in the existing SQL engine.

Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries on top of the relational engine by avoiding any change in the existing SQL engine.

Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries on top of the relational engine by avoiding any change in the existing SQL engine.

Computerized methods and systems retrieve a set of resources from at least one backend collection of resources in response to a resource query. Each resource has an associated backend resource type comprising one or more backend resource attribute. At least one view is identified, from a set of predefined views, that is satisfied by the retrieved set of resources. For each backend resource attribute of the retrieved set of resources, existence of a transformation between the backend resource attribute and a corresponding frontend attribute associated with the at least one view is verified in order to identify the at least one view. A selected view is selected from the identified at least one view based on a query intent derived at least in part from the resource query. A representation of the retrieved set of resources is generated according to the selected view.

A method implements a table discovery service. The method includes receiving a query string, converting the query string to a query graph, and identifying a selected graph, of a set of graphs, that matches the query graph. The method further includes transmitting a notification identifying a previously generated table corresponding to the selected graph, receiving a notification response to accept the previously generated table, and transmitting data from the previously generated table in response to the query string.

Data in a database can be protected, for instance by tokenizing the entries of the database using one or more token tables. To enable searching data within the database without first detokenizing the tokenized database entries, bigrams of each data entry can also be tokenized and stored in association with the tokenized data entry. When a query term is received, the query term can be parsed into bigrams, and each bigram can be tokenized. The tokenized query bigrams can be used to query the database, and tokenized database entries corresponding to tokenized bigrams that match the tokenized query bigrams can be identified and returned as search results.