Scaling of query processing resources for efficient utilization and performance is implemented for a database service. A query is received via a network endpoint associated with a database managed by a database service. Respective response times predicted for the query using different query processing configurations available to perform the query are determined. Those query processing configurations with response times that exceed a variability threshold determined for the query may be excluded. A remaining query processing configuration may then be selected to perform the query.

Technologies are described for storing and reporting user activities within a computing environment. For example, bitsets (e.g., compressed and/or uncompressed bitsets) can be used to store activities (e.g., where each activity is a bit in the bitset in chronological order). Separate bitsets can be maintained for followable aspects of the activities (e.g., a separate bitset for each unique followable). Activity streams can be produced from the compressed bitsets (e.g., custom streams reflecting followables designated by users).

According to one embodiment, a storage system includes a processor, a storage device, and a first memory. The storage device includes a nonvolatile memory, a control circuit, and a second memory. The processor retrieves, based on a retrieval key and retrieval information stored in the first memory, location information of data including the retrieval key and a value, and transmits the location information and the retrieval key to the control circuit. The control circuit reads the data from the nonvolatile memory based on the location information and the retrieval key, stores the data in the second memory, retrieves the value corresponding to the retrieval key from the data, and transmits the value to the processor.

An information handling system includes a hardware device having a query processing engine to provide queries into source data and to provide responses to the queries. A processor stores a query to a query address in the memory device, issues a command to the hardware device, the command including the query address and a response address in the memory device, and retrieves a response to the query from the response address. The hardware device retrieves the query from the query address in response to the command, provides the query to the query processing engine, and stores a response to the query from the query processing engine to the response address.

An accelerator is disclosed. A hardware may process a query on a database. A storage may store a software to process the query on the database. A coordinator may manage the hardware and the software to process the query on the database based at least in part on the query, to produce a result of the query on the database.

Example aspects include techniques for query processing over deep neural network runtimes. These techniques may include receiving a query including one or more query operators and determining a query representation based on the one or more query operators. In addition, the techniques may include determining a neural network program based on the query representation, the neural network program including one or more neural network operators for performing the query in a neural network runtime, generating a neural network data structure based on a dataset associated with the query, and executing the neural network program in the neural network runtime over the neural network data structure to generate a query result.

A system, apparatus, device, or method to output different iterations of data entities. The method may include establishing a first data entity; establishing a first state for the first data entity. The method may include establishing a second state for the first data entity. The method may include storing the first data entity, the first state, and the second state at a storage device. The method may include retrieving a first iteration of the first data entity exhibiting at least a portion of the first state. The method may include retrieving a second iteration of the first data entity exhibiting at least a portion of the second state. The method may include outputting the first iteration and the second iteration at an output time.

A system for creating surrogate models is provided. The system includes a user computing device including a user interface, a database, and a processor. The processor is configured to receive, from the user computing device, a workflow request, determine at least one application in the database required to complete the workflow request, retrieve, from the database, the at least one application and meta-data associated with the at least one application, and determine that a user has proper tenancies to use the at least one application. The processor is further configured to execute, using the at least one application on an external computing device, a workflow associated with the workflow request based at least in part on the meta-data, generate, based on execution of the workflow request, new meta-data associated with the at least one application, and transmit the new meta-data to the database.

Techniques for reducing CPU consumption in a federated search are disclosed. In some example embodiments, a computer-implemented method comprises determining an initial search scope by selecting a subset of searchable resources from a plurality of searchable resources based on interaction data of a user, with the initial search scope being defined by the selected subset of searchable resources, and the interaction data indicating online activity of the user directed towards past search results of past search queries submitted by the user. A federated search for a current search query is then performed using the initial search scope, with the federated search for the current search query being restricted to only the subset of searchable resources, and current search results for the current search query are generated based on the performing of the federated search.

A method, a system, and a computer program product for placement or storage of data structures in memory/storage locations. A type of a data structure for storing data and a type of data access to the data structure are determined. The type of data access includes a first and a second type of data access. A frequency of each type of access to each type of data structure accessed by a query is determined. Using the determined frequency, a number of first type of data accesses to the data structure is compared to a number of second type of accesses to the data structure. The numbers of first and second types of data access are compared to a predetermined threshold percentage of a total number of data accesses to the data structure. Based on the comparisons, a physical memory location for storing data is determined.