One or more computing devices, systems, and/or methods for searching across multiple user interfaces are provided. For example, a search query may be received from a user of a computing environment. A set of user interfaces, hosted by the computing environment, may be identified (e.g., mobile apps of a mobile operating system, browser tabs of a web browser, etc.). Content within the set of user interfaces may be queried using the search query to identify search results of the search query (e.g., webpages, social network posts, emails, or other content matching the search query). In an example, cached content of the user interfaces (e.g., recently accessed emails cached within a local content buffer cache by the web browser) may be searched using the search query for efficient querying of relevant content, such as through local or offline querying. The search results may be provided to the user.

Systems and methods are disclosed for receiving, at a data intake and query system, a query that includes an indication to process data managed by a third-party data storage and processing system that supports a different query language than the data intake and query system. The data intake and query system identifies a third-party data storage and processing system that manages the data to be processed and generates a subquery for execution by the third-party data storage and processing system, generates instructions for one or more worker nodes to receive and process results of the subquery from the third-party data storage and processing system, and instructs the worker nodes to provide results of the processing to the data intake and query system.

A method for query execution using a dual-resource pool design (e.g., a fast resource pool and a slow resource pool design) is provided. The method includes receiving a first query requesting retrieval of first data from a database, admitting the first query to a fast resource pool for execution, wherein the fast resource pool is configured with one or more thresholds for execution, executing the first query in the fast resource pool using resources allocated to the fast resource pool, during execution of the first query in the fast resource pool, determining the one or more thresholds for execution have been exceeded, admitting the first query to a slow resource pool for execution, and executing the first query in the slow resource pool using resources allocated to the slow resource pool.

Computer technology for: (i) performing prefetching based on shard workload in NoSQL; and/or (ii) perform distribution of stored data over the various tiers of a cache memory based on shard workload in NoSQL. This can help achieve better load balance among and between the shards of a database and the respectively associated nodes on which the shards are stored.

Methods and systems are disclosed for a sequential federated query for information in a residential, commercial, and/or distributed system. A method of processing a sequential federated query for distributed systems may comprise receiving a sequential federated query; deconstructing the sequential federated query into query elements; identifying a Contextual-Characteristic Data Source (CCDS), a Transactional-Unstructured Data Source (TUDS), and a data organization parameter based on the query elements; generating a CCDS result data set from the CCDS based on the data organization parameter; processing the CCDS result data set to develop a TUDS query based on the data organization parameter; generating a TUDS result data set from the TUDS based on the developed TUDS query and the data organization parameter; generating a final sequential federated query data set based on the TUDS result data set and the data organization parameters; processing a formatted sequential federated query data set based on the processing of the final sequential federated query data set and the data organization parameter; and providing the formatted sequential federated query data set to a management system for action.

A system using edge computing is enabled to efficiently acquire data of an acquisition target without fail. A data search server KSV includes an industry information table 221 which stores an edge ID of an edge server operated and managed by a company/body, the edge ID being correlated with an industry ID which identifies an industry, and an ID of the company/body belonging to the industry. When a data acquisition request designating the industry ID is received from a user terminal, edge servers correlated with the designated industry ID are searched from the industry information table 221, and sensing data is acquired from the searched edge servers and transmitted to the user terminal of the source of the request.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for neuromorphic experiential analysis of sensor data. The methods, systems, and apparatus include actions of obtaining sensor emissions from multiple sensors, generating monotonic data that indicates an orientation of the sensor emissions in respect to time, determining that the monotonic data matches a registered query, and in response to determining that the monotonic data matches a registered query, invoking an executor.

Resource management includes storing, for multiple workload pools of a data intake and query system, a workload pool hierarchy arranged in multiple workload pool layers. After storing a processing request is assigned a selected subset of workload pools in a second layer of the workload pool hierarchy based on a type of processing request. The processing request is then assigned to an individual workload pool in the selected subset to obtain a selected workload pool. Execution of the processing request is initiated on the selected workload pool.

A system and computer implemented method for execution of aggregation expressions on a distributed non-relational database system is provided. The method comprises the acts of determining, by a computer system, an optimization for execution of an aggregation operation, wherein the aggregation operation includes a plurality of data operations on a distributed non-relational database; modifying, by the computer system, the plurality of data operations to optimize execution; splitting the aggregation operation into a distributed aggregation operation and a merged aggregation operation; instructing each of a plurality of shard servers to perform the distributed aggregation operation; aggregating, at a merging shard server, the results of the distributed aggregation operation from each of the plurality of shard servers; and performing the merged aggregation operation on the aggregated results of the distributed aggregation operation from each of the plurality of shard servers.

A database node selects one of a number of database mirror hosts to perform a database read query. In theory, mirror hosts store redundant database entries. In practice, some mirror hosts fail to receive or retain some entries, e.g. if a mirror host was down for maintenance, a mirror host was overburdened, etc. The health of a mirror host, quantified as a health score, represents a likelihood that an insert statement was received and retained. Health scores are generated based on health metrics—data periodically retrieved from mirror hosts used to infer how well the database was operating, e.g. a count of entries that were inserted, a count of errors encountered while the entries were inserted, etc. If the database read query specifies entries inserted during a specific time period, the mirror host may be selected based on the health scores representing that time period.