Abstract:
A relational database system may include a query optimizer and a query execution engine. The query optimizer may be configured to receive a query from a query-generating entity and to determine a sequence of operations for executing the query. The query execution engine may use real-time statistics to select physical operators for performing the sequence of operations.
Abstract:
A system that creates a unique calendar event for an associated calendar resource in a standard calendaring system, for work to be performed in a critical infrastructure environment, based on a method of procedure document.
Abstract:
A configuration workstation generates configuration tables representative of applications that each comprise one or more object data structures. Each of the object data structures are linked to one or more staging databases that, in turn, obtain data from one or more standalone data sources. Each of the object data structures comprises at least one property that defines available data for the object data structure. The configuration tables are provided to a controller that obtains data from the staging database(s). The controller also causes the obtained data to be converted to the semantic data format and stored in a semantic database. A web server obtains requested semantic data from the semantic database for at least some of the object data structures for an application. The web server then generates a user interface based on the requested semantic data and provides it to a user device for display.
Abstract:
Aggregation in a computing system can include receiving, at a service node of the computing system, a first query specifying aggregation and translating the first query into a second query having a first canonical format and specifying the aggregation. The method can include forwarding the second query to a first subset of a plurality of endpoint nodes and translating, at each endpoint node of the first subset, the second query into a third query having a format executable by a data source connected to the endpoint node. The third query can specify a level of the aggregation to be performed by the data source determined based upon a processing capability of the data source. The endpoint nodes can initiate execution of the third query by the data sources and provide an aggregated result including a result from the data source(s) to the service node.
Abstract:
A technique is described for generating a knowledge graph that links names associated with a first subject matter category (C1) (such as brands) with names associated with a second subject matter category (C2) (such as products). In one implementation, the technique relies on two similarly-constituted processing pipelines, a first processing pipeline for processing the C1 names, and a second processing pipeline for processing the C2 names. Each processing pipeline includes three main stages, including a name-generation stage, a verification stage, and an augmentation stage. The generation stage uses a voting strategy to form an initial set of seed names. The verification stage removes noisy seed names. And the augmentation stage expands each verified name to include related terms. A final edge-forming stage identifies relationships between the expanded C1 names and the expanded C2 names using a voting strategy.