摘要:
Semantic queries are expressed and executed within a relational database. This can be done by defining semantic rules applied to execute the semantic queries using table valued functions and common table expressions, and then simply calling the defined table valued functions to execute the queries.
摘要:
Semantic queries are expressed and executed within a relational database. This can be done by defining semantic rules applied to execute the semantic queries using table valued functions and common table expressions, and then simply calling the defined table valued functions to execute the queries.
摘要:
Semantic queries are expressed and executed within a relational database. This can be done by defining semantic rules applied to execute the semantic queries using table valued functions and common table expressions, and then simply calling the defined table valued functions to execute the queries.
摘要:
Semantic queries are expressed and executed within a relational database. This can be done by defining semantic rules applied to execute the semantic queries using table valued functions and common table expressions, and then simply calling the defined table valued functions to execute the queries.
摘要:
A method and system of using a forward chaining application on a computing device to monitor a semantic storage system and invoke computations on scientific data according to declarative rules, while capturing operational provenance data stored alongside the scientific data where all data is stored in a semantic graph is disclosed and described. As the provenance data is stored with the data as nodes in the semantic graph, it will stay with the data and may be searched and queried using the same methods as searching the underlying data.
摘要:
A semantic reasoning engine is described for performing probabilistic reasoning over a semantic graph in a time-efficient and viable manner. The semantic reasoning engine includes a data store that provides the semantic graph, where the semantic graph is formed by a plurality of concepts connected together via probabilistic assertions. The semantic reasoning engine operates by providing an answer to a query by recursively collapsing the semantic graph based on at least one collapsing rule.
摘要:
Described herein is using type information with a graph of nodes and predicates, in which the type information may be used to determine validity of (type check) a query to be executed against the graph. In one aspect, each node has a type, and each predicate indicates a valid relationship between two types of nodes. A type checking mechanism uses the type information to determine whether a query is valid, which may be the entire query prior to query processing/compilation time, or as the query is being composed by a user. One or more valid predicates for a given node may be discovered based upon the node type, such as discovered to assist the user during query composition. Also described is using the type information to optimize the query.
摘要:
A semantic reasoning engine is described for performing probabilistic reasoning over a semantic graph in a time-efficient and viable manner. The semantic reasoning engine includes a data store that provides the semantic graph, where the semantic graph is formed by a plurality of concepts connected together via probabilistic assertions. The semantic reasoning engine operates by providing an answer to a query by recursively collapsing the semantic graph based on at least one collapsing rule.
摘要:
The described method/system/apparatus uses intelligence to better allocate tasks/work items among the processors and computers in the cloud. A priority score may be calculated for each task/work unit for each specific processor. The priority score may indicate how well suited a task/work item is for a processor. The result is that tasks/work items may be more efficiently executed by being assigned to processors in the cloud that are better prepared to execute the tasks/work items.
摘要:
The described method/system/apparatus uses intelligence to better allocate tasks/work items among the processors and computers in the cloud. A priority score may be calculated for each task/work unit for each specific processor. The priority score may indicate how well suited a task/work item is for a processor. The result is that tasks/work items may be more efficiently executed by being assigned to processors in the cloud that are better prepared to execute the tasks/work items.