Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Described is a technology comprising a query processing pipeline in which a SPARQL query is processed into an intermediate LINQ query, which is then processed by a LINQ provider. The LINQ provider decides which instructions correspond to flat database queries, and routes those instructions a database engine (e.g., SQL server) for querying a database. Other instructions are provided to a reasoning engine for processing, e.g., by performing a graph traversal and/or database queries. The pipeline may include a parser that parses the query into an abstract syntax tree, and an optimizer that processes the abstract syntax tree into a LINQ query, including by reordering LINQ instructions and/or associating a flag with each of the instructions that indicates whether to query the database or provide the instruction to a reasoning engine.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A data publication system is described herein that provides a data replication model that combines benefits of data distribution from non-relational paradigms with the benefits of deeply integrating datasets via relational database paradigms. The system allows the creation of programmatic functions for extracting subsets of data stored in any source model, extracting data from a variety of sources, and republishing that data in a target model built upon the aggregated source data. The target model can provide standard relational paradigms across a set of data from multiple sources, whether or not the original sources were relational in nature. The system applies known paradigms for data replication based upon programmatic functions as a means for data replication and integrates this method for data duplication and replication based upon arbitrary functions with the power of relational database systems to process associated entities of data in highly efficient ways.

Abstract: 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.

Abstract: 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.

Abstract: A semantic query may refer to a logical rule, where the rule is defined in terms of constituent expressions. In order to execute the semantic query efficiently, occurrences of the rule may be expanded by replacing the rule with its constituent expressions. Expansion may be performed repeatedly, until only grounded expressions remain. Expressions are grounded when they refer to tables or views that are represented in an underlying database. Once the rule has been reduced to grounded expressions, the semantic query processor may formulate a relational query in terms of the grounded expressions. If the relational query takes into account the various grounded expressions to which the rule reduces, then the portion of the semantic query that refers to the rule may be processed without an excessive number of round trips to the relational database.

Abstract: 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.

Abstract: A semantic query may refer to a logical rule, where the rule is defined in terms of constituent expressions. In order to execute the semantic query efficiently, occurrences of the rule may be expanded by replacing the rule with its constituent expressions. Expansion may be performed repeatedly, until only grounded expressions remain. Expressions are grounded when they refer to tables or views that are represented in an underlying database. Once the rule has been reduced to grounded expressions, the semantic query processor may formulate a relational query in terms of the grounded expressions. If the relational query takes into account the various grounded expressions to which the rule reduces, then the portion of the semantic query that refers to the rule may be processed without an excessive number of round trips to the relational database.

Abstract: In one example, information may be stored in a relational database. The information in the database may define a graph, in the sense that the information may define a set of entities and relations between the entities. A user may want to query the information using a graph-based query language. A graph query engine may receive the query, and may convert the query into a relational query language, for execution by the relational database. The relational database may calculate views of the underlying tables. Each view corresponds to a particular relation, and the rows in each view are pairs of entities to which the relation applies. Since the views correspond very closely to the specification of a graph, the graph-based query may be translated into a relational query that performs relational algebraic operations on the views in order to answer the graph-based query.

Abstract: Described is a technology comprising a query processing pipeline in which a SPARQL query is processed into an intermediate LINQ query, which is then processed by a LINQ provider. The LINQ provider decides which instructions correspond to flat database queries, and routes those instructions a database engine (e.g., SQL server) for querying a database. Other instructions are provided to a reasoning engine for processing, e.g., by performing a graph traversal and/or database queries. The pipeline may include a parser that parses the query into an abstract syntax tree, and an optimizer that processes the abstract syntax tree into a LINQ query, including by reordering LINQ instructions and/or associating a flag with each of the instructions that indicates whether to query the database or provide the instruction to a reasoning engine.