Abstract: Techniques herein are for query editing with semantic analysis of a query based on information extracted from a tuple graph. In an embodiment, a computerized method involves processing a dataset to extract an extracted schema that describes types and relationships that occur within the dataset. The dataset is not associated with a schema that is not contained in the dataset. The dataset has a graph of tuples. During an incremental parse, an abstract syntax tree (AST) that represents a query is modified. The extracted schema and the dataset are used to perform semantic analysis on the AST. In an embodiment, the tuples are resource description framework (RDF) triples. In an embodiment, the RDF triples include RDF schema statements. Extracting an extracted schema involves processing RDF schema statements. In an embodiment, the query is a SPARQL query and semantic analysis includes error alerting and code completion.

Abstract: Techniques for identifying, in a target graph, subgraphs that match a query graph are provided. Processing a query graph comprises multiple stages, one for each query node in the query graph. In the first stage, a query node is selected, different portions of the target graph are assigned to different threads, each thread identifies nodes that match the selected query node and stores the identities of those nodes in storage that is local to the thread. The results of each thread are then stored in a “global” data structure. In the second stage, a second query node is selected and different portions of the global data structure are assigned to different threads. Each thread identifies nodes that match the second query node and that are connected to a previously-matched node. The second stage repeats until all nodes in the query graph are processed.

Abstract: A graph processing system that supports automatic data model conversion from Resource Framework Description (RDF) to Property Graph (PG) is provided. Rather than using a naive conversion approach that creates PG nodes and edges without properties, a set of conversion rules is evaluated to automatically convert RDF triples into PG nodes and edges with properties, as appropriate. Accordingly, the converted PG data takes full advantage of the PG format while advantageously avoiding the creation of extraneous nodes and edges, allowing queries on the PG data to be efficiently executed on any database supporting the PG data model. The plurality of rules categorize each triple into three different cases depending on whether or not the predicate is “rdf:type” and whether or not the object is a literal value, generating graph entities as appropriate for each case. Optionally, user defined rules may override the automatic rules.

Abstract: Techniques herein are for query editing with semantic analysis of a query based on information extracted from a tuple graph. In an embodiment, a computerized method involves processing a dataset to extract an extracted schema that describes types and relationships that occur within the dataset. The dataset is not associated with a schema that is not contained in the dataset. The dataset has a graph of tuples. During an incremental parse, an abstract syntax tree (AST) that represents a query is modified. The extracted schema and the dataset are used to perform semantic analysis on the AST. In an embodiment, the tuples are resource description framework (RDF) triples. In an embodiment, the RDF triples include RDF schema statements. Extracting an extracted schema involves processing RDF schema statements. In an embodiment, the query is a SPARQL query and semantic analysis includes error alerting and code completion.

Abstract: Techniques for identifying, in a target graph, subgraphs that match a query graph are provided. Processing a query graph comprises multiple stages, one for each query node in the query graph. In the first stage, a query node is selected, different portions of the target graph are assigned to different threads, each thread identifies nodes that match the selected query node and stores the identities of those nodes in storage that is local to the thread. The results of each thread are then stored in a “global” data structure. In the second stage, a second query node is selected and different portions of the global data structure are assigned to different threads. Each thread identifies nodes that match the second query node and that are connected to a previously-matched node. The second stage repeats until all nodes in the query graph are processed.

Abstract: Automatic verification of determinism in structured parallel programs includes sequentially establishing whether code for each of a plurality of tasks of the structured parallel program is independent, outputting sequential proofs corresponding to the independence of the code for each of the plurality of tasks and determining whether all memory locations accessed by parallel tasks of the plurality of tasks are independent based on the sequential proofs.

Abstract: Automatic verification of deteiminism in structured parallel programs includes sequentially establishing whether code for each of a plurality of tasks of the structured parallel program is independent, outputting sequential proofs corresponding to the independence of the code for each of the plurality of tasks and deteimining whether all memory locations accessed by parallel tasks of the plurality of tasks are independent based on the sequential proofs.