Oracle International Corporation

Souripriya Das

Jagannathan Srinivasan

Eugene Inseok Chong

Aravind Yalamanchi

George Eadon

Zhe Wu

Matthew Steven Perry

Charles G Freiwald

Jayanta Banerjee

Melliyal Annamalai

Victor Antonio Lopez Villamar

Juan Francisco Garcia Navarro

Ramkumar Krishnan

Jogannathan Srinivasan

Chuck Freiwald

Ying Hu

Ananth Raghavan

Seema Sundara

Shrikanth Shankar

John Ray Thomas

Anil Nori

Vladimir Kolovski

Anh-Tuan Tran

Mahesh Jagannath

Samuel DeFazio

Timothy Chorma

Samuel De Fazio

Ruben Alejandro Gonzalez Yanez

Daniel Diaz Lopez

Techniques are provided for generating and maintaining Precomputed Result Tables ('PRTs') that are used by a database server to improve performance of SPARQL queries that target an RDF graph set. Each PRT corresponds to a particular pattern, which may be simple-triple, star, or chain. The techniques support the creation of PRTs for chain patterns that contain multiple instances of the same property, and PRTs for star and chain patterns that include reverse properties. Techniques are also described for maintaining such PRTs as Data Manipulation Language (DML) operations make changes to the RDF graph tables that belong to the RDF graph set associated with the PRTs.

Using pattern-based result tables to speed up SPARQL queries on RDF graph sets

Disclosed is an improved approach to represent RDF data in a database system, where one or more auxiliary tables are maintained for the RDF data. The auxiliary tables advantageously permit a database processing system to process a query using fewer numbers of joins or self-joins.

Method and system for using auxiliary tables for RDF data stored in a relational database

Embodiments use successive refinement to allow a user to systematically explore the result set of an arbitrary query over RDF, such as a SPARQL query. A user inputs an arbitrary base query and modifies this query by replacing selected variables with values to which each selected variable is bound within the result set of the base query. Embodiments present, via a GUI, variable facets that may be substituted for query variables. Embodiments also present, through a GUI, a query history graph that represents query versions that a user has created. A user may navigate this query history graph to return to previously-created query versions. The GUI also provides information about the facets, including a number of results that would be included in the result set of the query version resulting from substitution of the facet for the associated variable.

Method for faceted visualization of a SPARQL query result set

Systems and methods for visualizing relational data as RDF graphs in order to explore connections between data in the relational schema. The relational data is first converted into an initial RDF graph. Referential constraints between tables in the relational data, including unasserted referential constraints and pseudo-referential constraints, are automatically detected and used to augment the RDF graph. In addition, datatype properties in the RDF graph may be folded into annotation objects for better visualization. The resulting graph may be an edge-node graph, with edges corresponding to referential constraints between nodes corresponding to relational table rows.

Method and system for visualizing relational data as RDF graphs with interactive response time

Systems, methods, and other embodiments associated with resource description framework (RDF) security are described. One example method includes generating, based on sensitivity labels associated with the contents of a triple in an RDF record, a sensitivity label. The example method may also include comparing the sensitivity label to an access label associated with an entity requesting an action associated with the record to be performed. The example method may also include performing the action upon determining that the entity has sufficient permission to request the action.

Resource description framework security

A method, system, and computer program product for normalized ranking of semantic query search results. The method commences by forming a SPARQL query, the SPARQL query specifying a collection of named RDF graphs, then executing the SPARQL query to retrieve matched documents selected from the collection of named RDF graphs. Having retrieved the matches, the method calculates an absolute normalized score for the matched document. The calculation of an absolute normalized score for a second matched document allows for relevance ranking. To enhance the relationship of the computer-calculated score to the relevance (and not solely to occurrence counts) embodiments consider the number of distinct variables selected in the SPARQL query, and also consider the total number of bindings to the aforementioned variables. For comparing from among possibly different sized documents, a further normalization component is implemented by considering the total number of triples found in the document's underlying subgraph.

Normalized ranking of semantic query search results

Techniques for generating hash values for instances of distinct data values. In the techniques, each distinct data value is mapped to hash value generation information which describes how to generate a unique hash value for instances of the distinct data value. The hash value generation information for a distinct data value is then used to generate the hash value for an instance of the distinct data value. The hash value generation information may indicate whether a collision has occurred in generating the hash values for instances of the distinct data values and if so, how the collision is to be resolved. The techniques are employed to normalize RDF triples by generating the UIDS employed in the normalization from the triples' lexical values.

Representing and manipulating RDF data in a relational database management system

The TABLE function mechanism available in a RDBMS is used to integrate RDF models into SQL queries. The table function invocation takes parameters including an RDF pattern, an RDF model, and an RDF rule base and returns result rows to the SQL query that contain RDF triples resulting from the application of the pattern to the triples of the model and the triples inferred by applying the rule base to the model. The RDBMS includes relational representations of the triples and the rules. Optimizations include indexes and materialized views of the representations of the triples, precomputed inferred triples, and a method associated with the TABLE function that rewrites the part of the SQL query that contains the TABLE function invocation as an equivalent SQL string. The latter technique is generally applicable to TABLE functions.

Integrating RDF data into a relational database system

An un-indexed, partitioned temporary table and an exchange table are used in the inferencing of semantic data in a relational database system. The exchange table has the same structure as a semantic data table storing the semantic data. In the inferencing process, a new partition is created in the semantic data table. Inference rules are executed on the semantic data table, and any newly inferred semantic data generated is added to the temporary table. Once no new data is generated, the inferred semantic data is copied from the temporary table into the exchange table. Indexes that are the same as indexes for the semantic data table are built for the exchange table. The indexed data in the exchange table is then exchanged into the new partition in the semantic data table. By use of the un-indexed, partitioned temporary table, incremental maintenance of indexes is avoided, thus allowing for greater efficiency.

Database-based inference engine for RDFS/OWL constructs

Systems, methods, and other embodiments associated with access control for graph data at the instance-level are described. One example method includes accepting data access constraints that are expressed as match and apply pattern pairs to enforce security policies. A user query on graph data with a security policy restricts the returned data to data that the user is authorized to access. For this purpose, a user query that includes query selection criteria corresponding to one or more match pattern criteria based on the resource referenced in the query is rewritten to include security conditions specified in the associated apply pattern to restrict access to the graph data.

Access control for graph data

Techniques for using arbitrary sets of objects to constrain objects in database systems. The constraint set of objects is associated with the constrained object in the database system and the database system will perform an operation which adds an object to the constrained object only if the object being added belongs to the constraint set. In a preferred embodiment, the techniques are employed to obtain constraint sets of terms from ontologies which are then used to constrain columns containing those terms. An implementation of the techniques makes a materialized view out of the objects in the constraint set and uses the materialized view to define a referential integrity constraint on the constrained column.

Column constraints based on arbitrary sets of objects

Systems, methodologies, media, and other embodiments associated with supporting reference partitioned tables in a relational database are described. One example method includes identifying a partitioning strategy (e.g., range, list, hash) associated with a parent table that is related to a child table by a referential constraint. The example method may also include creating the child table to be a reference-partitioned table partitioned according to the partitioning strategy associated with the parent table. The example method may also include creating the child table to be equi-partitioned with respect to the parent table. The child table is not to replicate a partition key of the parent table but rather is to inherit a partitioning key associated with the parent table through a foreign key relationship.

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Nashua, NH, United States of America

Souripriya Das