Oracle International Corporation

Eugene Inseok Chong

Souripriya Das

Jagannathan Srinivasan

Aravind Yalamanchi

George Eadon

Charles G Freiwald

Zhe Wu

Melliyal Annamalai

Matthew Steven Perry

Ramkumar Krishnan

Jogannathan Srinivasan

Ying Hu

Ananth Raghavan

Seema Sundara

Shrikanth Shankar

Vladimir Kolovski

Mahesh Jagannath

Anh-Tuan Tran

Timothy Chorma

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

Systems for database query processors. In a method embodiment, processing commences upon receiving a first set of a plurality of database language queries that reference a normalized column in an RDF table and that also includes a JOIN clause that references both the normalized column in the RDF table and a corresponding lookup table (e.g., a denormalization dictionary) that contains both normalized RDF data and denormalized RDF data. An in-memory table is allocated and formatted to comprise virtual columns that correspond to denormalized RDF data. Virtual columns of the in-memory table are populated with denormalized RDF data. In case of receipt of a SPARQL query, the incoming query is first translated into non-SPARQL database statements which are in turn recoded into database language statements that use lookup functions to lookup the denormalized RDF data from the virtual columns of the in-memory table rather than by incurring expensive disk I/O operations.

In-memory denormalized RDF data

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

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.

Reference partitioned tables

A system for organizing and accessing a database. The system includes a primary B+tree index, a secondary B+tree index, a plurality of mapping table row identifiers stored in the secondary B+tree index and a plurality of database addresses for leaf blocks of the primary B+tree index corresponding to mapping table rows.

Supporting B+tree indexes on primary B+tree structures with large primary keys

Rewriting table functions as SQL strings

The method for processing data in a relational database wherein ontology data that specifies terms and relationships between pairs of said terms expressed in an OWL document is stored in the database, database queries that include a semantic matching operator are formed which identify the ontology data and further specify a stated relationship between two input terms, and the query is executed to invoke the semantic matching operator to determine if the two input terms are related by the stated relationship by consulting said ontology data.

System for ontology-based semantic matching in a relational database system

A method for maintaining a system for database management. The method includes recording the addresses of newly created leaf blocks during a split operation on a primary B+tree and maintaining the new addresses in a list as part of primary B+tree metadata.

Online tracking and fixing of invalid guess-DBAs in secondary indexes and mapping tables on primary B+tree structures

A mapping mechanism for a primary B+tree in a database management system. The primary B+tree includes a plurality of rows. The mapping mechanism includes introducing a mapping table that includes a plurality of rows, including a row for each row of the primary B+tree, and that stores the logical identifier of the corresponding primary B+tree row. In addition, reverse mapping is provided by augmenting a primary B+tree to include in each primary B+tree row a physical row identifier of the corresponding mapping table row. An auxiliary structure created on a primary B+tree can make use of the proposed mapping mechanism. Specifically, the auxiliary structures refers to primary B+tree rows indirectly by storing the physical row identifier of the corresponding mapping table row.

Growing community of inventors

Concord, MA, United States of America

Eugene Inseok Chong