Oracle International Corporation

Melliyal Annamalai

Souripriya Das

Zhe Wu

Eugene Inseok Chong

Jagannathan Srinivasan

Jogannathan Srinivasan

George Eadon

Vladimir Kolovski

Samuel DeFazio

Rajiv Chopra

Jagannathan Srinivsan

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

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

A system creates and uses a visual image index for visual image retrieval. The visual image index uses multi-level filtering to reduce the processing and storage needed to identify visual images in a table that have similar characteristics to those of a query image. Information about a visual image is stored in an image signature that contains data about basic visual primitives of the image (e.g., global color, local color, texture and structure). The signature is stored in a database table along with the image itself (or a link to the image). Visual image retrieval is performed by searching for signatures in the database that are similar to the signature of the query image. The multi-level filtering reduces the number of candidate images, before a full comparison is done on the filtered-set of candidate image signatures. The first sub-filter performs a range-based query on a table of image attributes culled from the image signature, each attribute in this table is indexed. The results of the range-based sub-filter are processed by a functional predicate sub-filter which determines a distance measure of each resultant image from the query image. The results of the functional predicate sub-filter are compared, using full signature comparison logic, to the query image signature to determine whether the query image is similar to a visual image in the table.

System for efficient content-based retrieval of images

Growing community of inventors

Nashua, NH, United States of America

Melliyal Annamalai