Oracle International Corporation

Samuel DeFazio

Jagannathan Srinivasan

Ravi Murthy

Dieter Gawlick

Souripriya Das

Jayanta Banerjee

Anil Nori

Melliyal Annamalai

Chin Hong

Rajiv Chopra

Pankaj Tandon

Chuck Freiwald

Jagannathan Srinivsan

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

A method and system are provided for handling within a database system data items that are associated with data types that are not supported by the database language used to the database system. The data types are registered with the database system by storing, in the database, metadata that indicates the structure, physical layout, and constraints that apply to the data types. The database system uses the structure and physical layout information to access individual elements of data items. The database system validates data items using the constraint information, which includes constraints that dictate what values are valid for certain elements based on what values are supplied for other elements. Data items associated with many different data types may be stored in the same table. Applications can access the data items through a transformation view that transforms the data items from their native format to a format expected by the applications.

Database having an integrated transformation engine using pickling and unpickling of data

A method and apparatus for processing a query in a database system using index types that are not built into the database system are disclosed. Routines for managing an index structure that is not supported by a database system are generated. Data that identifies the routines are submitted to the database system, thereby 'registering' the index types with the database system. In response to statements issued to the database system by a client, the database system calls the routines, causing the routines to create an index structure using data from a data container in the database, and to generate data that indicates which data in the data container satisfies a query issued by the client. The routines of the registered index type extend the indexing capabilities of the database systems and one or more such index types can be registered with the database system. The index structure managed by the routines may be maintained within segments of the database, and the segments may be accessed as index-only tables. Storing a row of data in a database using index-only tables involves storing in a leaf node an index entry that includes a key value along with all other values in the row of data. If the row of data exceeds a predetermined size, then a portion of the row of data is stored in an overflow area. Retrieving a row of data from an index-only table for a user-supplied key involves identifying a leaf node for the key, and reading a row of data from the index entry and any remaining portion from the overflow area when the row exceeds the predetermined size.

Method and system for processing queries in a database system using

A method and apparatus for building, maintaining, and using a multi-level index is provided. The multi-level index is accessed using a key. The key is divided into multiple portions referred to as sub-keys. The first level of the multi-level index is built on a first-level sub-key. Each index entry at the first-level is for a particular first-level sub-key value, and either includes sub-entries associated with second-level sub-key values or a reference to a second-level data retrieval structure. All second-level data retrieval structures are built on the portion of the key that has been designated as the second-level sub-key. As the vocabulary of the first-level sub-key becomes exhausted, fewer maintenance operations will have to be performed to maintain the first-level data retrieval structure. This decreases the overhead and increases the concurrency in a database system that uses the multiple-level index. The multi-level index structure is especially suited for queries that retrieve all values for a given first-level sub-key. The structure also has reduced storage costs compared to a single-level index structure, since first-level sub-key values are stored only once for each nested group.

Index-only tables with nested group keys

Growing community of inventors

Hollis, NH, United States of America

Samuel DeFazio