Incorporating Metric Access Methods for Similarity Searching on Oracle Database

The volume of multimedia and complex data (images, videos, audio, time series, DNA sequences, and others) has been growing at a very fast pace. Thus, it is necessary to store in databases many types of data which are not naturally handled by Database Management Systems (DBMSs). Complex data are well-suited to be queried by similarity. Many works addressed techniques for similarity searching, but the majority of them are not conceived to be integrated into a database engine. However, including similarity search into the database core would allow taking advantage of the DBMS resources to perform queries integrating complex and conventional data. Oracle Corp. developed the Oracle interMedia module to support multimedia data in its database manager, providing several operations to manipulate them. It allows performing content-based image retrieval through proprietary functions to extract intrinsic features from images and to compute their similarity. In this paper we describe another module for similarity search, also developed using the Oracle’s Extensible Architecture Framework. Our approach allows including user-defined feature extraction methods and distance functions into the database core, providing a wider flexibility. We also present experiments that show that employing our module to query images by content improves the results obtained using Oracle alone, both in the precision of the results and in the performance of executing queries.