INTRODUCTION
IN the last decade, multimedia databases have been used in many application fields. The Internet boom has increased this trend, introducing many new interesting issues related to the storage and management of distributed multimedia data. For these reasons, data models and database management systems (DBMSs) have been extended in order to enable the modeling and management of complex data types, including multimedia data. In particular, other than working on the extension of data models, the research community has focused on indexing techniques enabling content-based retrieval of multimedia information, query paradigms and languages, clustering techniques, and support for distributed multimedia information management.
Examples of DBMSs extended with functionalities to support multimedia data management (MMDBMSs) include CORE , OVID , VODAK , QBIC ,ATLAS , MIRROR , DISIMA, and so forth, each providing enhanced support for one or more media domains among text, sound, image, and video. In the beginning, many DBMS producers relied on the objectoriented data model to face the complexity of modeling multimedia data, but there have also been examples of MMDBMSs based on the relational data model and on specific nonstandard data models. However, in order to facilitate the diffusion of multimedia databases within industrial environments, researchers have been seeking solutions based on the relational data model, possibly associated to some standard design paradigms, like those used with traditional relational DBMSs (RDBMSs).
Extensible RDBMSs have been an attempt in this direction. In the last decade, DBMS vendors have produced extended versions of RDBMSs , with added capabilities to manage complex data types, including multimedia. In particular, these new products extend traditional RDBMSs with mechanisms for implementing the concept of object/relational universal server. In other words, they provide a means to enable the construction of user-defined Data Types (UDTs), and user-defined Functions (UDFs) for manipulating them. New standards for SQL have been created, and SQL3 has become the standard for RDBMSs extended with object-oriented capabilities .
The standard includes UDTs, UDFs, large objects (LOBs; a variant of binary large objects (BLOBs)), and type checking on UDTs, which are accessed through SQL statements. Early examples of extensible RDBMSs include Postgres, IBM/DB2 version 5 , Informix , and ORACLE 8 .As MMDBMSs technology has started becoming more mature, the research community has started focusing on multimedia software engineering issues, with particular emphasis on multimedia databases. In particular, main efforts have been devoted to multimedia data indexing and content-based retrieval, which has led to the development of many data indexing and organization approaches, each specialized on a particular media type, all aiming at guaranteeing an efficient retrieval of multimedia data based on their contents. Thus, we have had many indexing techniques for images and videos: some are based on physical characteristics of media types, and others based on their semantics.
However, in spite of these efforts, little attention has been devoted to multimedia databases and multimedia software engineering methodologies in the direction of providing paradigms for designing information systems capable of processing many different types of multimedia data together with traditional alphanumeric data. In particular, multimedia software engineering methodologies should embed not only data indexing issues but also techniques for database schema design, with guidelines on evaluating their quality and on refactoring them. In this project, we present a generic normalization framework for multimedia databases, providing guidelines and normal forms to evaluate and improve the quality of schemas. The framework applies in a seamless way to images and to other media types.
It is based on a new definition of imprecise dependency for multimedia data, named type-M dependency, which is parameterized upon the distance functions used for comparing multimedia data , and it has been exploited to define five new normal forms. The concept of type-M dependency generalizes similar concepts of imprecise or fuzzy functional dependencies (ffds) existing in the literature, which turned out to be inadequate to capture some important aspects of multimedia data.
IN the last decade, multimedia databases have been used in many application fields. The Internet boom has increased this trend, introducing many new interesting issues related to the storage and management of distributed multimedia data. For these reasons, data models and database management systems (DBMSs) have been extended in order to enable the modeling and management of complex data types, including multimedia data. In particular, other than working on the extension of data models, the research community has focused on indexing techniques enabling content-based retrieval of multimedia information, query paradigms and languages, clustering techniques, and support for distributed multimedia information management.
Examples of DBMSs extended with functionalities to support multimedia data management (MMDBMSs) include CORE , OVID , VODAK , QBIC ,ATLAS , MIRROR , DISIMA, and so forth, each providing enhanced support for one or more media domains among text, sound, image, and video. In the beginning, many DBMS producers relied on the objectoriented data model to face the complexity of modeling multimedia data, but there have also been examples of MMDBMSs based on the relational data model and on specific nonstandard data models. However, in order to facilitate the diffusion of multimedia databases within industrial environments, researchers have been seeking solutions based on the relational data model, possibly associated to some standard design paradigms, like those used with traditional relational DBMSs (RDBMSs).
Extensible RDBMSs have been an attempt in this direction. In the last decade, DBMS vendors have produced extended versions of RDBMSs , with added capabilities to manage complex data types, including multimedia. In particular, these new products extend traditional RDBMSs with mechanisms for implementing the concept of object/relational universal server. In other words, they provide a means to enable the construction of user-defined Data Types (UDTs), and user-defined Functions (UDFs) for manipulating them. New standards for SQL have been created, and SQL3 has become the standard for RDBMSs extended with object-oriented capabilities .
The standard includes UDTs, UDFs, large objects (LOBs; a variant of binary large objects (BLOBs)), and type checking on UDTs, which are accessed through SQL statements. Early examples of extensible RDBMSs include Postgres, IBM/DB2 version 5 , Informix , and ORACLE 8 .As MMDBMSs technology has started becoming more mature, the research community has started focusing on multimedia software engineering issues, with particular emphasis on multimedia databases. In particular, main efforts have been devoted to multimedia data indexing and content-based retrieval, which has led to the development of many data indexing and organization approaches, each specialized on a particular media type, all aiming at guaranteeing an efficient retrieval of multimedia data based on their contents. Thus, we have had many indexing techniques for images and videos: some are based on physical characteristics of media types, and others based on their semantics.
However, in spite of these efforts, little attention has been devoted to multimedia databases and multimedia software engineering methodologies in the direction of providing paradigms for designing information systems capable of processing many different types of multimedia data together with traditional alphanumeric data. In particular, multimedia software engineering methodologies should embed not only data indexing issues but also techniques for database schema design, with guidelines on evaluating their quality and on refactoring them. In this project, we present a generic normalization framework for multimedia databases, providing guidelines and normal forms to evaluate and improve the quality of schemas. The framework applies in a seamless way to images and to other media types.
It is based on a new definition of imprecise dependency for multimedia data, named type-M dependency, which is parameterized upon the distance functions used for comparing multimedia data , and it has been exploited to define five new normal forms. The concept of type-M dependency generalizes similar concepts of imprecise or fuzzy functional dependencies (ffds) existing in the literature, which turned out to be inadequate to capture some important aspects of multimedia data.
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