Indexing video data is essential for providing content-based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues, even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence, new solutions are significant. This paper develops a data model and a rule-based query language for video content-based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, and (2) video frames which contain these entities.

Indexing video data is essential for providing content based access. We consider how database technology can offer an integrated framework for modeling and querying video data. We develop a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (i.e., symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: the entities (i.e., objects) of interest in the domain of a video sequence; video frames which contain these entities. To represent this information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics.

The paper describes a method to cope with denormalized relational schemas in a database reverse engineering process. We propose two main steps to improve the understanding of data semantics. Firstly we extract inclusion dependencies by analyzing the equi join queries embedded in application programs and by querying the database extension. Secondly we show how to discover only functional dependencies which influence the way attributes should be restructured. The method is interactive since an expert user has to validate the presumptions on the elicited dependencies. Moreover, a restructuring phase leads to a relational schema in third normal form provided with key constraints and referential integrity constraints. Finally, we sketch how an entity relationship schema can be derived from such information.

This paper introduces a method of reverse engineering for operational relational databases. The conceptual schemas are derived using information extracted from data dictionaries, database extensions and application programs. Its main strength relies on the assumptions made on the a priori knowledge available about the database (only [Formula: see text] and/or [Formula: see text] constraints on attribute(s)) as well as the user competence. We argue that most of the knowledge needed to build a conceptual schema, if not described in the Data Description Language, is embedded in application programs under various forms. The method is therefore based on four main steps: firstly, application program analysis is performed and a set [Formula: see text] of equi-joins is obtained; secondly, a conceptual schema is derived from [Formula: see text], from the database extension and from the relational schema; thirdly, this conceptual schema is validated through an interactive dialogue with the expert user who is helped in this task by indications given by the method. Finally, a schema reorganization under user control is achieved to match the user requirements better. We introduce also how other kinds of queries can help the task of semantics discovery. Additionally, we precisely identify the phases when user interaction is needed. This method has been successfully validated on an operational database.