Since real world databases are known to be very large, they raise problems of the access. Therefore, real world databases onlycan be accessed by database management systems and the number of accesses has to be reduced to a minimum. Considering this property, we are forced to use standard set--oriented interfaces of relational database management systems. We present a system for discovering data dependencies, which is build upon a set--oriented interface. The point of main effort has been put on the discovery of domain restrictions, unary inclusionand functional dependencies in relational databases. The system also embodies an inference relation to minimize database access. 1 Introduction Data dependencies are the most common type of semantic constraints in relational databases which determine the database design. Despite the advent of highly automated tools, database design still consists basically of two types of activities: first, reasoning about data types and data dependencies and...

For semantic query optimization one needs detailed knowledge about the contents of the database. Traditional techniques use static knowledge about all possible states of the database which is already given. New techniques use knowledge only about the current state of the database which can be found by methods of knowledge discovery in databases. Databases are often very large and permanently in use. Therefore, methods of knowledge discovery are only allowed to take a small amount of the capacity of the database system. So database access has to be reduced to a minimum during the process of discovery and obviously if the database changes during the process of maintenance of the discovered knowledge. In this paper, the main effort has been put into minimizing the number of database accesses, w.r.t. discovery and maintenance. This is exemplified by the discovery of functional dependencies. We improve the inference of functional dependencies by using independencies and cardinality dependen...

The aim of query optimization is to produce an equivalent query which is less expensive to process than the original query. Semantic query optimization involves the use of semantic knowledge during the optimization process. The success strongly depends on the availability of this knowledge. We present a rule--based approach of semantic query optimization which overcomes the limitation of predefined knowledge by discovering constraints. We show that unnecessary distinct--keywords and group by--parts of an SQL query can be detected and removed with the use of discovered constraints. 1 Introduction Semantic query optimization (SQO) provides the same kind of transparency with respect to semantic knowledge like relational optimizers do with respect to physical representation. Since semantically equivalent queries can differ significantly in their evaluation costs, it is our major goal to free the user from finding the most effective query. Finding semantically equivalent queries requires s...

Since real world databases are known to be very large, they raise problems of the access. Therefore, real world databases onlycan be accessed by database management systems and the number of accesses has to be reduced to a minimum. Considering this property, we are forced to use standard set–oriented interfaces of relational database management systems. We present a system for discovering data dependencies, which is build upon a set–oriented interface. The point of main effort has been put on the discovery of domain restrictions,unary inclusionand functional dependencies in relational databases. The system also embodies an inference relation to minimize database access. 1

Knowledge discovery in databases is not only the nontrivial extraction of implicit, previously unknown and potentially useful information from databases. We argue that in contrast to machine learning, knowledge discovery in databases should be applied to real world databases. Since real world databases are known to be very large, they raise problems of the access. Therefore, real world databases only can be accessed by database management systems and the number of accesses has to be reduced to a minimum. Considering this property, we are forced to use, for example, standard set oriented interfaces of relational database management systems in order to apply methods of knowledge discovery in databases. We present a system for discovering data dependencies, which is build upon a set oriented interface. The point of main effort has been put on the discovery of value restrictions, unary inclusion- and functional dependencies in relational databases. The system also embodies an inference rel...