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...

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...

The implication problem is the problem of deciding whether a given set of dependencies entails other dependencies. Up to now, the entailment of excluded dependencies or independencies is only regarded on a metalogical level, which is not suitable for an automatic inference process. But, the inference of independencies is of great importance for new topics in database research like knowledge discovery in databases. In this paper, the expanded implication problem is discussed in order to decide entailment of dependencies and independencies. The main results are axiomatizations of functional, inclusion and multivalued independencies and the corresponding inference relations. Also, we discuss the use of independencies in knowledge discovery in databases and semantic query optimization. 1 1 Introduction The implication problem in relational database theory can be defined as a test: Given a set of dependencies \Sigma and a dependency oe, find whether \Sigma entails oe, or oe 2 Cn(\Sigma)....