Data mining from traditional relational databases as well as from non-traditional ones such as semi-structured data, Web data, and scientific databases housing biological, linguistic, and sensor data has recently become a popular way of discovering hidden knowledge.

Database Languages and Query Execution.- Inductive Databases and Multiple Uses of Frequent Itemsets: The cInQ Approach.- Query Languages Supporting Descriptive Rule Mining: A Comparative Study.- Declarative Data Mining Using SQL3.- Towards a Logic Query Language for Data Mining.- A Data Mining Query Language for Knowledge Discovery in a Geographical Information System.- Towards Query Evaluation in Inductive Databases Using Version Spaces.- The GUHA Method, Data Preprocessing and Mining.- Constraint Based Mining of First Order Sequences in SeqLog.- Support for KDD-Process.- Interactivity, Scalability and Resource Control for Efficient KDD Support in DBMS.- Frequent Itemset Discovery with SQL Using Universal Quantification.- Deducing Bounds on the Support of Itemsets.- Model-Independent Bounding of the Supports of Boolean Formulae in Binary Data.- Condensed Representations for Sets of Mining Queries.- One-Sided Instance-Based Boundary Sets.- Domain Structures in Filtering Irrelevant Frequent Patterns.- Integrity Constraints over Association Rules.