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By Dietmar Seipel, Michael Hanus, Ulrich Geske, Oskar Bartenstein

This quantity features a choice of papers provided on the fifteenth foreign convention on functions of Declarative Programming and data M- agement, INAP 2004, and the 18th Workshop on good judgment Programming, WLP 2004, that have been held together in Potsdam, Germany, from March 4th to sixth, 2004. Declarative programming is a sophisticated paradigm for the modeling and fixing of complicated difficulties. This speci?cation process has turn into a growing number of appealing lately, for instance, within the domain names of databases, for the processing of average language, for the modeling and processing of combinatorial difficulties, and for constructing knowledge-based structures for the net. The INAP meetings supply a discussion board for extensive discussions of appli- tions of significant applied sciences round good judgment programming, constraint challenge fixing, and heavily similar complex software program. They comprehensively conceal the influence of programmable good judgment solvers within the web society, its underlying te- nologies, and modern functions in undefined, trade, executive, and social prone. The Workshops on good judgment Programming are the once a year assembly of the Society for common sense Programming (GLP e.V.). they carry jointly researchers drawn to good judgment programming, constraint programming, and comparable components like databases and arti?cial intelligence. prior workshops were held in Germany, A- tria, and Switzerland. Thetopicsoftheselectedpapersofthisyear’sjointconferenceconcentrateon 3 presently very important ?elds: wisdom administration and choice aid, constraint programming and constraint fixing, and declarative programming and Web-based systems.

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However using RDBMSs provides us the benefits of using their buffer management systems specifically developed for freeing the user applications from the size considerations of the data. And moreover, there are several potential advantages of building mining algorithms to work on RDBMSs as described in Introduction. An interesting alternative is to store an F P -tree in a relational table and to propose algorithms for frequent patterns mining based on such a table. We studied two approaches in this category - F P , EF P (Extended Frequent Pattern).

Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In Proc. of the 21st VLDB Conference, 1995. 12. S. Sarawagi, S. Thomas, and R. Agrawal. Integrating mining with relational database systems: alternatives and implications. In Proc. of the ACM SIGMOD Conference on Management of data, Seattle,Washinton,USA, 1998. 13. K. Sattel and O. Dunemann. SQL database primitives for decision tree classifiers. In Proc. Of the 10nd ACM CIKN Int. Conf. on Information and Knowledge Management, Atlanta,Georgia, 2001.

Declaratively Querying and Visualizing Knowledge Bases in XML 23 Fig. 3. 2 Derivation Tree of a Diagnosis The visualization of a derivation tree for a specified diagnosis can be helpful during the validation task. : a given diagnosis is not derived as a possible solution) is simplified by depicting its derivation graph. The following declaration generates the derivation tree for the diagnosis P181: knowledge_base_to_rule_edges(KB, Edges) :findall( Edge, knowledge_base_to_rule_edge(KB, Edge), Edges_2 ), reaching_edges(’P181’, Edges_2, Edges).

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