By Dietmar Seipel, Michael Hanus, Ulrich Geske, Oskar Bartenstein
This booklet constitutes the completely refereed joint post-proceedings of the fifteenth foreign convention on functions of Declarative Programming and data administration, INAP 2004, and the 18th Workshop on common sense Programming, WLP 2004, held together in Potsdam, Germany in March 2004.
The 18 revised complete papers offered including an invited educational lecture and an invited paper have been chosen in the course of rounds of reviewing and development. The papers are equipped in topical sections on wisdom administration and choice help, constraint programming and constraint fixing, and declarative programming and Web-based systems.
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Extra resources for Applications of Declarative Programming and Knowledge Management: 15th International Conference on Applications of Declarative Programming and Knowledge
C Springer-Verlag Berlin Heidelberg 2005 48 W. Winiwarter Japanese sentence: これは、片面だけに字を書いて、同じ大きさに切りそろえたものを、 何枚も革のひもでとじた。 Roman transcription: Kore wa, katamen dake ni ji o kaite, onaji ookisa ni kirisoraeta mono o, nan mai mo kawa no himo de tojita. Human translation into German: Man hat es nur einseitig beschrieben und mehrere auf gleiche Größe zurechtgeschnittene Blätter mit Lederriemen zusammengebunden. Human translation into English: It was written on only one side, and several sheets, trimmed to the same size, were bound together with leather laces.
2. It is tedious to repeatedly scan the database and check a large set of candidates by pattern matching, which is especially true for mining long patterns. Recently, an F P -tree based frequent pattern mining method , called F P growth, developed by Han et al achieves high eﬃciency, compared with Apriorilike approach. The F P -growth method adopts the divide-and-conquer strategy, D. Seipel et al. ): INAP/WLP 2004, LNAI 3392, pp. 32–46, 2005. c Springer-Verlag Berlin Heidelberg 2005 SQL Based Frequent Pattern Mining with FP-Growth 33 uses only two full I/O scans of the database, and avoids iterative candidate generation.
Interestingly, Fig. 6. Findings in Rule Conditions Table 1. 629 636 417 692 10–19 20–50 198 14 Declaratively Querying and Visualizing Knowledge Bases in XML 27 in our current implemantation only half of the computation time was spend on the calls knowledge_slice_to_conditions(Slice, Conditions), whereas the other half was needed to find out that there exist no further ’KnowledgeSlice’ elements in the knowledge base. We could save this time by automatically completing the path expression to KB^’KnowledgeSlices’^’KnowledgeSlice’ based on the available schema knowledge.