By Dietmar Seipel, Michael Hanus, Ulrich Geske, Oskar Bartenstein
This e-book constitutes the completely refereed joint post-proceedings of the fifteenth overseas convention on purposes 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 geared up in topical sections on wisdom administration and determination aid, constraint programming and constraint fixing, and declarative programming and Web-based systems.
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Additional info for Applications of Declarative Programming and Knowledge Management: 15th International Conference on Applications of Declarative Programming and Knowledge
8. M. Hopfner, D. Seipel, J. Wolff von Gudenberg: Comprehending and Visualising Software based on XML Representations and Call Graphs, Proc. 11th IEEE International Workshop on Program Comprehension IWPC 2003. 9. R. Holt, A. Winter, A. Schürr: GXL: Towards a Standard Exchange Format, Proc. de/GXL/ 10. M. Hüttig, G. Buscher, T. Menzel, W. Scheppach, F. –P. Buscher: A Diagnostic Expert System for Structured Reports, Quality Assessment, and Training of Residents in Sonography, Medizinische Klinik, 2004.
That is why EF P can get signiﬁcant performance improvement. We compare the four approaches on data sets T25I10D10K and T25I20D100K: K-way join approach, loose-couple approach, EF P approach, and P ath approach using a user deﬁned table functions (Path). Figure 8 (c)(d) shows the results of experiments. From the graph we can make the following observation: EF P and P ath approach can get better performance than K-way join on large data sets or long patterns. The main reason is that generating candidate-k table Ck is time-consuming procedure when T is large or minimum support threshold is quite low.
Path == ik . path curcnt = ik . count + 1; update the table F P ; else insert ik into the table F P ; curpath += ik ; Fig. 3. Algorithm for constructing table F P 38 X. -U. Sattler, and I. Geist 2. Construct the table F P . Frequent items in T are sorted in descending order by frequency. ) Each frequent item is tested as follows. – If the item does not have the same item and path as those in the F P , insert it into the F P as a new item with the count being 1. – Otherwise, update the F P by increasing the count by 1.
Applications of Declarative Programming and Knowledge Management: 15th International Conference on Applications of Declarative Programming and Knowledge by Dietmar Seipel, Michael Hanus, Ulrich Geske, Oskar Bartenstein