Applications of Declarative Programming and Knowledge by Dietmar Seipel, Michael Hanus, Ulrich Geske, Oskar

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

This quantity encompasses a collection of papers provided on the fifteenth foreign convention on functions of Declarative Programming and information M- agement, INAP 2004, and the 18th Workshop on good judgment Programming, WLP 2004, which 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 develop into a growing number of appealing lately, for instance, within the domain names of databases, for the processing of usual language, for the modeling and processing of combinatorial difficulties, and for developing knowledge-based platforms for the internet. The INAP meetings offer a discussion board for in depth discussions of appli- tions of vital applied sciences round common sense programming, constraint challenge fixing, and heavily comparable complex software program. They comprehensively hide the impression of programmable common sense solvers within the net society, its underlying te- nologies, and modern functions in undefined, trade, executive, and social prone. The Workshops on common sense Programming are the yearly assembly of the Society for good judgment Programming (GLP e.V.). they carry jointly researchers drawn to good judgment programming, constraint programming, and similar parts like databases and arti?cial intelligence. past workshops were held in Germany, A- tria, and Switzerland. Thetopicsoftheselectedpapersofthisyear’sjointconferenceconcentrateon 3 presently very important ?elds: wisdom administration and determination help, constraint programming and constraint fixing, and declarative programming and Web-based structures.

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2] presents a methodology for SQL Based Frequent Pattern Mining with FP-Growth 45 tightly-coupled integration of data mining applications with a relational database system. [9] has tried to highlight the implications of various architecture alternatives for coupling data mining with relational database systems. They have also compared the performance of the SQL-92 architecture with SQL-OR based architecture, and they are on the base of Apriori-like approach. 6 Summary and Conclusion We have implemented SQL based frequent pattern mining using F P -growthlike approach.

Transfer the transaction table T into table T , in which infrequent items are excluded. The size of the transaction table is a major factor in the cost of joins involving T . It can be reduced by pruning the non-frequent items from the transactions after the first pass. We insert the pruned transactions into table T which has the same schema as that of T . In the subsequent passes, join with T can be replaces by join with T . This could result in improved performance especially for datasets which contains lot of non-frequent items.

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.

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