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 incorporates a collection of papers provided on the fifteenth foreign convention on purposes of Declarative Programming and data M- agement, INAP 2004, and the 18th Workshop on common sense 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 advanced difficulties. This speci?cation procedure has turn into a growing number of appealing in recent times, for instance, within the domain names of databases, for the processing of normal language, for the modeling and processing of combinatorial difficulties, and for setting up knowledge-based structures for the net. 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 complicated software program. They comprehensively disguise the effect of programmable common sense solvers within the web society, its underlying te- nologies, and modern purposes in undefined, trade, executive, and social providers. The Workshops on common sense Programming are the once a year assembly of the Society for good judgment Programming (GLP e.V.). they create jointly researchers attracted to common sense programming, constraint programming, and similar parts like databases and arti?cial intelligence. prior workshops were held in Germany, A- tria, and Switzerland. Thetopicsoftheselectedpapersofthisyear’sjointconferenceconcentrateon 3 at present vital ?elds: wisdom administration and choice help, constraint programming and constraint fixing, and declarative programming and Web-based systems.

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References 1. R. Agarwal, C. Aggarwal, and V. Prasad. A tree projection algorithm for generation of frequent itemsets. Journal of Parallel and Distributed Computing(Special Issue on High Performance Data Mining), 2000. 2. R. Agrawal and K. Shim. Developing tightly-coupled data mining application on a relational database system. of the 2nd Int. Conf. on Knowledge Discovery in Database and Data Mining, Portland,Oregon, 1996. 3. R. Agrawal, R. Srikant. Fast algorithms for mining association rules.

All these difficulties make reading and translating Japanese sentences a cumbersome and tedious process. If the reader reaches an inscrutable text passage, Incremental Learning of Transfer Rules for Customized Machine Translation 49 he must first guess where an unknown word starts and then consult a dictionary. To look up the word in a bilingual dictionary is quite straightforward as long as the reader is sure about the correct pronunciation, otherwise he has to consult a kanji dictionary, which lists kanji and their readings categorized by 214 basic elements or radicals.

There remain lots of further investigations. We plan to implement our SQL based frequent pattern mining approach on parallel RDBMS, and to check how efficiently our approach can be parallelized and speeded up using parallel database system. Additionally, we will investigate an SQL based algorithm which combine Apriori and F P -growth to scale both small and large data sets. References 1. R. Agarwal, C. Aggarwal, and V. Prasad. A tree projection algorithm for generation of frequent itemsets. Journal of Parallel and Distributed Computing(Special Issue on High Performance Data Mining), 2000.

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