Constraint Handling Rules
Constraint solving and programming is proving useful in many areas of Artificial Intelligence. Experience from applications has shown that typically, we are confronted with a heterogeneous mix of different types of constraints. To be able to express constraints as they appear in the application and to write and combine arbitrary constraint systems, we are developing a special purpose language for writing constraint systems called Constraint Handling Rules (CHR). Several CHR libraries exist in languages such as Prolog, LISP and Java, worldwide more than 30 projects use CHR.
In this talk, we will give an overview of CHR by examples, give syntax and semantics for the language and present some results on the analysis of CHR programs related to confluence and opertional equivalence. CHR and dozens of its constraint solvers can be used online via the internet at http://www.pms.informatik.uni-muenchen.de/~webchr/
Thom Fruehwirth is an assistant professor at Ludwig-Maximilians-University, Munich, Germany, working in constraint programming. In the same area, he was researcher at the European Computer-Industry Research Centre (ECRC) in Munich from 1991 to 1996. Before that he was for 5 years assistant at the Technical University of Vienna, where he graduated. He is the main author of the first German textbook on constraint programming.
Knowledge Discovery in Databases
Under the name of Knowledge Discovery in Databases (KDD), or simply Data Mining, researchers are investigating all aspects of the computer-based intelligent analysis of large sets of stored data. With increasingly widespread use of such techniques in the context of data warehouses in business and administration, the area has also become a commercial success, being pursued by a large number of big and small companies. In this tutorial, we will give an introduction into this rapidly developing field. We will define the central research questions that characterize the area, and identify its close links to parent discplines such as statistics, machine learning/AI, and databases. We will present and overview of the techniques that are being used in the field, and describe some of them in more detail. In doing so, we will primarily focus on recent approaches that have turned out successful in applications, from market basket analysis to text mining.