Knowledge Discovery for Business Information Systems Information Filters Supplying Data Warehouses with Benchmarking Information Parallel Mining of Association Rules Unsupervised Feature Ranking and Selection Approaches to Concept Based Exploration of Information Resources Hybrid Methodology of Knowledge Discovery for Business Information Fuzzy Linguistic Summaries of Databases for an Efficient Business Data Analysis and Decision Support Integrating Data Sources Using a Standardized Global Dictionary Maintenance of Discovered Association Rules Multidimensional Business Process Analysis with the Process Warehouse Amalgamation of Statistics and Data Mining Techniques: Explorations in Customer Lifetime Value Modeling Robust Business Intelligence Solutions The Role of Granular Information in Knowledge Discovery in Databases Dealing with Dimensions in Data Warehousing Enhancing the KDD Process in the Relational Database Mining Framework by Quantitative Evaluation of Association Rules Speeding up Hypothesis Development Sequence Mining in Dynamic and Interactive Environments Investigation of Artificial Neural Networks for Classifying Levels of Financial Distress of Firms: The Case of an Unbalanced Training Sample. Current database technology and computer hardware allow us to gather store access and manipulate massive volumes of raw data in an efficient and inexpensive manner. In addition the amount of data collected and warehoused in all industries is growing every year at a phenomenal rate. Nevertheless our ability to discover critical non obvious nuggets of useful information in data that could influence or help in the decision making process is still limited. Knowledge discovery (KDD) and Data Mining (DM) is a new multidisciplinary field that focuses on the overall process of information discovery from large volumes of data. The field combines database concepts and theory machine learning pattern recognition statistics artificial intelligence uncertainty management and high performance computing. To remain competitive businesses must apply data mining techniques such as classification prediction and clustering using tools such as neural networks fuzzy logic and decision trees to facilitate making strategic decisions on a daily basis. Knowledge Discovery for Business Information Systems contains a collection of 16 high quality articles written by experts in the KDD and DM field from the following countries: Austria Australia Bulgaria Canada China (Hong Kong) Estonia Denmark Germany Italy Poland Singapore and USA.