This semantics is shown to be equivalent to a semantics based on comparing the relative importance of sets of models, by what amounts to a qualitative probability measure. The consequence operations defined by those equivalent semantics are then characterized by a weakening of Tarski's properties in which the monotonicity requirement is replaced by three weaker conditions. Classical prepositional connectives are characterized by natural introduction-elimination rules in the nonmonotonic setting. The success of computerized data management has resulted in the accumulation of huge amounts of data in several organizations. The recent emergence of Data Mining, or Knowledge Discovery in Databases, is a testimony to this trend.
Data mining involves the development of tools that can extract patterns from large databases.
Many serial algorithms were presented, but applications, which use these algorithms are still limited by the capabilities of a single computer, namely - only one CPU and limited amount of memory. Considering the huge amounts of data to process, running on one computer can take incredibly large amount of computation time. All these limits lead us to the ideal of using more powerful parallel systems, which of course require parallel algorithms . Efficient parallel algorithms were developed. In this work we have studied performance of the Count Distribution algorithm on a scalable cluster of heterogeneous computers.