document: A parallelized indexing method for large-scale case-based reasoning Case-based reasoning -LRB- CBR -RRB- is a problem solving methodology commonly seen in artificial intelligence . It can correctly take advantage of the situations and methods in former cases to find out suitable solutions for new problems . CBR must accurately retrieve similar prior cases for getting a good performance . In the past , many researchers proposed useful technologies to handle this problem . However , the performance of retrieving similar cases may be greatly influenced by the number of cases . In this paper , the performance issue of large-scale CBR is discussed and a parallelized indexing architecture is then proposed for efficiently retrieving similar cases in large-scale CBR . Several algorithms for implementing the proposed architecture are also described . Some experiments are made and the results show the efficiency of the proposed method
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