Optical Character Reader (OCR) incorrect recognition is a serious problem when searching for OCR-scanned documents in databases such as digital libraries. In order to reduce costs, this paper proposes fuzzy retrieval methods for English text containing errors in the recognized text without correcting the errors manually. The proposed methods generate multiple search terms for each input query term based on probabilistic automata which reflect both error-occurrence probabilities and character-connection probabilities. Experimental results of test-set retrieval indicate that one of the proposed methods improves the recall rate from 95.96% to 98.15% at the cost of a decrease in precision from 100.00% to 96.01% with 20 expanded search terms.

Decentralized XML databases are often used in Electronic Commerce (EC) business models such as e-brokers on the Web. To flexibly model such applications, we need a modeling language for EC business processes. To this end, we have adopted a query language approach and have designed a query language, called XBML, for decentralized XML databases used in EC businesses. In this paper, we explain and validate the functionality of XBML by specifying e-broker business models and describe the implementation of the XBML server, focusing on the distributed query processing.