an organization &# 39 ; s data records are often noisy : because of transcription errors , incomplete information , and lack of standard formats for textual data . a fundamental task during data cleansing and integration is matching strings — perhaps across multiple relations — that refer to the same entity . furthermore , it is desirable to perform this matching within an rdbms , which is where the data is likely to reside . in this paper , we adapt the widely used and established cosine similarity metric from the information retrieval field to the relational database context in order to identify potential string matches across relations . we then use this similarity metric to characterize this key aspect of data cleansing and integration as a join between relations on textual attributes , where the similarity of matches exceeds a specified threshold . computing an exact answer to the text join can be expensive . for query processing efficiency , we propose an approximate , sampling - based approach to the join problem that can be easily and efficiently executed in a standard , unmodified rdbms . therefore the present invention includes a system for string matching across multiple relations in a relational database management system comprising generating a set of strings from a set of characters , decomposing each string into a subset of tokens , establishing at least two relations within the strings , establishing a similarity threshold for the relations , sampling the at least two relations , correlating the relations for the similarity threshold and returning all of the tokens which meet the criteria of the similarity threshold .