document: Text joins in an RDBMS for web data integration The integration of data produced and collected across autonomous , heterogeneous web services is an increasingly important and challenging problem . Due to the lack of global identifiers , the same entity ( e.g. , a product ) might have different textual representations across databases . Textual data is also often noisy because of transcription errors , incomplete information , and lack of standard formats . A fundamental task during data integration is matching of strings that refer to the same entity . In this paper , we adopt the widely used and established cosine similarity metric from the information retrieval field in order to identify potential string matches across web sources . We then use this similarity metric to characterize this key aspect of data 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 a sampling-based join approximation strategy for execution in a standard , unmodified relational database management system ( RDBMS ) , since more and more web sites are powered by RDBMSs with a web-based front end . We implement the join inside an RDBMS , using SQL queries , for scalability and robustness reasons . Finally , we present a detailed performance evaluation of an implementation of our algorithm within a commercial RDBMS , using real-life data sets . Our experimental results demonstrate the efficiency and accuracy of our techniques .
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