Querying and integrating sources of structured data from the Web in most cases requires similarity-based concepts to deal with data level conflicts. This is due to the often erroneous and imprecise nature of the data and diverging conventions for their representation. On the other hand, Web databases offer only limited interfaces and almost no support for similarity queries. The approach pre- sented in this paper maps string similarity predicates to standard predicates like substring and keyword search as offered by many of the mentioned systems. To minimize the local processing costs and the required network traffic, the map- ping uses materialized information on the selectivity of string samples such as q-samples, substrings, and keywords. Based on the predicate mapping similar- ity selections and joins are described and the quality and required effort of the operations is evaluated experimentally.
['Eike Schallehn', 'Ingolf Geist', 'Kai-Uwe Sattler']
Supporting Similarity Operations Based on Approximate String Matching on the Web