document: Answering approximate queries over autonomous web databases To deal with the problem of empty or too little answers returned from a Web database in response to a user query , this paper proposes a novel approach to provide relevant and ranked query results . Based on the user original query , we speculate how much the user cares about each specified attribute and assign a corresponding weight to it . This original query is then rewritten as an approximate query by relaxing the query criteria range . The relaxation order of all specified attributes and the relaxed degree on each specified attribute are varied with the attribute weights . For the approximate query results , we generate users ' contextual preferences from database workload and use them to create a priori orders of tuples in an off-line preprocessing step . Only a few representative orders are saved , each corresponding to a set of contexts . Then , these orders and associated contexts are used at query time to expeditiously provide ranked answers . Results of a preliminary user study demonstrate that our query relaxation and results ranking methods can capture the user 's preferences effectively . The efficiency and effectiveness of our approach is also demonstrated by experimental result .
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