Abstract
This paper presents a novel approach of the flexible query and ranking (FQR) by relaxing the original query in order to provide approximate answers to the user. FQR extends the categorical query criteria with the most similar values by estimating the similarity of different pairs of values in the query workload. Also FQR expands the numerical query criteria range to the nearby values by using the kernel density estimation technology. FQR speculates the importance of each specified attribute based on the user query and assigns the score of each attribute value according to its “desirableness” to the user. The tuples satisfying the relaxed query are finally ranked according to their satisfaction degree.
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Meng, X., Ma, Z.M., Yan, L. (2008). Providing Flexible Queries over Web Databases. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85565-1_74
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DOI: https://doi.org/10.1007/978-3-540-85565-1_74
Publisher Name: Springer, Berlin, Heidelberg
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