Surrogate Ranking for Very Expensive Similarity Queries

1 We consider the problem of similarity search in applications where the cost of computing the similarity between two records is very expensive, and the similarity measure is not a metric. In such applications, comparing even a tiny fraction of the database records to a single query record can be orders of magnitude slower than reading the entire database from disk, and indexing is often not possible. We develop a general-purpose, statistical framework for answering top-k queries in such databases, when the database administrator is able to supply an inexpensive surrogate ranking function that substitutes for the actual similarity measure. We develop a robust method that learns the relationship between the surrogate function and the similarity measure. Given a query, we use Bayesian statistics to update the model by taking into account the observed partial results. Using the updated model, we construct bounds on the accuracy of the result set obtained via the surrogate ranking. Our exp...

• Type: Conference
• Year: 2010
• Where: ICDE
• Authors: Chris Jermaine, Fei Xu, Mingxi Wu, Ravi Jampani, Tamer Kahveci
• Keywords: Actual Similarity Measure, Database, ICDE 2010, Similarity Measure, Surrogate Ranking Function