Fast Evaluation Techniques for Complex Similarity Queries
||Fast Evaluation Techniques for Complex Similarity Queries
||K. Böhm, M. Mlivoncic, H.-J. Schek, R. Weber
||27th Int. Conf. on Very Large Databases (VLDB)Roma, Italy
||Institute of Information Systems, ETH Zurich
Complex similarity queries, i.e., multi-feature
multi-object queries, are needed to express the in-formation
need of a user against a large multi-media
repository. Even if a user initially issues
a single-object query over one feature, a system
with relevance feedback will automatically gener-ate
a complex similarity query. Relevance feed-back
is only useful if response times are inter-active.
Therefore, this article contributes to the
important problem how to evaluate such complex
queries efficiently. We describe a new evalua-tion
technique called Generalized VA-File-based
Search (GeVAS). It builds on the VA-File,
supports queries over several feature types, and
borrows the idea to search an index structure with
several query objects in parallel from Ciaccia et
al. Our main contributions are twofold: 1) we
show that GeVAS does not degenerate for queries
with many objects or many feature types. 2) We
develop a number of variants of GeVAS, tailored
to the different distance measures and distance-combining
functions, and we show that they yield
a significant performance improvement.
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