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ACM Transactions on Database Systems (TODS)
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ACM SIGIR Forum
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
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ACM Computing Surveys (CSUR)
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ACM Transactions on Information Systems (TOIS)
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ACM Computing Surveys (CSUR)
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Computer Communications
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ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part IV
Similarity caching in large-scale image retrieval
Information Processing and Management: an International Journal
Cache-Based Query Processing for Search Engines
ACM Transactions on the Web (TWEB)
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Similarity search in metric spaces is a general paradigm that can be used in several application fields. It can also be effectively exploited in content-based image retrieval systems, which are shifting their target towards the Web-scale dimension. In this context, an important issue becomes the design of scalable solutions, which combine parallel and distributed architectures with caching at several levels. To this end, we investigate the design of a similarity cache that works in metric spaces. It is able to answer with exact and approximate results: even when an exact match is not present in cache, our cache may return an approximate result set with quality guarantees. By conducting tests on a collection of one million high-quality digital photos, we show that the proposed caching techniques can have a significant impact on performance, like caching on text queries has been proved effective for traditional Web search engines.