Abstract
Similarity search in metric spaces represents an important paradigm for content-based retrieval of many applications. Existing centralized search structures can speed-up retrieval, but they do not scale up to large volume of data because the response time is linearly increasing with the size of the searched file. The proposed GHT* index is a scalable and distributed structure. By exploiting parallelism in a dynamic network of computers, the GHT* achieves practically constant search time for similarity range queries in data-sets of arbitrary size. The structure also scales well with respect to the growing volume of retrieved data. Moreover, a small amount of replicated routing information on each server increases logarithmically. At the same time, the potential for interquery parallelism is increasing with the growing data-sets because the relative number of servers utilized by individual queries is decreasing. All these properties are verified by experiments on a prototype system using real-life data-sets.
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Batko, M., Gennaro, C., Zezula, P. (2005). Similarity Grid for Searching in Metric Spaces. In: Türker, C., Agosti, M., Schek, HJ. (eds) Peer-to-Peer, Grid, and Service-Orientation in Digital Library Architectures. Lecture Notes in Computer Science, vol 3664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11549819_3
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DOI: https://doi.org/10.1007/11549819_3
Publisher Name: Springer, Berlin, Heidelberg
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