Abstract
We study the continuous similarity search problem for evolving queries which has recently been formulated. Given a data stream and a database composed of n sets of items, the purpose of this problem is to maintain the top-k most similar sets to the query which evolves over time and consists of the latest W items in the data stream. For this problem, the previous exact algorithm adopts a pruning strategy which, at the present time T, decides the candidates of the top-k most similar sets from past similarity values and computes the similarity values only for them. This paper proposes a new exact algorithm which shortens the execution time by computing the similarity values only for sets whose similarity values at T can change from time \(T-1\). We identify such sets very fast with frequency-based inverted lists (FIL). Moreover, we derive the similarity values at T in O(1) time by updating the previous values computed at time \(T-1\). Experimentally, our exact algorithm runs faster than the previous exact algorithm by one order of magnitude.
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Acknowledgments
This work was supported by JSPS KAKENHI Grant Number JP15K00148, 2016.
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Yamazaki, T., Koga, H., Toda, T. (2017). Fast Exact Algorithm to Solve Continuous Similarity Search for Evolving Queries. In: Sung, WK., et al. Information Retrieval Technology. AIRS 2017. Lecture Notes in Computer Science(), vol 10648. Springer, Cham. https://doi.org/10.1007/978-3-319-70145-5_7
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DOI: https://doi.org/10.1007/978-3-319-70145-5_7
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