Abstract
Locality Sensitive Hashing (LSH) is a popular method for high dimensional indexing and search over large datasets. However, little efforts have put forward to utilizing LSH in mobile applications for processing spatio-textual similarity queries, such as find nearby shopping centers that have a top ranked hair salon. In this paper, we present hybrid-LSH, a new LSH method for indexing data objects according to both their spatial location and their keyword similarity. Our hybrid-LSH approach has two salient features: First our hybrid-LSH carefully combines the spatial location based LSH and textual similarity based LSH to ensure the correctness of the spatial and textual similarity based NN queries. Second, we present an adaptive query-processing model to address the fixed range problem of traditional LSH and to handle queries with varying ranges effectively. Extensive experiments conducted on both synthetic and real datasets validate the efficiency of our hybrid LSH method.
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Zhu, M., Shen, D., Liu, L., Yu, G. (2015). Hybrid-LSH for Spatio-Textual Similarity Queries. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_14
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DOI: https://doi.org/10.1007/978-3-319-25255-1_14
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