{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T20:55:51Z","timestamp":1781211351219,"version":"3.54.1"},"reference-count":24,"publisher":"Association for Computing Machinery (ACM)","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2012,5]]},"abstract":"<jats:p>\n            Location-based services (LBS) have become more and more ubiquitous recently. Existing methods focus on finding relevant points-of-interest (POIs) based on users' locations and query keywords. Nowadays, modern LBS applications generate a new kind of spatio-textual data,\n            <jats:italic>regions-of-interest<\/jats:italic>\n            (ROIs), containing region-based spatial information and textual description, e.g., mobile user profiles with active regions and interest tags. To satisfy search requirements on ROIs, we study a new research problem, called spatio-textual similarity search: Given a set of ROIs and a query ROI, we find the similar ROIs by considering spatial overlap and textual similarity. Spatio-textual similarity search has many important applications, e.g., social marketing in location-aware social networks. It calls for an efficient search method to support large scales of spatio-textual data in LBS systems. To this end, we introduce a filter-and-verification framework to compute the answers. In the filter step, we generate signatures for the ROIs and the query, and utilize the signatures to generate candidates whose signatures are similar to that of the query. In the verification step, we verify the candidates and identify the final answers. To achieve high performance, we generate effective high-quality signatures, and devise efficient filtering algorithms as well as pruning techniques. Experimental results on real and synthetic datasets show that our method achieves high performance.\n          <\/jats:p>","DOI":"10.14778\/2311906.2311910","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"824-835","source":"Crossref","is-referenced-by-count":52,"title":["Seal"],"prefix":"10.14778","volume":"5","author":[{"given":"Ju","family":"Fan","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoliang","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lizhu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Chen","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Hu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2012,5]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"918","volume-title":"VLDB","author":"Arasu A.","year":"2006","unstructured":"A. Arasu , V. Ganti , and R. Kaushik . Efficient exact set-similarity joins . In VLDB , pages 918 -- 929 , 2006 . A. Arasu, V. Ganti, and R. Kaushik. Efficient exact set-similarity joins. In VLDB, pages 918--929, 2006."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242591"},{"issue":"1","key":"e_1_2_1_3_1","first-page":"373","article-title":"Retrieving top-k prestige-based relevant spatial web objects","volume":"3","author":"Cao X.","year":"2010","unstructured":"X. Cao , G. Cong , and C. S. Jensen . Retrieving top-k prestige-based relevant spatial web objects . PVLDB , 3 ( 1 ): 373 -- 384 , 2010 . X. Cao, G. Cong, and C. S. Jensen. Retrieving top-k prestige-based relevant spatial web objects. 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In LDB, pages 491--500, 2001."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/SSDBM.2007.22"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.14778\/2078331.2078340"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2012.93"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1989323.1989361"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.5555\/645503.656259"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1989323.1989362"},{"key":"e_1_2_1_17_1","volume-title":"Foundations of Multidimensional and Metric Data Structure","author":"Samet H.","year":"2006","unstructured":"H. Samet . Foundations of Multidimensional and Metric Data Structure . 2006 . H. Samet. Foundations of Multidimensional and Metric Data Structure. 2006."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1007568.1007652"},{"issue":"1","key":"e_1_2_1_19_1","first-page":"1219","article-title":"Trie-join: Efficient trie-based string similarity joins with edit-distance constraints","volume":"3","author":"Wang J.","year":"2010","unstructured":"J. Wang , G. Li , and J. Feng . Trie-join: Efficient trie-based string similarity joins with edit-distance constraints . PVLDB , 3 ( 1 ): 1219 -- 1230 , 2010 . J. Wang, G. Li, and J. Feng. Trie-join: Efficient trie-based string similarity joins with edit-distance constraints. 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