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An obvious approach would be to partition the dataset with a uniform grid structure of a given cell size and identify the cells where this term is in the top-k most frequent keywords. However, this answer would be incomplete since it only checks for neighborhoods that are perfectly aligned with the grid. Furthermore, for every neighborhood (square) that is an answer, we can define infinitely more result neighborhoods by minimally shifting the square without including more posts in it. To address that, we need to identify contiguous regions where any point in the region can be the center of a neighborhood that satisfies the query. We propose an algorithm to efficiently answer an RSK query using an index structure consisting of a uniform grid augmented by materialized lists of term frequencies. We apply various optimizations that drastically improve query latency against baseline approaches. We also provide a theoretical model to choose the optimal cell size for the index to minimize query latency. We further examine a restricted version of the problem (RSKR) that limits the scope of the answer and propose efficient\n                    <jats:italic>approximate<\/jats:italic>\n                    algorithms. Finally, we examine how parallelism can improve performance by balancing the workload using a smart\n                    <jats:italic>load slicing<\/jats:italic>\n                    technique. Extensive experimental performance evaluation of the proposed methods using real Twitter datasets and crime report datasets, shows the efficiency of our optimizations and the accuracy of the proposed theoretical model.\n                  <\/jats:p>","DOI":"10.1007\/s00778-022-00759-9","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T06:02:44Z","timestamp":1658728964000},"page":"501-524","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Reverse spatial top-k keyword queries"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4430-4749","authenticated-orcid":false,"given":"Pritom","family":"Ahmed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6584-1455","authenticated-orcid":false,"given":"Ahmed","family":"Eldawy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8679-4988","authenticated-orcid":false,"given":"Vagelis","family":"Hristidis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5462-9451","authenticated-orcid":false,"given":"Vassilis J.","family":"Tsotras","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"759_CR1","doi-asserted-by":"publisher","unstructured":"Achtert, E., B\u00f6hm, C., Kr\u00f6ger, P., Kunath, P., Pryakhin, A., Renz, M.: Efficient reverse k-nearest neighbor search in arbitrary metric spaces. 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