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Clustering Geo-tagged Tweets for Advanced Big Data Analytics.

, , , and . BigData Congress, page 42-51. IEEE Computer Society, (2016)

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Hierarchy exploitation in data warehouses for mining association rules., and . SEBD, page 243-256. (2000)A Swiss Army Knife for manipulating Web search results., , , and . SEBD, page 289-296. Edizioni Seneca, (2009)A flexible framework to cross-analyze heterogeneous multi-source geo-referenced information: the J-CO-QL proposal and its implementation., , and . WI, page 499-508. ACM, (2017)ERX: A Conceptual Model for XML Documents.. SAC (2), page 898-903. ACM, (2000)1-58113-240-9.A Unifying Model of Flexible Queries with Distinct Semantics of Search Term Weights., , and . Flexible Approaches in Data, Information and Knowledge Management, volume 497 of Studies in Computational Intelligence, Springer, (2013)SMaC: spatial map caching technique for mobile devices., and . SAC, page 1829-1830. ACM, (2010)An interoperable open data framework for discovering popular tours based on geo-tagged tweets., , , , and . Int. J. Intell. Inf. Database Syst., 10 (3/4): 246-268 (2017)Hadoop vs. Spark: Impact on Performance of the Hammer Query Engine for Open Data Corpora., , and . Algorithms, 11 (12): 209 (2018)On-line aggregation of POIs from Google and Facebook., , and . SAC, page 1081-1089. ACM, (2019)Knowledge Discovery from Geo-Located Tweets for Supporting Advanced Big Data Analytics: A Real-Life Experience., , and . MEDI, volume 9344 of Lecture Notes in Computer Science, page 285-294. Springer, (2015)